text:
\section{Introduction}
Recent recommendation systems have achieved great success in optimizing immediate engagement such as click-through rates \cite{hidasi2015session,sun2019bert4rec}. However, in real-life applications, long-term user engagement is more desirable than immediate engagement because it directly affects some important operational metrics, e.g., daily active users (DAUs) and dwell time \cite{xue2022resact}. Despite the great importance, how to effectively optimize long-term user engagement remains a significant challenge for existing recommendation algorithms. The difficulties are mainly on i) the evolving of long-term user engagement lasts for a long period; ii) factors that affect long-term user engagement are usually non-quantitative, e.g., users' satisfactory; and iii) the learning signals which are used to update our recommendation strategies are sparse, delayed, and stochastic. When trying to optimize long-term user engagement, it is very hard to relate the changes in long-term user engagement to a single recommendation \cite{wang2022surrogate}. Moreover, the sparsity in observing the evolution of long-term user engagement makes the problem even more difficult.

Reinforcement learning (RL) has demonstrated its effectiveness in a wide range of long-term goal optimization tasks, such as board games~\cite{alphago,alphazero}, video games~\cite{alphastar,atari}, robotics~\cite{levine2016end} and algorithmic discovery~\cite{AlphaTensor2022}.  Conventionally, when trying to solve a real-world problem with reinforcement learning, we need to first formulate it as a Markov Decision Process (MDP) and then learn an optimal policy that maximizes cumulative rewards or some other user-defined reinforcement signals in the defined MDP~\cite{sutton2018reinforcement,chen2021user}. Considering the characteristic that RL seeks to optimize cumulative rewards, it is rather suitable for optimizing long-term signals, such as user stickiness, in recommendation~\cite{zou2019reinforcement,wang2022surrogate}. As in Figure~\ref{rl_rec}, recommender systems can be modeled as an agent to interact with users which serve as the environment. Each time the agent completes a recommendation request, we can record the feedback and status changes of users to calculate a reward as well as the new state for the agent. Applying RL will lead to a recommendation policy which optimizes user engagement from a long-term perspective.

Despite that RL is an emerging and promising framework to optimize long-term engagement in recommendation, it heavily relies on a delicately designed reward function to incentivize recommender systems behave properly. However, designing an appropriate reward function is very difficult and sometimes can be as challenging as solving the Markov Decision Process itself, especially in large-scale complex tasks like recommendation~\cite{wang2022surrogate,christakopoulou2022reward}. On one hand, the reward function should be aligned with our ultimate goal as much as possible. On the other hand, rewards should be sufficiently instructive to provide step-by-step guidance to the agent. For immediate engagement, we can simply use metrics such as click-through rates to generate rewards~\cite{zheng2018drn,zhao2020mahrl}. Turning to long-term engagement, the problem becomes rather difficult because attributing contributions to long-term engagement to each step is really tough. If we only assign rewards when there is a significant change in long-term engagement, learning signals could be too sparse for the agent to learn a reasonable policy. Existing RL recommender systems typically define the reward function empirically~\cite{zou2019reinforcement,xue2022resact,christakopoulou2022reward} or use short-term signals as surrogates~\cite{wang2022surrogate}, which will severely violate the aforementioned requirements of consistency and instructivity.

To mitigate the problem, we propose a new training paradigm, \textbf{Pre}ference-based \textbf{Rec}ommender systems (PrefRec), which allows RL recommender systems to learn from preferences between users' historical behaviors rather than explicitly defined rewards. Instead of providing delicately designed rewards, there is a (virtual) teacher giving feedback about his/her preferences between pairs of users' behaviors. Such preferences are easy to obtain since no expert knowledge is required. With technologies such as crowdsourcing, we can easily gather a large amount of labeled human feedback.
We use the feedback (stored in a preference buffer) to automatically train a reward model in an end-to-end manner. Then the learned reward model will generate rewards to train the recommendation policy. To overcome the problem that the reward model may not work well in some unseen actions, we introduce a separate value function, trained by expectile regression, to assist the training of the critic in PrefRec\footnote{PrefRec adopts the framework of actor-critic in reinforcement learning.}. Our main contributions are threefold:
\begin{itemize}
    \item We propose a novel paradigm to optimize long-term user engagement in recommendation. This paradigm can fully exploit the advantages of reinforcement learning in optimizing long-term goals, and at the same time avoid the complex reward engineering in reinforcement learning.
    \item We design an effective optimization method for the proposed preference-based recommender systems, which uses an additional value function, expectile regression and reward model pre-training to improve the performance.
    \item We conduct extensive experiments on a wide range of long-term user engagement optimization tasks. Experimental results demonstrate that PrefRec significantly outperforms previous state-of-the-art approaches on all the tasks.
\end{itemize}
In the remainder of this paper, Section~\ref{preliminaries} introduces background knowledge about what is long-term user engagement and how to formulate recommendation as an MDP. Section~\ref{challenges} discusses the main challenges in optimizing long-term user engagement via RL. Section~\ref{methods} describes the framework of PrefRec and how to effectively optimize the recommendation policy for PrefRec. Sections~\ref{exp} explains the experimental setup and reports empirical results of our approach with respect to the current state-of-the-art. Following that, Section~\ref{relatedwork} discusses previous related studies. Lastly, Section~\ref{conclusion} presents our final remarks and gives a conclusion.

\begin{figure}
    \centering
    \includegraphics[width=0.6\linewidth]{figs/RL_Rec.pdf}
    \caption{Reinforcement learning recommender systems.}
    \label{rl_rec}
\end{figure}

\begin{figure*}
    \centering
    \includegraphics[width=0.8\textwidth]{figs/framework.pdf}
    \caption{The framework of preference-based recommender systems (PrefRec). A teacher provides feedback about his/her preferences between users' behavioral trajectories. Trajectory 1 demonstrates a trend from low-active to high-active and trajectory 2 shows an opposite tendency. Therefore, the teacher will prefer trajectory 1 to trajectory 2. With feedback of preferences, we can automatically train a reward function in an end-to-end manner. The preference-based recommender systems is then optimized by using rewards predicted by the learned reward function.}
    \label{fig:framework}
\end{figure*}

\section{Preliminaries}
\label{preliminaries}
In this section, we first introduce long-term user engagement in recommendation. Then, we explain how to formulate recommendation as a Markov Decision Process and the optimization objective.
\subsection{Long-term User Engagement in Recommendation}
\label{session-basis}
Long-term user engagement is an important metric in recommendation, and typically reflects as the stickiness of users to a product. In general, given a product, we expect users to spend more time on it and/or use it as frequently as possible. In this work, we assume that users interact with the recommender systems on a session basis: when a user accesses a product, such as an App, a session begins, and it ends when the user leaves. During each session, users can launch an arbitrary number of recommendation requests as they want. Such session-based recommender systems has been widely deployed in real-life applications such as short-form videos recommendation and news recommendation~\cite{zheng2018drn,zhan2022deconfounding,zhao2020maximizing,ijcai2019-883}. We are particularly interested in increasing
\begin{itemize}
\item[i] the number of recommended items that users consume during each visit;
\item[ii] the frequency that users visit the product.
\end{itemize}
Optimizing these two indicators is nontrivial because it is difficult to relate them to a single recommendation. For example, if a user increases its visiting frequency, we are not able to know exactly which recommendation leads to the increase. To this end, we propose to use reinforcement learning to take into account the potential impact on the future when making decisions.

\subsection{Recommendation as a Markov Decision Process (MDP)}
Applying RL to recommender systems requires defining recommendation as a Markov Decision Process (MDP). Recommender systems can be described as an agent to interact with users, who act as the environment. Formally, we formulate recommendation as a Markov Decision Process (MDP) $\langle\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\gamma \rangle$:
\begin{itemize}
    \item $\mathcal{S}$ is the continuous state space. $s\in\mathcal{S}$ indicates the state of a user which contains static information such as gender and age; and dynamic information, such as the rate of likes and retweets. A state is what the recommender systems relies on to make decisions.

    \item $\mathcal{A}$ is the continuous action space, where $a\in\mathcal{A}$ is an action which has the same dimension as the representation of recommendation items.  We determine the item to recommend by comparing the similarity of an action and item representations~\cite{zhao2020mahrl,xue2022resact}.

    \item $\mathcal{P}: \mathcal{S}\times\mathcal{A}\times\mathcal{S}\to\mathbb{R}$ is the transition function, where $p(s_{t+1}|s_t,a_t)$ defines the probability that the next state is $s_{t+1}$ after recommending an item $a_t$ at the current state $s_t$.

    \item $\mathcal{R}: \mathcal{S}\times\mathcal{A}\to\mathbb{R}$ is the reward function. $r(s_t,a_t)$ determines how much the agent will be rewarded after recommending $a_t$ at state $s_t$.

    \item $\gamma$ is the discount factor. $\gamma$ determines how much the agent cares about rewards in the distant future relative to those in the immediate future.
\end{itemize}
The recommendation policy $\pi(a|s): \mathcal{S}\to\mathcal{A}$ is defined as a mapping from state to action. Given a policy $\pi(a|s)$, we define a state-action value function (Q-function) $Q^{\pi}(s,a)$ which generates the expected cumulative reward (return) of taking an action $a$ at state $s$ and thereafter following $\pi$:
\begin{equation}
    Q^{\pi}(s_t,a_t)=\mathbb{E}_{(s_{t'},a_{t'})\sim \pi} \left[r(s_{t},a_{t})+\sum_{t'=t+1}^\infty \gamma^{(t'-t)}\cdot r(s_{t'},a_{t'}) \right].
\end{equation}
We seek to optimize the policy $\pi(a|s)$ so that the return obtained by the recommendation agents is maximized:
\begin{equation}
    \max_{\pi} \mathcal{J}(\pi)=\mathbb{E}_{s_t\sim d_t^{\pi}(\cdot),a_t\sim \pi(\cdot|s_t)} \left[Q^{\pi}(s_t,a_t) \right],
\end{equation}
where $d_t^{\pi}(\cdot)$ denotes the state visitation frequency at step $t$ under the policy $\pi$. By optimizing the above objective, the agent can achieve the largest cumulative return in the defined MDP.

\begin{figure*}
    \centering
    \includegraphics[width=0.8\textwidth]{figs/expectile.pdf}
    \caption{Left: Illustration of the expectile loss function $H^{\tau}(u)$. $\tau=0.5$ corresponds to the MSE loss, and $\tau>0.5$ upweights positives differences $u$. Center: Expectiles of a Gaussian distribution. Right: An example of expectile regression on a two-dimensional distribution.}
    \label{fig:expectile}
\end{figure*}
\section{Challenges of Designing the Reward Function}
\label{challenges}
Despite that RL is a promising approach to optimize long-term user engagement, the application of RL heavily relies on a well-designed reward function. The reward function must be able to reflect the changes in long-term engagement at each time step.
In the meantime, it should be able to provide instructive guidance to the agent for optimizing the policy.
Practically, quantifying rewards properly is very challenging because it is really difficult to relate changes in long-term engagement to a single recommendation~\cite{wang2022surrogate}. For example, when recommending a video to a user, we have no way of knowing how many videos the user will continue to watch on the platform before exiting the current session, and obviously, how this amount will be affected by the recommendation is even harder to know. For this reason, it is challenging to give a reward regarding the impact of recommended videos on the average video consumption of users. Similarly, recommending a video cannot be used to predict when the user will revisit the platform after exiting the current session. Designing rewards related to the visiting frequency of users is also difficult. As a compromise solution, one could only assign rewards at the beginning or the end of a session. However, this kind of reinforcement signals will be too sparse for the recommender systems to learn a reasonable policy, especially when the session length is large~\cite{xue2022resact}. Existing methods either design the reward function highly empirically~\cite{zou2019reinforcement,xue2022resact,christakopoulou2022reward} or use immediate engagement signals as surrogates~\cite{wang2022surrogate}, which will cause deviation between the optimization objective and the real long-term engagement. For this reason, it is urgent to propose a framework to address the difficulties in reward designing when using RL to optimize long-term user engagement.

\section{Preference-based Recommender Systems}
\label{methods}
To resolve the difficulties in designing the reward function, we propose a novel paradigm, \textbf{Pef}erence-based \textbf{Rec}ommender systems (PrefRec), which allows an RL recommender systems to learn from preferences on users' historical behaviors rather than explicitly defined rewards. By this way, we can overcome the problems in designing the reward function when optimizing long-term user engagement. In this section, we first introduce how to utilize preferences to generate reinforcement signals for learning a recommender systems. Then, we discuss how to optimize the performance of preference-based recommender systems by using expectile regression to better estimate the value function. Next, we propose to pre-train the reward function to stabilize the learning process. Last, we summarize the algorithm.

\subsection{Reinforcing from Preferences}
While the reward for a recommendation request is hard to obtain, preferences between two trajectories of users' behaviors are easy to determine. For example, if one trajectory shows a transition from low-active to high-active and the other shows an opposite trend or an insignificant change, we can easily indicate the preference between them. Labeling preferences does not require any expert knowledge, so we can easily use techniques such as crowdsourcing to obtain a large amount of feedback on preferences.
In preference-based recommender systems, we assume that there is a (virtual) teacher providing preferences between user's behaviors and the recommender systems uses this feedback to perform the task. There are mainly two advantages in using preferences: i) labeling the preference between a pair of trajectories is quite simple compared to designing rewards for every step; and ii) the recommender systems is incentivized to learn the preferred behavior directly because reinforcement signals come from preferences.

We provide the framework of preference-based recommender systems in Figure~\ref{fig:framework}. As we can find, there is a teacher providing preferences between a pair of users' behavioral trajectories\footnote{For a pair of trajectories, they may come from different users or from different periods of the same user.}. The teacher could be humans or or even a program with labeling criteria.
For trajectory 1, the user increases its visiting frequency  gradually and consumes more and more items in each session, which indicates that it is satisfied with the current recommendation policy. On the contrary, trajectory 2 shows the user is becoming less and less active, suggesting that the current recommender system should be improved to better serve the user. The teacher will obviously prefer trajectory 1 to trajectory 2. After generating such preference data, we use them to automatically train a reward function $\hat{r}(s,a;\psi)$, parameterized by $\psi$, in an end-to-end manner. The preference-based recommender systems uses the predicted reward $\hat{r}(s,a;\psi)$ rather than hand-crafted rewards to update its policy.

Formally, a trajectory $\sigma$ is a sequence of observations and actions $\{(s_1,a_1),\dots,(s_T,a_T)\}$. Given a pair of trajectories $(\sigma^0, \sigma^1)$, a teacher provides a feedback indicating which trajectory is preferred, i.e., $y=(0,1)$, $(1,0)$ or $(0.5,0.5)$, where $(0,1)$ indicates trajectory $\sigma^1$ is preferred to trajectory $\sigma^0$, i.e., $\sigma^{1} \succ \sigma^{0}$; $(1,0)$ indicates $\sigma^{0} \succ \sigma^{1}$; and $(0.5,0.5)$ implies an equally preferable case. Each feedback is triple $(\sigma^0,\sigma^1,y)$ which is stored in a preference buffer $\mathcal{D}_{p}=\{((\sigma^0,\sigma^1,y))_i\}_{i=1}^{N}$. We use a deep neural network with parameters $\psi$ to predict the reward $\hat{r}(s,a;\psi)$ at a specific step $(s,a)$. By following the Bradley-Terry model~\cite{bradley1952rank,park2022surf}, we assume that the teacher's probability of preferring a trajectory depends exponentially on the accumulated sum of the reward over the trajectory. Then the probability that trajectory $\sigma^1$ is preferred to trajectory $\sigma^0$ can be written as a function of $\hat{r}(s,a;\psi)$:
\begin{equation}
    P_{\psi}\left[\sigma^{1} \succ \sigma^{0}\right]=\frac{\exp \left(\sum_{t} \hat{r}\left(\mathbf{s}_{t}^{1}, \mathbf{a}_{t}^{1};\psi \right)\right)}{\sum_{i \in\{0,1\}} \exp \left(\sum_{t} \hat{r}\left(\mathbf{s}_{t}^{i}, \mathbf{a}_{t}^{i};\psi \right)\right)}.
\end{equation}
Since the preference buffer $\mathcal{D}_{p}=\{((\sigma^0,\sigma^1,y))_i\}_{i=1}^{N}$ contains true labels of preferences, we can train the reward model $\hat{r}(s,a;\psi)$ through supervised learning, updating it by minimizing the cross-entropy loss:
\begin{equation}
\label{r_loss}
\mathcal{L}_{\psi}=-\underset{\left(\sigma^{0}, \sigma^{1}, y\right) \sim \mathcal{D}_p}{\mathbb{E}}\left[y(0) \log P_{\psi}\left[\sigma^{0} \succ \sigma^{1}\right]+y(1) \log P_{\psi}\left[\sigma^{1} \succ \sigma^{0}\right]\right],
\end{equation}
where $y(0)$ and $y(1)$ are the first and second element of $y$, respectively. After learning the reward model $\hat{r}(s,a;\psi)$, we can use it to generate learning signals for training the recommendation policy.

\subsection{Optimizing the Recommendation Policy}
When learning the recommendation policy, there is a replay buffer $\mathcal{D}_r$ storing training data. Unlike conventional RL recommender systems, we store $(s,a,s')$ rather than $(s,a,r,s')$ in the buffer $\mathcal{D}_r$ because we cannot obtain explicit rewards from the environment\footnote{With a slight abuse of notation, we use $s'$ to denote the next state.}. We utilize the learned reward model $\hat{r}(s,a;\psi)$ to label the reward for a tuple $(s,a)$. After labelling, an intuitive approach is to learn the Q-function by minimizing the following temporal difference (TD) error:
\begin{equation}
\label{naive_td}
\mathcal{L}^{Q}_{\theta}=\underset{\left(s,a,s'\right) \sim \mathcal{D}_r}{\mathbb{E}}\left[(\hat{r}(s,a;\psi)+\gamma Q(s',\pi(\cdot|s');\hat{\theta})-Q(s,a;\theta))^2 \right],
\end{equation}
where $\mathcal{D}_r$ is the replay buffer, $Q(s,a;\theta)$ is a parameterized Q-function, $Q(s,a;\hat{\theta})$ is a target network (e.g.,
with soft updating of parameters defined via Polyak averaging), and $\pi(\cdot|s)$ is the recommendation policy. However, in practice, we find that this method does not perform well. It may be because the recommendation policy $\pi(\cdot|s)$ will choose significantly different actions from the stored data, making the reward function and the Q-function unable to predict the corresponding values. To resolve this, we introduce a separate value function (V-function) that predicts how good or bad a state is. By doing so, we can eliminate the uncertainty that comes with the recommended policy. Instead of minimizing the TD error in Eq.~\ref{naive_td}, we turn to minimize the following loss to learn the Q-function:
\begin{equation}
\label{new_td}
\mathcal{L}^{Q}_{\theta}=\underset{\left(s,a,s'\right) \sim \mathcal{D}_r}{\mathbb{E}}\left[(\hat{r}(s,a;\psi)+\gamma V(s';\eta)-Q(s,a;\theta))^2 \right],
\end{equation}
where $V(s';\eta)$ is the V-function with parameters $\eta$. Given the replay buffer $\mathcal{D}_r$, the relationship between Q-function and V-function is
\begin{equation}
\label{v-q}
    V(s)=\mathbb{E}_a[Q(s,a)], (s,a) \in \mathcal{D}_r.
\end{equation}
Conventionally, we can optimize the parameters of the V-function by minimizing the following Mean Squared Error (MSE) loss:
\begin{equation}
    \label{vloss_0}
    \mathcal{L}^{V}_{\eta}=\underset{\left(s,a\right) \sim \mathcal{D}_r}{\mathbb{E}}\left[(Q(s,a;\hat{\theta})-V(s;\eta))^2 \right].
\end{equation}
However, such V-function corresponds to the behavior policy which collects the replay buffer $\mathcal{D}_r$. We want to achieve improvement upon the behavior policy. Inspired by expectile regression~\cite{koenker2001quantile,kostrikov2022offline}, we let the V-function to regress the $\tau$ expectile ($\tau \geq 0.5$) of $Q(s,a)$ rather than the mean statistics as in Eq.~\ref{v-q}. Then the loss for V-function becomes:
\begin{equation}
    \label{vloss}
    \mathcal{L}^{V}_{\eta}=\underset{\left(s,a\right) \sim \mathcal{D}_r}{\mathbb{E}}\left[H^{\tau}(Q(s,a;\hat{\theta})-V(s;\eta)) \right],
\end{equation}
where $H^{\tau}(u)=|\tau-\mathbb{I}(u<0)|u^2$, $\mathbb{I}(\cdot)$ is the indicator function. Specially, if $\tau=0.5$, Eq.~\ref{vloss_0} and Eq.~\ref{vloss} are identical. For $\tau > 0.5$, this asymmetric loss (Eq.~\ref{vloss}) downweights the contributions of $Q(s,a;\hat{\theta})$ smaller than $V(s;\eta)$ while giving more weights to larger values (as in Figure~\ref{fig:expectile}, left). Figure~\ref{fig:expectile} (right) illustrates expectile regression on a two-dimensional distribution: increasing $\tau$ leads to more data points below the regression curve. Back to the learning of the V-function, the purpose is to let $V(s;\eta)$ to regress an above-average value. The Q-function is jointly trained with the V-function, while it also serves as the critic to guide the update of the recommendation policy, i.e., the actor. The recommendation policy is optimized by minimizing the loss:
\begin{equation}
    \label{piloss}
    \mathcal{L}^{P}_{\mu}=- \underset{\left(s,a\right) \sim \mathcal{D}_r}{\mathbb{E}}\left[Q(s, \pi(\cdot|s;\mu);\theta)\right],
\end{equation}
where $\pi(\cdot,s;\mu)$ is the recommendation policy with parameters $\mu$. We follow the standard paradigm of actor-critic in reinforcement learning here to optimize the recommendation policy.

\begin{algorithm}
\caption{Preference-based Recommender Systems}
\label{algo}
\KwIn{Preference buffer $\mathcal{D}_{p}=\{(\sigma^0,\sigma^1,y)_i\}_{i=1}^{N}$,
replay buffer $\mathcal{D}_{r}=\{(s,a,s')_i\}_{i=1}^{M}$, pre-train episodes $K$}
 Initialize the reward model $r(s,a;\psi)$, the Q-function $Q(s,a;\theta)$, the V-function $V(s;\eta)$, the recommendation policy $\pi(\cdot|s;\mu)$\label{2} \\
 Set soft-update rate $\lambda$ and initialize the target Q-function $Q(s,a;\hat{\theta})$\label{target}\label{3}\\
 \For{pre-train episodes $k=1$ to $K$}{
 Sample a mini-batch of preferences $(\sigma^0,\sigma^1,y)$ from $\mathcal{D}_p$\\
 Update the reward model $r(s,a;\psi)$: $\psi \leftarrow \psi-\nabla_{\psi}\mathcal{L}_{\psi}$ (Eq.~\ref{r_loss})
 }
\While{not end training}{
Sample a mini-batch of transitions $(s,a,s')$ for $\mathcal{D}_r$\\
Label the sampled transitions by the reward model to obtain $(s,a,\hat{r},s')$\\
Update the V-function $V(s;\eta)$: $\eta \leftarrow \eta-\nabla_{\eta}\mathcal{L}^V_{\eta}$ (Eq.~\ref{vloss})\\
Update the Q-function $Q(s,a;\theta)$: $\theta \leftarrow \theta-\nabla_{\theta}\mathcal{L}^Q_{\theta}$ (Eq.~\ref{new_td}) \\
Update the recommendation policy $\pi(\cdot|s;\mu)$: $\mu \leftarrow \mu-\nabla_{\mu}\mathcal{L}^P_{\mu}$ (Eq.~\ref{piloss})\\
Soft-update the target Q-function $Q(s,a;\hat{\theta})$: $\hat{\theta}\leftarrow \lambda\theta+(1-\lambda)\hat{\theta}$\\
\If{fine-tune reward model}{
Sample a mini-batch of preferences $(\sigma^0,\sigma^1,y)$ from $\mathcal{D}_p$\\
 Update the reward model $r(s,a;\psi)$: $\psi \leftarrow \psi-\nabla_{\psi}\mathcal{L}_{\psi}$ (Eq.~\ref{r_loss})}
}
\end{algorithm}
\subsection{Pre-training the Reward Function}
The reward function is used to automatically generate reinforcement signals to train the recommendation policy. However, A reward model that is not well-trained will cause the collapse of the recommendation policy. Thus, to stabilize the training, we propose to pre-train the reward function before starting the updating of the recommendation policy. Specifically, we first prepare a preference buffer that stores a pool of preference feedback between interaction histories. Then we initialize a deep neural network with the structure of multi-layer perceptron to learn from the preference buffer. The neural network is updated by minimizing the loss in Eq.~\ref{r_loss}. After training for several episodes, we start the training of the recommendation policy. At this phase, the reward model is updated simultaneously with the recommender systems. By doing so, the reward model can handle potential shift in human preferences and can therefore generate more accurate learning signals.

\subsection{Overall Algorithm}
We provide the overall algorithm of PrefRec in Algorithm~\ref{algo}. A preference buffer $\mathcal{D}_p$ and a replay buffer $\mathcal{D}_r$ is required for training PrefRec. From lines 1 to 2, we do the initialization for the deep neural networks and set hyper-parameters such as soft-update rate. From lines 3 to 5, we pre-train the reward model to confirm that it is able to provide reasonable learning signals when updating the recommendation policy. Lines 7 to 18 describe the training process of the recommendation policy. We first sample a mini-batch of transitions from the replay buffer $\mathcal{D}_r$. Then we lable the samples data with the reward model. Following that, we update the parameters of the V-function, the Q-function and the recommendation policy accordingly. Finally, if fine-tuning the reward model, we will train the reward model to keep it up-to-date.

\section{Experimental Results}
\label{exp}
We conduct extensive experiments on various of long-term user engagement optimization tasks. In particular, we want to answer the following three research questions (RQs):
\begin{itemize}
    \item \textbf{RQ1}: Can the framework of PrefRec lead to improvement in long-term user engagement?
    \item \textbf{RQ2}: Whether PrefRec is able to outperform existing state-of-the-art methods?
    \item \textbf{RQ3}: How do the components in PrefRec contribute to the performance?
\end{itemize}

We begin by introducing the preparation of the dataset. Then we introduce the baselines we compared with and explain how to perform the evaluation. Following that we report the overall results on a variety of tasks. Finally, we perform ablations to understand the working process of PrefRec.

\subsection{Preparing the dataset}
Since PrefRec is a new framework in recommendation, there is no available dataset for evaluation. To prepare the dataset, we track complete interaction histories of around $100,000$ users from a leading short-form videos platform for months, during which over 25 millions recommendation services are provided.
For each user, we record its interaction history in a session-request form: the interaction history consists of several sessions with each session containing a number of recommendation requests (see Sec.~\ref{session-basis}). The recommender system provides service at each recommendation request where we record the state of a user and the action of the recommender system. Each state is a 245-dimensional vector containing the user's state information, such as gender, age and historical like rate. We applied scaling, normalization and clipping to each dimension of states in order to stabilize the input of models. An action is a 8-dimensional vector which is the representation of recommended item at a request. We record the timestamp of each time a user starts and exits a session. With the timestamp, we can calculate the duration that a user revisits the platform and thus can infer the visiting frequency.
We measure changes in long-term user engagement at the end of each session for each user. Specifically, we first calculate the average session depth $\delta^u_{avg}$ and the average revisiting time $\epsilon^u_{avg}$ for each user $u$ during the time span. For each session, $\delta^u_i$ denotes the number of requests in the $i$-th session of the user $u$. If $\delta^u_i$ is larger than the average session depth $\delta^u_{avg}$, we consider that there is an improvement in session depth. Similarly, we can calculate the revisiting time $\epsilon^u_{i}$ for session $i$ and if it is less than the average revisiting time $\epsilon^u_{avg}$, we consider that there is an improvement in visiting frequency. We quantify the changes in session depth and visiting frequency into six levels (level 0 to level 5; the more higher level, the more positive change) where the levels are determined by the following equations, respectively:
\begin{equation}
\begin{aligned}
\label{level}
    \mathbf{L}^u_{i}(depth)=\left(\lfloor \frac{\delta^u_i}{\delta^u_{avg}} \rfloor\right).clip(0,5) \\
     \mathbf{L}^u_{i}(frequency)=\left(\lfloor \frac{\epsilon^u_{avg}}{\epsilon^u_{i}} \rfloor\right).clip(0,5)
\end{aligned}
\end{equation}
We provide the proportion of the calculated levels of all sessions in Figure~\ref{fig:l_stat}. For session depth, most of sessions stay in level $0$ and only very few of them are located in level $4$ and $5$. The visiting frequency demonstrates a more even distribution.

After processing the data, we can prepare the two buffers, i.e., the preference buffer $\mathcal{D}_p$ and the replay buffer $\mathcal{D}_r$, which are used in PrefRec. For the preference buffer $\mathcal{D}_p$, we uniformly sample $20,000$ pairs of users who have launched for more than $200$ recommendation requests in the platform. We set the length of trajectory $\sigma$ as $100$ and randomly sample a segment of trajectories with this length from the interaction histories of the selected users. To generate the preferences, we write a scripted teacher who provides its feedback by comparing cumulative levels of changes on the trajectory segments. For the replay buffer $\mathcal{D}_r$, we randomly sample $80\%$ of users as the training set and split their interaction histories into transitions $(s,a,s')$to fill up the replay buffer $\mathcal{D}_r$.

\begin{figure}
    \centering
    \includegraphics[width=0.45\textwidth]{figs/l_stat.pdf}
    \caption{The proportion of the levels in session depth and visiting frequency.}
    \label{fig:l_stat}
\end{figure}

\begin{figure*}
    \centering
    \includegraphics[width=0.8\textwidth]{figs/PrefRec_learning_curve.pdf}
    \caption{Learning curves of RL recommender systems under the framework of PrefRec, averaged over 5 runs. }
    \label{fig:learning_curves}
\end{figure*}
\subsection{Baselines}
We compare PrefRec with a variety of baselines, including reinforcement learning methods for off-policy continues control (DDPG, TD3, SAC), offline reinforcement learning algorithms (TD3\_BC, BCQ, IQL), and imitation learning:
\begin{itemize}
    \item \textbf{DDPG}~\citep{lillicrap2015continuous}: An off-policy reinforcement learning algorithm which concurrently learns a Q-function and a deterministic policy. The update of the policy is guided by the Q-function.
    \item \textbf{TD3}~\citep{fujimoto2018addressing}: A reinforcement learning algorithm which is designed upon DDPG. It applies techniques such as clipped double-Q learning, delayed policy updates, and target policy smoothing.
    \item \textbf{SAC}~\citep{haarnoja2018soft}:  An off-policy reinforcement learning algorithm trained to maximize a trade-off between expected return and entropy, a measure of randomness in the policy. It also incorporates tricks such as the clipped double-Q learning.
    \item \textbf{TD3\_BC}~\citep{fujimoto2021minimalist}: An offline reinforcement learning algorithm designed based on TD3. It applies a behavior cloning (BC) term to regularize the updating of the policy.
    \item \textbf{BCQ}~\citep{fujimoto2019off}: An offline algorithm which restricts the action space in order to force the agent towards behaving similarly to the behavior policy.
    \item \textbf{IQL}~\citep{kostrikov2022offline}: An offline reinforcement learning method which uses expectile  regression to estimate the value of the best action in a state.
    \item \textbf{IL}: Imitation learning treats the behaviors in the replay buffer as expert knowledge and learns a mapping from observations to actions by using expert knowledge as supervisory signals.
\end{itemize}

\subsection{Evaluation Metric}
Among the $10,0000$ users, we sample $80\%$ of them as the training set and the remaining $20\%$ users constitute the test set. For the test users, we store their complete interaction histories separately, in the session-request format. We adopt Normalised Capped Importance Sampling (NCIS)~\cite{swaminathan2015self}, a widely used standard offline evaluation method~\cite{gilotte2018offline,farajtabar2018more}, to evaluate the performance. Formally, the score of a policy $\pi$ is calculated by
\begin{equation}
\begin{aligned}
\tilde{J}^{NCIS}(\pi)=\frac{1}{|\mathcal{U}|}\sum_{u \in \mathcal{U}}\left[ \frac{\sum_{i=0}^{|\mathcal{T}_u|}\tilde{\rho}_i(\pi,\mathcal{T}_u)\mathbf{L}^u_i}{\sum_{i=0}^{|\mathcal{T}_u|}\tilde{\rho}_i(\pi,\mathcal{T}_u)}\right],
\end{aligned}
\end{equation}
where $\mathcal{U}$ is the set of test users, $\mathcal{T}_u$ is the set of sessions of the user $u$, $\tilde{\rho}_i(\pi,\mathcal{T}_u)$ is the probability that the policy $\pi$ follows the request trajectory of the $i$-th session in $\mathcal{T}_u$, and $\mathbf{L}^u_i$ is the level of change for the $i$-the session (as defined in Eq.~\ref{level}). Intuitively, $\tilde{J}^{NCIS}(\pi)$ awards a policy with a high score if the policy has large probability to follow a good trajectory.

\begin{table}
\centering
\caption{Overall performance comparisons on various long-term user engagement optimization tasks. The ``$\pm$\small'' indicates $95\%$
    confidence intervals.}
\scalebox{0.95}{
\begin{tabular}{lccc}
\toprule[1pt]
 & Depth  & Frequency  & Mixture   \\
  \midrule[0.5pt]
 DDPG~\citep{lillicrap2015continuous} & 0.3211 $\pm$\small0.0060 & 1.1408 $\pm$\small0.0163 & 1.0642 $\pm$\small0.0119\\
 TD3~\citep{fujimoto2018addressing} & 0.3495 $\pm$\small 0.0038 & 0.9714 $\pm$\small 0.0141 & 0.6423 $\pm$\small 0.0097\\
 SAC~\citep{haarnoja2018soft} & 0.3190 $\pm$\small 0.0031 & 0.6416 $\pm$\small 0.0058 & 0.5348 $\pm$\small 0.0047\\ \midrule[0.5pt]
 TD3\_BC~\citep{fujimoto2021minimalist} & 0.3246 $\pm$\small 0.0032 & 0.6781 $\pm$\small 0.0060 & 0.5326 $\pm$\small 0.0047\\
 BCQ~\citep{fujimoto2019off} & 0.3142 $\pm$\small 0.0033 & 0.6580 $\pm$\small 0.0060 & 0.5528 $\pm$\small 0.0052\\
 IQL~\citep{kostrikov2022offline} & 0.3311 $\pm$\small 0.0054 & 1.0354 $\pm$\small 0.0141 & 0.8230 $\pm$\small 0.0099\\ \midrule[0.5pt]
 IL & 0.3202 $\pm$\small 0.0031 & 0.6406 $\pm$\small 0.0058 & 0.5348 $\pm$\small 0.0047\\  \midrule[0.5pt]
 PrefRec (ours) &\textbf{0.4229 $\pm$\small0.0077} &\textbf{1.7706 $\pm$\small0.0206} &\textbf{1.3788 $\pm$\small0.0133}\\
 $\%$ Improv.& 21.00$\%$& 55.20$\%$ & 29.56$\%$\\ \bottomrule[1pt]
\end{tabular}
}
\label{tbl:overall_performance}
\end{table}

\subsection{Overall Results}
We conduct experiments to verify if the framework of PrefRec can improve long-term user engagement in terms of i) session depth; ii) visiting frequency; and iii) a mixture of the both. We randomly sample $500$ users from the test set and use them to plot the learning curves of our method and the baselines. As in Fig.~\ref{fig:learning_curves}, PrefRec achieves a significant and consistent increase in cumulative long-term engagement changes in all the tasks, although it does not receive any explicit reinforcement signal. Similar phenomenon can also be observed in some of those generic reinforcement learning algorithms, such as DDPG. They demonstrates growth in specific tasks, though not that stable. The learning curves indicate that the learned reward function is able to provide reasonable reinforcement signals (we provide the training plots of the reward function in Appendix~\ref{r_plot}.) and the framework of PrefRec provides an effective training paradigm to achieve reward-free recommendation policy learning. Next, we save the models with the best performance in training and test their performance on the whole test set. As in Table~\ref{tbl:overall_performance}, those generic reinforcement learning algorithms poorly without explicit rewards. PrefRec is able to outperform all the baselines by a wide margin in all the three tasks, showing the effectiveness of the proposed optimization methods.

\begin{figure}
    \centering
    \includegraphics[width=0.48\textwidth]{figs/tau_ab.pdf}
    \caption{Ablations on the expectile regression factor $\tau$.}
    \label{fig:tau_ab}
\end{figure}

\begin{figure}
    \centering
    \includegraphics[width=0.48\textwidth]{figs/pretrain_ab.pdf}
    \caption{Ablations on reward function pre-training and reward function fine-tuning.}
    \label{fig:pretrian_ab}
\end{figure}

\subsection{Ablations}
To understand how the components in PrefRec affect the performance, we perform ablation studies on the expectile regression factor and the pre-training/fine-tuning of the reward function. As can be found in Table~\ref{fig:tau_ab}, when $\tau=0.5$ where the expectile regression becomes identical to the regression to mean, there is an obvious drop in performance. The phenomenon indicates that the expectile regression with a proper $\tau$ contributes to the improvement in performance. What's more, if compared with the results of DDPG in Table~\ref{tbl:overall_performance}, we can find that introducing a separate V-function also benefits the performance since DDPG can be considered an algorithm whose $\tau=0.5$ and without a V-function. Next, we study whether the pre-training and fine-tuning of the reward function is useful. As in Table~\ref{fig:pretrian_ab}, if we directly update the recommendation policy without pre-training the reward function, the performance decreases. Similarly, training the recommendation policy with a frozen reward function also degrades the performance. The results suggests that the pre-training and fine-tuning of the reward function are important to the performance.

\section{Related Work}
\label{relatedwork}
In this section, we review related studies regarding the two main
topics of this work: long-term user engagement in recommendation and reinforcement learning recommender systems.
\subsection{Optimizing Long-term User Engagement in Recommendation}
Long-term user engagement has recently attracted increasing attention due to its close connection to user stickiness. Main challenges in this area include sparse and non-stationary feedback signals. Zhang et al.~\citep{zhang2021counterfactual} augments the sparse rewards by re-weighting the original feedbacks with counterfactual importance sampling. Wu et al.~\citep{wu2017returning} consider user click as immediate reward and user's return time as a hint on long-term engagement. It also assumes a stationary distribution of candidates for recommendation. In contrast, Zhao et al.~\citep{zhao2021rabbit} empirically identify the problem of non-stationary users' taste distribution and propose distribution-aware recommendation methods. Other researches focus on maximizing the diversity of recommendations as an indirect approach to optimize long-term engagement. For example, Adomavicius et al.~\citep{adomavicius2011maximizing} propose a graph-based approach
to maximize diversity based on maximum flow. Ashton et al.~\citep{ashton2020algorithmic} propose that high recommendation diversity is related to long-term user engagement. Apart from diversity, Wang et al.~\citep{wang2022surrogate} conduct large-scale empirical studies and propose several surrogate criteria for optimizing long-term user engagements, including high-quality consumption, repeated consumption, etc. In this paper we avoid directly learning from the sparse and non-stationary signals inferred from the environment. Instead, we propose to learn a parameterized reward function to optimize the long-term user engagement.
\subsection{Reinforcement Learning for Recommender Systems}
Reinforcement learning allows an autonomous agent to interact with environment and optimizes long-term goals from experiences, which is particularly suitable for tasks in recommender systems~\citep{zou2019reinforcement,mazoure2021improving,shani2005An,bai2019model,chen2018stabilizing,zhang2022multi}. Shani et al.~\citep{shani2005An} first proposed to employ Markov Decision Process~(MDP) to model the recommendation behavior. The subsequent researches focus on standard RL problems, e.g., exploration and exploitation trade-off in the context of recommendation systems~\citep{chen2021values,tang2017exploration}, together with model-based or simulator-based approaches to improve sample efficiency~\citep{shi2019virtual,bai2019model}. Recently, there has been works on designing proper reward functions for efficient training of recommender systems. For example, Zou et al.~\citep{zou2019reinforcement} use a Q-network with hierachical LSTM to model both the instant and long-term reward. Ji et al.~\citep{ji2021reinforcement} model the recommendation process as a Partially Observable MDP and estimate the lifetime values of the recommended items. Zheng et al.~\citep{zheng2018drn} explicitly model the future returns by introducing the user activeness score. Ie et al.~\citep{ie2019slateq} decompose the Q-function for tractable and more efficient optimization. There are also researches focusing on behavior diversity~\citep{wang2022surrogate,zhou2010solving} as a surrogate for the reward function. Instead of relying on the aforementioned handcrafted reward signals, in this paper we propose to automatically train a reward function based on preferences between users' behavioral trajectories, which avoids the difficulties in reward engineering.
\section{Conclusions}
\label{conclusion}
In this paper, we propose PrefRec, a novel paradigm of recommender systems, to improve long-term user engagement. PrefRec allows RL recommender systems to learn from preferences between users' historical behaviors rather than explicitly defined rewards. By this way, we can fully exploit the advantages of RL in optimizing
long-term goals, while avoiding complex reward engineering. PrefRec uses the preferences to automatically learn a reward function. Then the reward function is applied to generate reinforcement signals for training the recommendation policy. We design an effective optimization method for PrefRec, which utilizes an additional value function, expectile regress and reward function pre-training to enhance the performance. Experiments demonstrate that PrefRec significantly and consistently outperforms the current state-of-the-art on various of long-term user engagement optimization tasks.

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


A federal judge signed an order (pdf) on October 9 that forces the government to work with news organizations to coordinate the release of videos showing the force-feeding of a Guantánamo inmate.
Syrian Abu Wa'el Dhiab, who was cleared for release from the prison camp in 2009, has intermittently been on a hunger strike protesting his confinement. He petitioned merely that he be allowed to go to the feedings in his wheelchair rather than being forcibly taken from his cell to a restraint chair in another room and that a doctor, rather than prison officials, decide when he must be force fed. Steven Miles from the Center for Bioethics at the University of Minnesota testified earlier that the feedings were done in an abusive way, according to The Washington Post.
U.S. District Court Judge Gladys Kessler has ordered that prison officials' identities be concealed on the videos, then government officials work with the Associated Press, Bloomberg, CBS, Dow Jones, First Look Media, The Guardian, McClatchy, National Public Radio, the New York Times Company, Reuters, USA Today, The Washington Post and other outlets to come up with a plan by October 20 to release the tapes.
Kessler rejected the government's claim that the release of the video would harm national security. "The Court finds—as it will now detail—that most of [the justifications] are unacceptably vague, speculative, lack specificity, or are just plain implausible," she wrote (pdf).


Ratha Yatra, the annual chariot (Ratha) festival is held in many parts of India and also in some parts of the world. The Puri Ratha Yatra festival is the oldest and the most famous among all. During this festival Lord Jagannath along with his elder brother Balabhadra and younger sister Subhadra starts for a grand journey to their masi's (aunt) house which is the Gundicha Temple situated at a distance of 2 km from the main Jagannath temple. The grand procession from one temple to the temple is held every year with much pomp and grandeur and is called the Ratha Yatra.
I was in Puri a few days before this auspicious occasion of Ratha Yatra. The ever-busy city gets a new makeover during this festival but the preparations start days ahead of the ultimate day of the Ratha Yatra. Three chariots for the respective Gods are prepared every year with wood from specified trees. The logs are collected from the specific place and are set afloat in Mahanadi and then brought to the temple area by road.
The deities are carried to their respective chariots through the Singhadwar of the temple. Thousands of devotees from across the world wait impatiently outside the temple to take part in this grand festival. The chariot is pulled by the devotees with the help of thick ropes. The chariots then move through the street of Puri called Badadanda till it reaches the destination for a week at Gundicha Temple. After the stay of a week at the aunt's house, the Gods return to the main temple.
Three chariots have different names, the chariot of Lord Jagannath is known as Nandighosha, that of Lord Balabhadra is known as Taladhwaja and that of Goddess Subhadra is known as Devadalana. Each chariot is attached with four horses which are of different colours, dark for Balarama, white for Jagannatha and red for Subhadra. Daruka, Matali and Arjuna are the respective charioteers for Jagannath, Balabhadra and Subhadra.
The chariots are covered with red, green, black and yellow cloth and adorned with leaves and flowers. The chariots can be identified by their colour and height. The tallest Ratha belongs to Lord Jagannath which is forty-five feet high with the canopy of red and yellow cloth. The chariot of Lord Balabhadra is forty-four feet high covered with the red and green cloth while that of Goddess Subhadra is the smallest of the three with the height of forty-three feet covered with red and black cloth.
The temple shaped richly adorned large chariots are drawn by mass of devotees who gather here to ward off their sins by the very tough of the chariots or the rope. This is a faith that is carried on for generations, one who pulls the rope or even touches the rope of the chariot is showered with divine blessings and attain salvation.
The preparations were on with great enthusiasm as a few days were left of this grand festival. The chariots were not yet ready, workers were toiling hard to construct this magnificent vehicle. Piles of logs were accumulated on one side of the roadway near the temple through which the prepared vehicle of the Lords will roll on. I only had the picture of the chariots were made but not of the final decorated Rathas so I had to lend the cover picture from Wikipedia.
I was not fortunate enough to be a part of this festival but was blessed enough to see the preparation, to see how a large number of people participate to make the huge, sturdy and spectacular chariot that carries the Gods. This is a festival of masses where people of all class gather to be blessed by touching the rope of the chariots. Faith that keeps us going, faith that brings hope, faith that let us live. Let this faith bring peace in this universe, let this faith brotherhood and love and let this Ratha Yatra bring happiness and prosperity to all.
Puri at a glance, with travel information.
Doesn't it get really crowded? I haven't visited Puri during Ratha Yatra but remember seeing the procession on TV once. The king swept the road as the chariots proceeded. I didn't know that the chariots are reassembled every year until now. Thanks for your post, Sarmistha!
Certainly the most popular Rath Yatra in India.


Coleen Nolan has ruled out appearing on this year's "I'm a Celebrity... Get Me Out Of Here!".
Despite speculation the 53-year-old star could be taking her "Loose Women" panellist opinions Down Under, the singer is adamant she won't be gracing the Australian jungle with her presence.
A message on her official Twitter account read: "Hello tweeties - we can confirm that our Coleen is definitely NOT going into the #imacelebrity jungle this year .... she's looking forward to watching it from the safety of her sofa tho #DontBelieveTheRumours (sic)"
The presenter is no stranger to reality TV after appearing on "Celebrity Fit Club" and "Dancing on Ice", but she doesn't look set to add the jungle to her list.
ITV have confirmed the programme will return at 9pm on Sunday November 18th, and this year's edition of the reality show - which features several celebrities who live together in a jungle environment without luxuries - will be hosted by Declan Donnelly and newcomer Holly Willoughby.
Holly is replacing usual presenter Ant McPartlin who is currently taking time out from his career after he recently spent time in rehab and was fined £86,000 for drink-driving.
Aside from Coleen, Katie Price's ex Kieran Hayler, The Chase's Mark Labbett and former "Coronation Street" actor Shayne Ward have all been tipped to take part.
While audiences are excited, there are growing fears the launch could be in jeopardy because forecasters in New South Wales region of Australia have warned that downpours and thunderstorms are likely - which will have a huge impact on the celebrity contestants" ability to set up camp.
A source recently said: "The place has been through a drought for the past few months and everyone has been praying for some rain."
"Now it's set to come right at the start of "I'm a Celebrity"."


text:
KCSA
Public and Investor Relations, Corporate and Marketing Communications
FOR: 4Kids Entertainment, Inc.
KCSA Todd Fromer / Michael Cimini
CONTACTS: (212) 896-1215 / (212) 896-1233
todd@kcsa.com / mcimini@kcsa.com
4Kids Entertainment Reports Third Quarter Results
Net Revenues Increase 109%, Net Income Rises 46%
NEW YORK, November 13, 2003 – 4Kids Entertainment, Inc. (NYSE: KDE) today announced net revenues for the third quarter ended September 30, 2003 climbed 109% to $25.3 million from $12.1 million in the same period last year. Net income for the quarter was $2.9 million, or $0.20 per diluted share, compared to net income of $2.0 million, or $0.14 per diluted share, in the year-earlier period, an increase of 46%.
For the nine months ended September 30, 2003, net revenues rose 156% to $69.7 million from $27.2 million in the same period last year. Net income for the nine-month period was $9.5 million, or $0.68 per diluted share, compared to net income of $4.7 million, or $0.35 per diluted share, in the same period a year earlier, an increase of 100%.
Al Kahn, Chairman and Chief Executive Officer of 4Kids Entertainment, said, "Third quarter results reflect the continued success of our strategy to create many different revenue streams based upon a diversified property portfolio. Yu-Gi-Oh!™ and Pokémon® continue to be among the top ranked children's programs on the Kids' WB! network. Strong retail sales of Yu-Gi-Oh! related products and licensed merchandise were one of the primary drivers of revenues for our licensing division. 4Kids Home Video shipped its two-millionth Yu-Gi-Oh! title in September."
"Teenage Mutant Ninja Turtles™ also continues to build momentum at retail. The Teenage Mutant Ninja Turtles toy line from Playmates® contributed significantly to our results for the third quarter. We also released the first Teenage Mutant Ninja Turtles home video title in the U.S. in September. The Teenage Mutant Ninja Turtles television series has been licensed for broadcast in many international markets, where there already is substantial interest in Turtles licenses. We also received contributions this quarter from licensing programs for Artlist Collection: The Dog™ and Nintendo® Classic™."
Commenting on the Fox Box, 4Kids' four hours of programming on Saturday mornings on the Fox Network, Kahn said, "We launched the 2003-2004 Fox Box season on September 6 with exciting new shows, including Shaman King and Sonic X™ as well as new episodes of returning shows Teenage Mutant Ninja Turtles, Kirby: Right Back At Ya!™ and Ultimate Muscle: The Kinnikuman Legacy™. Season to date, this new line-up is rating higher than the comparable period last season."
"We are continuing to work with our major licensees to prepare for the holiday season. Konami® has released new Yu-Gi-Oh! and Teenage Mutant Ninja Turtles video game titles. Mattel® will introduce new Yu-Gi-Oh! toys and Playmates will introduce new Teenage Mutant Ninja Turtles toys in the fourth quarter," commented Kahn.
"With Fox Box as our content distribution platform, 4Kids is well positioned to launch new properties. We also have the financial strength to support new and existing licensing programs," concluded Kahn.
About 4Kids Entertainment
Headquartered in New York City with international offices in London, 4Kids Entertainment, Inc. (NYSE: KDE) is a global provider of children's entertainment and merchandise licensing. 4Kids, through its wholly owned subsidiaries, provides domestic and international merchandise licensing; television, film, music and home video production and distribution; media planning and buying; product development; and Web site development. For further information, visit the Company's Web site at www.4KidsEntertainment.com.
This press release contains forward-looking statements. Due to the fact that the Company faces competition from toy companies, television networks, motion picture studios and other licensing companies, and the uncertainty of public response to the Company's properties, actual results or outcomes may differ materially from any such forward-looking statements.
This release and prior releases are available on the Company's Worldwide Web site at www.4KidsEntertainment.com
You may register to receive 4Kids Entertainment's future press releases or to download a complete Digital Investor Kit™ including press releases, regulatory filings and corporate materials by clicking on the "Digital Investor Kit™" icon at www.kcsa.com.
Tables Follow:
4KIDS ENTERTAINMENT, INC. AND SUBSIDIARIES
CONSOLIDATED BALANCE SHEET HIGHLIGHTS
(In thousands of dollars)
Cash and cash equivalents $ 63,491 $ 78,712
Investments 35,988 10,787
Working capital 124,284 114,298
Total assets 174,240 162,839
Total liabilities 28,599 30,168
Total stockholders' equity $ 145,641 $ 132,671
CONSOLIDATED STATEMENTS OF INCOME (UNAUDITED)
(In thousands of dollars, except share data)
Nine Months Ended
NET REVENUES $ 25,334 $ 12,099 $ 69,681 $ 27,238
COSTS AND EXPENSES:
Selling, general and administrative 8,579 6,842 23,996 16,328
Production service costs 2,574 327 6,556 804
Amortization of television and film costs and
Fox broadcast fee 9,700 1,919 24,204 3,301
Total costs and expenses 20,853 9,088 54,756 20,433
INCOME FROM OPERATIONS 4,481 3,011 14,925 6,805
INTEREST INCOME 252 388 835 1,180
INCOME BEFORE INCOME TAX
PROVISION 4,733 3,399 15,760 7,985
INCOME TAX PROVISION 1,866 1,436 6,278 3,241
NET INCOME $ 2,867 $ 1,963 $ 9,482 $ 4,744
PER SHARE AMOUNTS:
Basic earnings per common share $ 0.22 $ 0.16 $ 0.72 $ 0.38
Diluted earnings per common share $ 0.20 $ 0.14 $ 0.68 $ 0.35
Weighted average common shares
outstanding - basic 13,247,459 12,597,355 13,174,119 12,588,162
outstanding - diluted 14,169,729 13,678,026 14,018,995 13,656,787

redpajama_set_name: RedPajamaCommonCrawl


Maritime by Holland (Nederland Maritiem Land) is the Dutch maritime cluster organisation that connects twelve maritime sectors: sea ports, offshore, maritime supply industries, shipbuilding, seagoing shipping, dredging, maritime services & knowledge institutes, inland shipping, Royal Navy, yachtbuilding/watersport industries, and fisheries. Trade Associations and leader firms from these sectors form the members. Nederland Maritiem Land acts as the facilitator of the network and as the voice of the cluster on collective maritime opportunities and challenges.
One of the world's great maritime powers is located on the North Sea Coast. It comprises a vibrant Eco system of 12,000 high tech maritime companies within a 100 kilometre radius. A place where yacht- and shipbuilders collaborate with marine engineers, dredgers share ties with equipment suppliers, and R&D giants compare notes with specialised consultancies. This provides an unbeatable combination of maritime expertise and hands on experience. The cluster provides employment to 271,500 people and generates a combined added value of € 23.3 billion.
Today Holland is home to many companies that are at the top of their business. A powerful cluster of twelve different sectors, connected to the rivers and seas, provides an unbeatable combination of expertise and experience. Holland exports nearly two thirds of its maritime work, changing the life of communities worldwide. The first gasoil free, LNG powered inland vessel, the Greenstream was designed and built by the Dutch. This innovation means a tremendous leap forward in sustainability and efficiency. We gave the world the marvel that is Dubai's Palm Island and the Maasvlakte II. Connoisseurs covet our yachts. Our ports and Dutch flagged merchant ships like reefers, seagoing towage, general cargo, semi-submersibles, product- and gas tankers catalyse the global economy.
These maritime successes are driven by a signature 'Can Do' attitude. The Dutch believe that any problem can be solved and any dream can be realised. We are respected craftsmen, who take pride in the quality of our work. We are audacious,yet pragmatic with a reputation for on-time, on-budget delivery in the toughest of circumstances.
Our work has tremendous value. We are actively tackling the challenge of climate change, population growth and energy scarcity. Sustainability is at the forefront of our minds. We are constantly pioneering innovative designs and energy efficient propulsion technologies. There is an ongoing search for lighter materials and other ways to reduce our environmental footprint.
Maritime equipment from Holland, powers the world's complex, special vessels. We protect urban areas against rising seas.
The Dutch are innovators in the offshore oil- and gas-production sector, and our navy participates in peacekeeping missions worldwide. The Dutch relish the chance to take on extraordinary complex projects. The right expertise is always available somewhere in the maritime cluster. Our instinct is to collaborate across industries and with clients. We seek common goals and a shared vision. This makes it easy for governments and companies worldwide to work with the Dutch.


text:
Researcher, Carbon Leadership Forum, University of Washington
Brad Benke
Brad Benke, AIA, is a Research Scientist at the Carbon Leadership Forum focused on creating and improving LCA data, tools, and policies to rapidly scale and advance carbon drawdown strategies for the built environment. With experience in regenerative design consulting and deep-green architecture, he builds resources and agency for designers, builders, and organizations to take immediate action on climate change issues. Brad is a former Senior Architect at McLennan Design, where he led diverse teams and stakeholders towards achieving carbon neutrality goals for buildings and organizations across the country. Brad is also a LEED accredited professional and a former Co-chair of AIA Seattle's Committee on the Environment.
by Brad Benke
People say that if you want to build a better future for our planet, it has to come from a place of love. I like to think I've always followed this advice, but to be honest, it's taken a lot of years, heartaches, and failures for it to really sink in.
I headed West in search of mountains after graduating with an architecture degree from North Dakota State University. I quickly fell in love with ridgelines, rivers, and forests, which, in turn, became my best teachers on the reality and urgency of the climate crisis. It didn't take long for my love of wild places to raise questions about my profession on multiple ethical levels. Does this product damage the planet? Where did it even come from? Is its manufacturing choking the rivers I love? And a favorite of mine – should this be made at all, and who gets to decide?
Aldo Leoppold had words about this, "Harmony with land is like harmony with a friend; you cannot cherish his right hand and chop off his left." Even with the best intentions, it is almost impossible to leave no trace and I often felt like I was complicit in the destruction of the very same places I'd go to recharge. I deeply wanted to change that, and fortunately, I found countless mentors who were all asking the same question – How can we do better?
This photo is taken from a ridge in the Olympic National forest. It's a favorite place to hike for my wife and me (and our dog). Although beautiful, the sunset was only possible thanks to wildfire smoke blowing in from Oregon.
Since then I've immersed myself in the world of materials ecology and life cycle thinking while still finding time to immerse myself in wild places of the Pacific Northwest. There has been no shortage of troubling news in the past few years. The painfully apparent effects of social injustice, inequality, climate change, and biodiversity loss can feel like a few centuries worth of bad decisions fell on our generation to solve. Those in the building industry collide with the immensity of these challenges on a daily basis.
I've now been working in the "sustainability" space for close to a decade. Even in this short window I've seen (and gratefully been a part of) some truly inspiring moments. I've had courageous clients who care deeply about sustainable buildings and products, I've watched some green building rating systems go from stretch goals to code minimums, I've worked with major governments and corporations to take real steps toward carbon neutrality in the built environment, and I've collaborated with countless others doing everything they can to catalyze systematic change. This work can be incredibly rewarding. But I've also witnessed (and sadly often been a part of) the hesitation, stubbornness, and ignorance of the building industry towards making the type of radical transformations that are needed. Even with positive changes happening, they can often feel miniscule and snail-paced given the storms of this century that we all face.
Out of all the positive changes I've seen in the past decade one thing has held true – the biggest ones required a big tent. I've learned the importance of not just loving the planet, but loving those who are fighting alongside you, and especially those who aren't.
Turning the wheel of embodied carbon requires nothing less than all of us and it is what excites me most about joining the Carbon Leadership Forum and the incredible community that supports it. I want to express deep gratitude to each and every one of you who are reading this. I know you've likely dedicated countless hours to move these issues forward in your companies and for the profession at large in the spirit of collective action. I hope to return the favor to each of you through my work on improving the messy world of LCA data, tools, and methodologies. I hope to provide examples of how we can do better. If nothing else, please know that it will all come from a place of love.
Email Brad: bbenke at uw.edu
Research Engineer, Carbon Leadership Forum, University of Washington Monica Huang is a research engineer with experience in environmental life cycle...


After reading so much on Fantasia e hookah pens, you must be wondering where to buy Fantasia E Hookah. Well, for most of us, we can do a simple Google search and be able to find where to buy Fantasia e hookah. However, what you find may not be the best price or deal available!
I know some of us don't care for saving money, but for those of us who do, you'll be happy to know that you can save dollars at a time off each pen.
First of all, Fantasia E Hookah pens retail for $15.95 and that is how much they are generally being sold for. If you browse around Marketplaces, you may find them for a bit cheaper. HazyShop has been selling Fantasia e hookah pens over the past several years for about $7, which is almost half off. They are the lowest price we've seen over the years. If you're interested, click here.


The new company behind the Swiftpack and King brands of automated liquid and tablet filling systems says it is to embark on a substantial programme of investment including new product launches and machine upgrades.
Following a management-led buyout, Swiftpack has become part of the newly-created Nova Packaging Systems Group Inc, which has bought the Swiftpack and King businesses from US-based DT Industries with support from investment partners Kinderhook Capital.
Massachusetts-based Nova also simultaneously acquired the Kalish, Lakso and Merrill machinery brands.
Swiftpack's management team, headed by managing director and co-investor Des O'Neil, remains unchanged, with the company retaining its UK headquarters at its 50 000ft2 manufacturing facility at Alcester. Warwickshire.
"We are delighted to have maintained the majority of the company's skilled workforce and pleased Swiftpack Automation's expansion plans will bring further improvements both to product ranges and in other areas such as research and development and after-sales support," says O'Neil.
At the recent Total exhibition Swiftpack demonstrated a new 12-lane tablet counter with a handling speed of over 100 bottles/min and there are plans to launch a 24-lane version at November's Pack Expo.


The Union science ministry has announced a 24 per cent hike in fellowship amounts for PhD scholars, angering sections of recipients who point out that their tuition fees alone have risen by 276 per cent over the past four years.
The ministry has also promised "periodic enhancements" and additional "financial incentives" linked to scholars' performance during the PhD, but a senior official told The Telegraph that the finance ministry has rejected a proposal for a 4 per cent annual raise.
Under the revised rates, junior research fellows in the first two years of PhD will receive Rs 31,000 per month instead of the current Rs 25,000 per month while senior research fellows in subsequent PhD years will receive Rs 35,000 instead of Rs 28,000, the science ministry said on Wednesday.
The increase will impact over 60,000 scholars pursuing PhD programmes in the physical, chemical and biological sciences, engineering, mathematics, agricultural sciences and pharmaceutical sciences in academic institutions across the country.
Research associates, or post-doctoral scholars, will receive a 30 per cent to 35 per cent increase in their salaries, the ministry said. The top bracket of Research Associateship is fixed at Rs 54,000, and all research fellows will receive house rent allowance under Central government rules.
The Union human resource development minister Prakash Javadekar described the decision as a "major gift to researchers" from the Narendra Modi government. "The decision to hike fellowship amount shows … the Narendra Modi government's care for researchers and innovators," the minister tweeted.
The Centre had last increased fellowship amounts by 50 per cent in October 2014. But science and technology secretary Ashutosh Sharma said a comparison with the new 24 per cent hike would not be appropriate because the ministry is seeking periodic enhancements.
"We're creating an inter-ministerial committee that will examine all fellowship matters, including the possibility of periodic enhancements for all, as well as financial incentives linked to performance of scholars during the PhD," Sharma said.
But a senior government official told this correspondent that the finance ministry had rejected a proposal — the first-ever by science departments — to increase the fellowship amounts by 4 per cent every year.
The committee will be expected to devise metrics, such as publications in top research journals, to assess PhD scholars' performance, he said. High performers would receive financial as well as academic incentives in addition to their fellowship amounts.
But scholars have decried the hike saying the increase provides fresh evidence for the government's disregard for researchers. "We're disappointed — we'd asked for 80 per cent hike," said a PhD research scholar at the Indian Institute of Technology, Bombay.
Scientists at the Indian Institutes of Science Education and Research compared the raise in fellowship amounts to the increase in tuition fees. The fee has increased from Rs 4,840 per semester in 2014 to Rs 18,200 in 2019, said Sachin Pandey, a fourth-year scholar at the IISER, Calcutta. "This is a 276 per cent increase in fees," he said.
Pandey said some researchers who are also receiving HRA would get effective increases of only 18 per cent and not 24 per cent as claimed by the government. "This is because the HRA rates have been lowered under the seventh pay commission," he said.


a random character if you have not received a PM.
- Tonnes of graphical and attribute fixes around the world of Sharda.
- /collectfurniture now collects 5 furniture items from your house.
- The Auction House has arrived!
which disables the current party window HP/SP bars.


post zu favoriten 71m2 - Amazing flat in Berlin Mitte (Rosenthaler Platz) dieses posting verbergen einbl.
I sublet my beautiful appartment "Berlin style" perfect located: 4 min Rosenthaler Plaz (U8).
It's really central and in a wonderful street. Cafes, REWE supermarket, tram/ubhan/sbhan really close.
Island of Museum 15 min walking.
71 mq, one bedroom (french size bed: 140x200), sharing garden downstairs.
I ask 700 euro for the whole period + a little deposit that of course I will give you back at the end if everything will be fine.
If you need more information or pictures, feel free to ask. Thx!


text:
Q: MongoDB: SyntaxError: missing } after property list So I have a basic test db with Mongo setup. Here is what the users collection currently looks like:
{
    "_id" : ObjectId("5d8e23ed2f8849a57711e48e"),
    "firstName" : "Joe",
    "lastName" : "Black",
    "email" : "joeblack@gmail.com",
    "portfolio" : [ ],
    "watchlist" : [ ],
    "goals" : [ ],
    "badges" : [ ],
    "ranks" : [ ],
    "hasPortfolio" : false,
    "hasWatchlist" : false,
    "hasGoals" : false
}
{
    "_id" : ObjectId("5d8e23ed2f8849a57711e48f"),
    "firstName" : "Juan",
    "lastName" : "Gonzales",
    "email" : "juangonzales@outlook.com",
    "portfolio" : [ ],
    "watchlist" : [ ],
    "goals" : [ ],
    "badges" : [ ],
    "ranks" : [ ],
    "hasPortfolio" : false,
    "hasWatchlist" : false,
    "hasGoals" : false
}
{
    "_id" : ObjectId("5d8e338fe0562db42af793c9"),
    "firstName" : "Foo",
    "lastName" : "Bar",
    "email" : "foobar@baz.com",
    "portfolio" : [ ],
    "watchlist" : [ ],
    "goals" : [ ],
    "ranks" : [ ],
    "badges" : [
        {
            "name" : "Tester",
            "type" : "bronze",
            "new" : true
        }
    ],
    "hasPortfolio" : false,
    "hasWatchlist" : false,
    "hasGoals" : false
}

What I'm trying to do, is update the portfolio array on user foobar@baz.com. But I keep getting the following error:

2019-09-27T13:07:00.590-0500 E QUERY    [js] SyntaxError: missing } after property list @(shell):6:8

The command
db.users.update({email: 'foobar@baz.com'}, {
  $set: {
    portfolio: [
      {
        availableSupply: "1000000"
        currency: "SHIT"
        exchange: "Binance",
        exchange_base: "btc",
        marketCap: 10000,
        name: "ShitCoin",
        percentage: 100,
        price: 0.01,
        position: 1,
        value: 0.01,
        inWatchlist: false
      }
    ]
  }
})

A: plz try this
db.users.update({email: 'foobar@baz.com'}, {
  $set: {
    portfolio: [
      {
        availableSupply: "1000000",
        currency: "SHIT",
        exchange: "Binance",
        exchange_base: "btc",
        marketCap: 10000,
        name: "ShitCoin",
        percentage: 100,
        price: 0.01,
        position: 1,
        value: 0.01,
        inWatchlist: false
      }
    ]
  }
})

A: Just add the commas after each item:
portfolio: [
  {
    availableSupply: "1000000", // You missed these
    currency: "SHIT", // You missed these
    exchange: "Binance",
    exchange_base: "btc",
    marketCap: 10000,
    name: "ShitCoin",
    percentage: 100,
    price: 0.01,
    position: 1,
    value: 0.01,
    inWatchlist: false
  }
]

redpajama_set_name: RedPajamaStackExchange


Prozack is a feminized indica dominant Marijuana strain created by crossing Lavender and Kush. Strong hashish aroma with a pleasant, long lasting effect.
Prozack has a strong hashish aroma that will delight all hash lovers, while, its effect is relaxing and sedative, very long-lasting. It has a great medicinal potential used to treat a number of ailments.
The plant grows bushy and compact, not very tall (no more than 1.5-2m outdoors). The buds of this Marijuana strain are rocky, extremely compact. It is ideal for the production of top grade cannabis concentrates due to the quality and quantity of resin glands.
In a growing room you will need to give it a growth period of 3-4 weeks to move to a bloom period of 65-75 days offering productions of up to 450 grams per square meter in optimal conditions. In the outside, you will have it ready for harvest in early October (northern hemisphere), where you can collect 500 grams per plant.


text:
Mabel Meldrum, mother of Paradise
Mabel Meldrum, Mother of Paradise Miss Mabel Lee was sent out west by her father because she was too "boy crazy" back home. Unfortunately, Mabel's father sent her to a state with a very unbalanced ratio of men to women, and she quickly married a man named Gordon Meldrum and made Wyoming her new home. Gordon and Mabel inherited what we now know as Paradise Ranch from Gordon's father and used it as a working cattle ranch. Young Miss Mabel Lee Gordon worked as a doctor in Buffalo and managed Paradise as a working cattle ranch. Mabel and Gordon soon realized what a treasure the ranch was and began inviting their friends to spend time at the ranch. Over time, it soon evolved into a guest ranch. Some of the first guests at Paradise Mabel and Gordon had a son, Jack, in 1909. Shortly thereafter, Gordon died tragically from a ruptured appendix with Mabel at his side in 1911. Mabel's first husband, Gordon Meldrum In 1916, Mabel married a previous guest of the ranch named George Wolcott. Wolcott was a Harvard Law School graduate and a very wealthy man. He built Mabel a "winter cottage" in town that still exists today. During their marriage, Mabel ran Paradise Ranch, building it into a major guest ranch. She led fishing and hunting expeditions, advertised for new clientele, and managed all the employees. Five years after their marriage, Wolcott died suddenly in his sleep. Mabel sold the ranch in 1926 and married Robert Hayden. During this marriage, the once gentle, refined lady transformed the way she dressed. She changed from fashionable, Eastern clothing to skirts worn with boots and cartridge belts, to finally cutting her hair and wearing men's clothing! Mabel with Robert Hayden Mabel was widowed again after 23 years of marriage...


text:
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Opinion: Trusting Fromm experience could be costly for Smart
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Opinion: Trusting Fromm experience could be costly for Smart (Photo by Jamie Squire/Getty Images)
By Jared Yamamoto
We're still fresh off the NCAA College Football National Championship game and Nick Saban's Alabama Crimson Tide empire was upset again by the Clemson Tigers on Monday night.
Yes, you may be shocked to see me writing about college sports instead of National or Georgia politics. It's been so annoying in the political arena with all the border wall, paper ballots, and government shutdown nonsense so let's switch it up and cling to college football for at least a few more days.
I shocked many of my friends when I told them that I pulled for Clemson in the National Championship to avenge the Georgia Bulldogs from their agonizing defeat they faced last season to the Crimson Tide in the same game.
Now when you tell people that you pulled for Clemson, most Southeastern Conference 'SEC' fans tell you that "you've got to pull for your conference" or "how could you pull for a soft conference like that" and my response is consistent, "I'm ready for the Alabama dynasty to die."
Alabama has been annoyingly dominant in the SEC for over a decade. They've crushed SEC competition, something my friends that are big fans of the University of Georgia know all too well. That's a story for another day.
I'm not here to rag on the Dawgs. In fact, I've always had a soft spot for the Dawgs and genuinely want them to do well. I grew up in Atlanta and some of my finest memories were riding around Atlanta in the car doing outdoorsy things in the Fall, listening to legendary Bulldogs announcer Larry Munson with my father on WSB.
Among the many legendary calls he made, one in particular stands out – back in 2001 when the Dawgs upset the Tennessee Volunteers in Knoxville: "We just stepped on their face with a hobnail boot and broke their nose," Munson remarked. "We just crushed their face."
I also remember the heartbreaking losses and crushing upsets.
With all the highs and lows of UGA football, I've been there with you and rode the emotional roller coaster with my friends and family that are die-hard fans.
Trust me, I remember the Jim Donnan and Mark Richt years.
But when Kirby Smart was hired as head coach back in 2015 the future seemed especially bright for UGA football.
After a decent first season with Kirby Smart, the Dawgs exceeded all expectations and stunned the country when they went to the National Championship in Smart's second season. I think we all can recall that outcome…
Smart has the makings of a great coach and there's no doubt that his recruiting and mindset have helped improve UGA's roster from top to bottom. However, I'm noticing a problem developing in Athens.
Opinion: Trusting Fromm experience could be costly for Smart Georgia Bulldogs Mascot Uga. (Scott Cunningham/Getty Images)
Aside from Alabama's dominance over the Dawgs, not one, but two 5-star quarterbacks that were successfully recruited to Athens have left UGA for other schools during the Smart era.
Just to put this in perspective, the hardest part for these coaches is to get these 18-year-old kids to commit and physically attend your university with all the competition in the SEC.
I know that we'll all kick ourselves if now former UGA freshman quarterback Justin Fields, who has basically transferred to Ohio State, takes the Buckeyes to the promise land next season.
But the person who was quickly forgotten was quarterback Jacob Eason, who lost his job to current starting quarterback Jake Fromm in 2017 after injuring his left knee in the first quarter of the first game of the season against Appalachian State.
He transferred to the University of Washington and is set to start this Fall for the Huskies.
I'm also reading reports that Clemson's freshman now National Champion quarterback Trevor Lawrence, the Cartersville, Georgia sensation, heavily considered UGA too. If you haven't seen him, he may already be ready for the NFL.
That's a bad trend brewing at UGA and I don't like the long-term trajectory.
Let me first say that Jake Fromm is a great quarterback and many schools would be glad to have him. But I've analyzed a lot of football and I don't see him leading UGA to a National Championship.
I really hope that I am wrong. Seriously. I want UGA to do well, but Fromm has a problem in big games when UGA is forced to throw to get back into games.
In 2017, the National Championship year, he proved that he couldn't throw his way back into a contested road game at Jordan-Hare Stadium at Auburn.
He had horrific numbers in the National Championship game against Bama throwing 1 touchdown, 2 interceptions, going 16 for 32 passing.
And in 2018, the LSU blowout in Death Valley will forever haunt Dawgs fans. Why? Because, if the Dawgs had won that game, the SEC Championship against Alabama would not have mattered for either team. Both teams would have been seeded for the College Football Playoff and we all know what happened.
Nonetheless, I don't blame Fromm for the recent SEC Championship loss, because Coach Smart's 4th and long fake punt in the 4th quarter will loom larger than any players performance in that game.
For UGA's program to reach that elite tier with the Alabamas, Clemsons, Ohio States, and Notre Dames of the world, Coach Smart must take some chances and put the best players in their positions even if it means disrupting the politics of the team.
UGA has benefitted from a relatively weak SEC East over the past 3 years and as we all know, the trends will change, and Dawgs fans should be cautious about improving schools like Kentucky and Florida.
It's not easy to win a National Championship, but the great recruits are here and UGA has a real chance to contend for the next couple of years. Now, Smart needs to make it his top priority to keep these 5-star players flocking to Athens.
Jared Yamamoto is a Doctrinaire and Producer of The Von Haessler Doctrine radio show heard daily from 9-Noon on News 95.5 and AM 750 in Atlanta, Georgia.
Email jared.yamamoto@coxinc.com
Twitter: @jaredyamamoto
Instagram: @jaredyamamoto
I-20 @ Wesley ChapelDekalb Co.: Roadwork on I-20/wb near Wesley Chapel Road blocking 2 left lanes.
I-85 after Jonesboro RoadRED ALERT: Fulton Co.: Crash on I-85/sb after Jonesboro Road blocking ALL LANES; use Roosevelt Hwy as alternate.
Georgia woman, mother of 'zip tie guy' arrested after Capitol riots
The pot thickens: No Mega Millions winner Friday night means even larger jackpot next week
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The IGFA World Records committee has a full plate of records to review this month, including these six saltwater and three freshwater catches.
Saltwater anglers in Australia and both coasts of the United States are certainly eager to hear word of the potential new black grouper, Atlantic snook, Pacific halibut, and southern bluefin tuna records, but one more record application may be drawing a bit more attention: Guy Yocom's record application for the yellowfin tuna he caught off the coast of Cabo San Lucas, Mexico.
The bite turned on for three ambitious freshwater anglers as well. These potential world records of trahira, barramundi and bull trout are also awaiting review by the World Record committee.


text:
using System;
using System.Collections.Generic;
using System.Linq;
using Xunit;

namespace Aoite.Redis
{
    class RedisStreamTests
    {
        private IRedisClient CreateClient(bool flushAll = true)
        {
            var client = new RedisClient(6379);
            if(flushAll)
                client.FlushAll();
            return client;
        }
        private void SetKey(IRedisClient client, int count)
        {
            for(int i = 1; i <= count; i++)
            {
                client.Set("key" + i, count);
            }
        }

        [Fact()]
        public void MyTestMethod()
        {
            var client = this.CreateClient();
            client.Set("a", "b");
            Assert.Equal("b", (string)client.Get("a"));
            var value1 = new BinaryValue(TestModels.RootContainer.CreateFakeRoot());
            client.Set("x", value1);
            var value2 = client.Get("x");
            Assert.Equal(value1.ByteArray, value2.ByteArray);
            Assert.IsType<TestModels.RootContainer>(value2.ToModel());
        }
        [Fact()]
        public void MyTestMethod2()
        {
            var client = this.CreateClient();
            var s = client.Keys("*");
            Assert.Equal(new[] { "key:1", "key:2", "key:3", "key:4", "key:5" }, s.OrderBy(x => x).ToArray());
        }
        [Fact()]
        public void MyTestMethod4()
        {
            var client = this.CreateClient();
            SetKey(client, 20);
            var s = client.Scan().ToArray();
            Assert.Equal(20, s.Count());
            //Assert.Equal(new[] { "key:1", "key:2", "key:3", "key:4", "key:5" }, s.OrderBy(x => x).ToArray());
        }

        [Fact()]
        public void MyTestMethod3()
        {
            using(var mock = new MockConnector("localhost", 9999, "+OK\r\n", "+OK\r\n"))
            using(var redis = new RedisClient(mock))
            {
                Assert.True(redis.Set("key", "value"));
                Assert.Equal("*3\r\n$3\r\nSET\r\n$3\r\nkey\r\n$5\r\nvalue\r\n", mock.GetMessage());

                Assert.True(redis.Set("key", "value"));
                Assert.Equal("*3\r\n$3\r\nSET\r\n$3\r\nkey\r\n$5\r\nvalue\r\n", mock.GetMessage());
            }
        }

        [Fact()]
        public void TypeTest()
        {
            using(var client = this.CreateClient())
            {
                client.Set("a", new BinaryValue(null));
                var value = client.Get("a");
                Assert.Null(value.ToModel());
                Assert.Null((string)value);
                //client.Set("b", "6x");
                //Assert.Equal(RedisType.String, client.Type("a"));
                //Assert.Equal(RedisType.String, client.Type("b"));
            }
        }

        [Fact()]
        public void BRPopLPushTest()
        {
            using(var client = this.CreateClient(false))
            {
                var t = client.BRPopLPush("msg", "reply", 1);
                Assert.Equal("1", (string)t);
                t = client.BRPopLPush("msg", "reply", 1);
                Assert.Equal("2", (string)t);

                t = client.BRPopLPush("msg", "reply", 1);
                Assert.Null(t);
            }
        }
    }
}

redpajama_set_name: RedPajamaGithub


Abandoned concert venue legendary for its rock counterculture.
This Detroit ruin was once a gothic bastion of its community, but now it stands as a haunting ruin.
Hell on Earth is actually a small town in Michigan where anyone can be mayor for a day.
Turnip-shaped island just off the Michigan shore in Lake Huron.
Largest magic museum in the United States with more than half a million pieces of memorabilia.


This projector lamp is known under the following lamp codes: VLT-EX240LP, 5J.J3V05.001, 5J.J3T05.001, 5J.J8J05.001, 5J.J6S05.001, VLT-EX320LP, 5J.J9P05.001, 5J.J9W05.001, 5J.J8G05.001, LV-LP37 / 0030C001AA, 5J.J4105.001, 5J.JCL05.001, 1408 0040 and fits in the following projectors: MITSUBISHI EW270U, MITSUBISHI EX200U, MITSUBISHI EW230U-ST, MITSUBISHI ES200U, MITSUBISHI EX241U, BENQ MX660, BENQ MX711, BENQ MX660P, BENQ MX615+, BENQ MX613ST, BENQ MX710, BENQ MS614, BENQ MX615, BENQ MW663, BENQ MS616ST, MITSUBISHI EX220U, MITSUBISHI EX240U, MITSUBISHI EW330U, MITSUBISHI EW331U-ST, MITSUBISHI EX320U-ST, MITSUBISHI EX320U, MITSUBISHI EX321U, MITSUBISHI EX321U-ST, BENQ MX666, BENQ MW665, BENQ MX618ST, CANON LV-S300, CANON LV-X300, BENQ MS612ST, BENQ TH682ST, Robe DigitalSpot 3500DT, BENQ MX666+, BENQ MW665+.
High quality UHR projector lamp including housing. We buy our projector lamps directly from a listed Taiwanese manufacturer specialized in the production of projector lamps. The lamp manufacturing complies with the ISO9001 standards. Reliable and economically an attractive alternative with proven high quality over the years.


text:
Posted on September 21, 2016 by Delaware Gazette
Pumpkin farms bid farewell to summer
By Brandon Klein - bklein@civitasmedia.com
Christy and Rob Leed pose behind a batch of pumpkins to be sold at their pumpkin farm Leeds Farm, 8738 Marysville Road, in Ostrander during the weekends starting on Sept. 17.
Brandon Klein | The Gazette
Leeds Farm in Ostrander features nearly 40 fall-related activities including including mini-zip lines, combine slide and hayrides with animatronics on the trail. Farm goers can buy pumpkins, most of them grown at Rob's farm in Milford Center, or feed the farm's animals. Leeds Farm also host a series of fundraisers throughout the season.
The pumpkins, hayrides and other fall festivities couldn't wait for summer to end on Thursday.
While the Delaware County Fair kicks off today, Leeds Farm, 8738 Marysville Road in Ostrander, will also begin the 2016 harvest season. While closed on the weekdays, the farm will be open to the public from 10 a.m. to 5 p.m. through Oct. 30.
"I went into Lowes the other day and they had all Halloween stuff," said farmer Rob Leeds. "So I guess it's time."
Mid-September is a usual time for Leeds Farm to kick off the fall festivities, sometimes overlapping with the fair. Rob will be working with the junior fair as the director of the Ohio State University Extension in Delaware County during this weekend.
Meanwhile, about 70 employees ranging from high school to college graduates will be handling the day-to-day operations, he said. Many of his employees have moved on to richer careers in medicine, business and education.
"We've found them to be excellent," he said. "The relationship you have with your staff is half the fun of doing this job."
Some employees at the family-owned pumpkin farm were adding the final touches Thursday.
"It's crunch time," said Christy Leeds, Rob's wife.
Farm goers can buy pumpkins, most of them grown at Rob's farm in Milford Center, and feed the farm's animals. Families can also purchase admission to play at the farm's 80 acres of 30 to 40 farm activities including mini-zip lines, a combine slide and hayrides with animatronics on the trail.
The farm's newest addition is "Paintball on the Porch," which features a shooting gallery and ready-to-aim-at targets. The activity is not included in admission
"Agriculture tourism has really taken off in Ohio," Rob said.
"I'm all about ambiance," he said. "It's nice in the fall season to relax."
Leeds Farm also have fundraisers throughout the season. The farm will host its 10th annual Autism Harvest Festival, which offers free admission to families with autistic children from 9 a.m. to 11 a.m. today; and its Witches Night on Oct.6 with proceeds going to breast cancer research.
Other pumpkin farms will be open to the public after fall arrives. Lehner Pumpkin Farm, 2920 State Route 203, in Radnor and Miller's County Gardens, 2488 state Route 37 West, in Delaware will open to the public on Sept. 24.
Lehner Pumpkin Farm, 2920 state Route 203, will be open 10 a.m. to 7:30 p.m. Saturdays and Sundays from Sept. 24 to Oct. 30. The farm features a 1,000 acres including 48 acres of pumpkins and 30 activities.
"Lehner's Pumpkin Farm is gearing up for a fantastic fall season," said Christy Lehner, who run's the farm's events with her husband.
The farm will have special days this season including a visit from the Columbus Zoo & Aquarium on Oct. 9.
Miller County Gardens features hayrides to the pumpkin fields from 10 a.m. to 5 p.m. Saturdays and Sundays; barrel rides for children; a play area and a corn maze. The farm will host its Fallfest, featuring a live band, on Oct. 9.
"It's a way of life," Leeds said.
Editor's note: Shelly Lehner is the owner of Lehner's Pumpkin Farm, 2920 state Route 203, in Radnor. Her daughter-in-law, Christy Lehner, and Christy's husband run its fall events. An earlier version of this story had incorrect information.
http://aimmedianetwork.com/wp-content/uploads/sites/40/2016/09/web1_DSC_0009-1.jpgChristy and Rob Leed pose behind a batch of pumpkins to be sold at their pumpkin farm Leeds Farm, 8738 Marysville Road, in Ostrander during the weekends starting on Sept. 17. Brandon Klein | The Gazette
http://aimmedianetwork.com/wp-content/uploads/sites/40/2016/09/web1_DSC_0025-1.jpgLeeds Farm in Ostrander features nearly 40 fall-related activities including including mini-zip lines, combine slide and hayrides with animatronics on the trail. Farm goers can buy pumpkins, most of them grown at Rob's farm in Milford Center, or feed the farm's animals. Leeds Farm also host a series of fundraisers throughout the season. Brandon Klein | The Gazette
http://aimmedianetwork.com/wp-content/uploads/sites/40/2016/09/web1_20160915_133708-1.jpgBrandon Klein | The Gazette
By Brandon Klein
bklein@civitasmedia.com
Brandon Klein can be reached at 740-413-0904 or on Twitter at @brandoneklein.
Hi! A visitor to our site felt the following article might be of interest to you: Pumpkin farms bid farewell to summer. Here is a link to that story: http://www.delgazette.com/news/33513/pumpkin-farms-bid-farewell-to-summer-2