Neural Model-Based Reinforcement Learning for Recommendation
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging. In this paper, we propose a novel model-based reinforcement learning framework for recommendation systems, where we develop a generative adversarial network to imitate user behavior dynamics and learn her reward function. Using this user model as the simulation environment, we develop a novel DQN algorithm to obtain a combinatorial recommendation policy which can handle a large number of candidate items efficiently. In our experiments with real data, we show this generative adversarial user model can better explain user behavior than alternatives, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.
 This paper belongs to the space of treating recommendation as a reinforcement learning problem, and proposed a model-based (cascaded DQN) approach, using a generative adversarial network to simulate user rewards. Pros: + proposed a set of cascading Q functions, to learn a recommendation policy + unified min-max optimization to learn the behavior model and the reward function + interesting idea of using generative adversarial networks to simulate user rewards. Cons: - in Figure 6 no comparison with model-free (policy-gradient type) of approaches - there is not a lot of detail on the value of the generative adversarial network for the user behavior dynamics, thus this prevents the reader from fully understanding the contribution - only 2 datasets are used - only 100 users for test users seems few - why only 1000 active users were sampled from MovieLens? Personally, I would prefer less details on formulating the recommendation problem as an RL problem (as there have been other papers before with a similar formulation) and more detail on the simulation user reward model, and in general in sections 4 and 5. Also, the experiments could be strengthened.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Model-Based Reinforcement Learning for Recommendation
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging. In this paper, we propose a novel model-based reinforcement learning framework for recommendation systems, where we develop a generative adversarial network to imitate user behavior dynamics and learn her reward function. Using this user model as the simulation environment, we develop a novel DQN algorithm to obtain a combinatorial recommendation policy which can handle a large number of candidate items efficiently. In our experiments with real data, we show this generative adversarial user model can better explain user behavior than alternatives, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper belongs to the space of treating recommendation as a reinforcement learning problem, and proposed a model-based (cascaded DQN) approach, using a generative adversarial network to simulate user rewards. Pros: + proposed a set of cascading Q functions, to learn a recommendation policy + unified min-max optimization to learn the behavior model and the reward function + interesting idea of using generative adversarial networks to simulate user rewards. Cons: - in Figure 6 no comparison with model-free (policy-gradient type) of approaches - there is not a lot of detail on the value of the generative adversarial network for the user behavior dynamics, thus this prevents the reader from fully understanding the contribution - only 2 datasets are used - only 100 users for test users seems few - why only 1000 active users were sampled from MovieLens? Personally, I would prefer less details on formulating the recommendation problem as an RL problem (as there have been other papers before with a similar formulation) and more detail on the simulation user reward model, and in general in sections 4 and 5. Also, the experiments could be strengthened.
assistant
 This paper belongs to the space of treating recommendation as a reinforcement learning problem, and proposed a model-based (cascaded DQN) approach, using a generative adversarial network to simulate user rewards. Pros: + proposed a set of cascading Q functions, to learn a recommendation policy + unified min-max optimization to learn the behavior model and the reward function + interesting idea of using generative adversarial networks to simulate user rewards. Cons: - in Figure 6 no comparison with model-free (policy-gradient type) of approaches - there is not a lot of detail on the value of the generative adversarial network for the user behavior dynamics, thus this prevents the reader from fully understanding the contribution - only 2 datasets are used - only 100 users for test users seems few - why only 1000 active users were sampled from MovieLens? Personally, I would prefer less details on formulating the recommendation problem as an RL problem (as there have been other papers before with a similar formulation) and more detail on the simulation user reward model, and in general in sections 4 and 5. Also, the experiments could be strengthened.