Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence
A conjugate Gamma-Poisson model for Dynamic Matrix Factorization incorporated with metadata influence (mGDMF for short) is proposed to effectively and efficiently model massive, sparse and dynamic data in recommendations. Modeling recommendation problems with a massive number of ratings and very sparse or even no ratings on some users/items in a dynamic setting is very demanding and poses critical challenges to well-studied matrix factorization models due to the large-scale, sparse and dynamic nature of the data. Our proposed mGDMF tackles these challenges by introducing three strategies: (1) constructing a stable GammaMarkov chain model that smoothly drifts over time by combining both static and dynamic latent features of data; (2) incorporating the user/item metadata into the model to tackle sparse ratings; and (3) undertaking stochastic variational inference to efficiently handle massive data. mGDMF is conjugate, dynamic and scalable. Experiments show that mGDMF significantly (both effectively and efficiently) outperforms the state-of-the-art static and dynamic models on large, sparse and dynamic data.
Paper : 2816 This paper presents a  scalable model which integrates dynamical Poisson factorization with metadata information. Here metadata information contains non-dynamic users and items attributes.  Authors design an algorithm which has two stages. One is to incorporate the metadata information to static latent random variables for each user and item. Second part is to learn dynamical nature of the observations via latent single order markov chain variable of gamma-gamma structure. The markov chain is modeled by absorbing the dynamical nature of data via its rate parameter of gamma distribution. Stochastic Variational inference has been used to infer approximating distribution’s parameters.  Algorithm has shown having learn-ability and predictive power over large datasets, also it has shown how it would enhance the recommendation of more sparsely rated items to users compared to baseline algorithms. Main contribution of paper is to model how to incorporate effect of users and items attributes which have nature of categorical distribution as an weight which is sampled from gamma distribution. Experiments has been done on three different datasets, results show advantage of current algorithm than baselines, there are some discussion about results and clarity which will be detailed in following.    Quality: Paper has clear approach to present the model and dependencies between latent variables and observation data. Most of derivation have been verified.  Just there are few minor points:  -in line 103 and 107, it has not been discussed how to determine the value for f_{u,m} or f_{i,n}, it is guessed to be that f_{u,m} and f_{i,n} are binary values that indicates presence or lack of presence of  an attribute for an item or user in certain datasets, but has not been specified.   -in results section, in order to verify effect of metadata incorporation, it is useful to plot the GDMF recommendation beside mGDMF and DCPF in Figure 2. This way we can see how much recommending sparsely rated item is resulted by incorporation of metadata attributes to the model.   Clarity: Paper has been motivated well and general concepts and background have been explained well, just a few minor points: - Most of “deviation”s in supplementary material section should be changed to “derivation”                      - on line 110 to 114, it is discussed about necessity of introducing auxiliary variable, and reason mentioned to be not attaining positive correlation between latent variables in Markov chain. It will be useful for reader if it is explained why such thing happens and how auxiliary variable can solve this problem.   - adding graphical diagram (plate model) to the paper makes the article an easier read  Originality: As mentioned in summary the main contribution of this paper is to model incorporation of users and items attributes which have categorical  nature as an importance weight which is sampled from gamma distribution and it will affect the item or user latent variable.   Authors have cited many of recent works, and  have done an in depth literature review also could distinguish their contribution to what has been done so far clearly.    Significance:  As discussed, in Quality section, it is useful in figure 2 to have another subplot for GDMF recommendation to verify the significance of mGDMF.  Also I would like to mention that dynamical Poisson factorization model in [31] is close to this dynamical Poisson factorization system and code is available on github that could have been used as one of baseline methods.