Collaborative Variational Bandwidth Auto-encoder for Recommender Systems

Collaborative filtering has been widely adopted by modern recommender systems to discover user preferences based on their past behaviors. However, the observed interactions for different users are usually unbalanced, which leads to high uncertainty in the collaborative embeddings of users with sparse ratings, thereby severely degenerating the recommendation performance. Consequently, more efforts have been dedicated to the hybrid recommendation strategy where user/item features are utilized as auxiliary information to address the sparsity problem. However, since these features contain rich multimodal patterns and most of them are irrelevant to the recommendation purpose, excessive reliance on these features will make the model difficult to generalize. To address the above two challenges, we propose a VBAE for recommendation. VBAE models both the collaborative and the user feature embeddings as Gaussian random variables inferred via deep neural networks to capture non-linear similarities between users based on their ratings and features. Furthermore, VBAE establishes an information regulation mechanism by introducing a user-dependent channel variable where the bandwidth is determined by the information already contained in the observed ratings to dynamically control the amount of information allowed to be accessed from the corresponding user features. The user-dependent channel variable alleviates the uncertainty problem when the ratings are sparse while avoids unnecessary dependence of the model on noisy user features simultaneously. Codes and datasets are released at this https URL.