  Recent years have witnessed rapid developments on collaborative filtering
techniques for improving the performance of recommender systems due to the
growing need of companies to help users discover new and relevant items.
However, the majority of existing literature focuses on delivering items which
match the user model learned from users' past preferences. A good
recommendation model is expected to recommend items that are known to enjoy and
items that are novel to try. In this work, we introduce an
exploitation-exploration motivated variational auto-encoder (XploVAE) to
collaborative filtering. To facilitate personalized recommendations, we
construct user-specific subgraphs, which contain the first-order proximity
capturing observed user-item interactions for exploitation and the high-order
proximity for exploration. A hierarchical latent space model is utilized to
learn the personalized item embedding for a given user, along with the
population distribution of all user subgraphs. Finally, experimental results on
various real-world datasets clearly demonstrate the effectiveness of our
proposed model on leveraging the exploitation and exploration recommendation
tasks.
