Exploiting intra- and inter-session dependencies for session-based recommendations

Session-based recommender systems (SBRSs) aim at predicting the next item via learning the dynamic and short-term preferences of users. Most of the existing SBRSs usually make predictions based on the intra-session dependencies embedded in session information only, ignoring more complex inter-session dependencies and other available side information (e.g., item attributes, users), which in turn greatly limits the improvement of the recommendation accuracy. In order to effectively extract both intra- and inter-session dependencies from not only the session information but also the side information, to further improve the accuracy of next-item recommendations, we propose a novel hypergraph learning (HL) framework. The HL framework mainly contains three modules, i.e., a hypergraph construction module, a hypergraph learning module, and a next-item prediction module. The hypergraph construction module constructs a hypergraph to connect the users, items and item attributes together in a unified way. Then, the hypergraph learning module learns the informative latent representation for each item by extracting both intra- and inter-session dependencies embedded in the constructed hypergraph. Also, a latent representation for each user is learned. After that, the learned latent representations are fed into the next-item prediction module for next-item recommendations. We conduct extensive experiments on two real-world datasets. The experimental results show that our HL framework outperforms the state-of-the-art approaches.