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

Nan Wang, Shoujin Wang, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun

Abstract:

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 recommen
dation 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 con
struction 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.