Hierarchical Context enabled Recurrent Neural Network for Recommendation
This addresses the challenge of capturing evolving user interests in recommendation systems, though it appears incremental as it builds on existing RNN methods.
The authors tackled the problem of modeling long-term dependencies and interest drifts in sequential recommendations by proposing HCRNN, which uses hierarchical contexts and a bi-channel attention structure, achieving the best performance on datasets like CiteULike, MovieLens, and LastFM.
A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our \textit{interest drift assumption}. As we suggest a new RNN structure, we support HCRNN with a complementary \textit{bi-channel attention} structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.