A novel context-aware recommender system based on a deep sequential learning approach (CReS)

Abstract

With an increase in online longitudinal users’ interactions, capturing users’ precise preferences and giving accurate recommendations have become an urgent need for all businesses. Existing sequence-aware methods generally exploit a static low-rank vector for acquiring the overall sequential features, and incorporate context information as auxiliary input. As a result, they have a restricted modeling ability for extracting multi-grained sequential behaviors over contextual information. In other words, they poorly capture the hierarchical relationship between context relations and item relations that currently influence users’ preferences in a unified framework. Besides, they usually utilize users’ short-term preferences with either static or irrelevant long-term representation for the prediction. To tackle the above issues, in this paper, we propose a novel Context-aware Recommender System Based on a Deep Sequential Learning Approach (CReS) to capture users’ dynamic preferences by modeling the hierarchical relationships between contexts and items in a particular session, and for combining users’ short-term sessions with the relevant long-term representations. Specifically, within a certain session, we design a hierarchical attention network between the identified context relations and items relations, namely CReSession. Therefore, with CReSession, we could provide a suitable session representation that mimics the hierarchical user interests on multiple granularities of contextual types and its corresponding items. We then introduce a neural attentive bi-directional GRU network to distill only those highly related to the recent short-term session. Finally, the relevant long-term representations and the short-term session are combined with the sequential residual connection to form the final user representation in a unified manner. With extensive experiments on two real-world datasets, CReS not only achieves significant improvement over the state-of-the-art methods in terms of pre-defined metrics, but also provides an interpretable result regarding why we recommend these items to users.