Session-based Recommendation with Hierarchical Memory Networks

The task of session-based recommendation aims to predict users' future interests based on anonymous historical sessions. Recent works have shown that memory models, which capture user preference from previous interaction sequence with long short-term or short-term memory, can lead to encouraging results in this problem. However, most existing memory models tend to regard each item as a memory unit, which neglect n-gram features and are insufficient to learn the user's feature-level preferences. In this paper, we aim to leverage n-gram features and model users' featurelevel preferences in an explicit and effective manner. To this end, we present a memory model with multi-scale feature memory for session-based recommendation. A densely connected convolutional neural network (CNN) with short-cut path between upstream and downstream convolutional blocks is applied to build multi-scale features from item representations, and features in the same scale are combined with memory mechanism to capture users' featurelevel preferences. Furthermore, attention is used to adaptively select users' multi-scale feature-level preferences for recommendation. Extensive experiments conducted on two benchmark datasets demonstrate the effectiveness of the proposed model in comparison with competitive baselines.