Multi-behavior Recommendation with Action Pattern-aware Networks

Predicting the next interaction based on an anony-mous short-term sequence is challenging in session-based rec-ommendation. Multi-behavior recommendations aim to capture effective user intention representations by considering session sequences with several action types. However, recent multi-behavior-based approaches for session-based recommendation still have limitations. First, the final prediction for most existing approaches is limited to the next item, ignoring which action the predicted item is associated with. Second, existing approaches consider item sequences and action sequences individually and thus do not explicitly model the action dependencies for a single item. In this paper, we propose a novel session-based recommendation algorithm with Action Pattern-Aware Networks (APANet), which could incorporate both historical item sequences and reformulated item-wise action patterns into the modeling process, and predict the next-best interaction (i.e., next-best item and its associated action) given a short-term anonymous multi-behavior sequence. Comprehensive experiments on three public benchmark datasets demonstrate the effectiveness of the proposed APANet.