Mining Implicit Behavioral Patterns via Attention Networks for Sequential Recommendation

Abstract

Sequential recommender systems (SRSs) seek to model users’ dynamic preferences based on their interaction sequences to suggest items they may be interested in. Existing methods suffer from two major flaws in modeling user interest representations. First, most methods only encode the absolute positions of items in an interaction sequence, but ignore the relative position information between items, impeding the modeling of temporal patterns of user behaviors. At the same time, the multi-layer stacked network structure will cause the dilution of position information. Second, historical interactions usually contain many items that are irrelevant to the user's subsequent choices, while existing work lacks approaches to explicitly distinguish them from interest-related items. In this work, we alleviate the foregoing issues by proposing a user behavioral patterns enhanced attention network (BPERec), where a novel position encoding module enables the enhancement of temporal patterns implicit in interaction sequences. Additionally, an explicit denoising attention mechanism is proposed to enhance user interest representations. Experimental results on three benchmark datasets reveal that our method outperforms state-of-the-art baseline methods.