Motif-aware Sequential Recommendation

Abstract

Sequential recommendation is intended to model the dynamic behavior regularity through users' behavior sequences. Recently, various deep learning techniques are applied to model the relation of items in the sequences. Despite their effectiveness, we argue that the aforementioned methods only consider the macro-structure of the behavior sequence, but neglect the micro-structure in the sequence which is important to sequential recommendation. To address the above limitation, we propose a novel model called Motif-aware Sequential Recommendation (MoSeR), which captures the motifs hidden in behavior sequences to model the micro-structure features. MoSeR extracts the motifs that contain both the last behavior and the target item. These motifs reflect the topological relations among local items in the form of directed graphs. Thus our method can make a more accurate prediction with the awareness of the inherent patterns between local items. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art sequential recommendation models.