Self-Supervised Learning Based on Similar Users for Sequential Recommendation

Abstract

Sequential Recommendation (SR) predicts the next interaction behavior via modeling the interaction between the user and the item over a time sequence. A series of works applied Self-Supervised Learning (SSL) in SR to obtain better user representations. Although these efforts proved effective, they only focused on the information of the user itself and ignores self-supervised signals from other users. Due to the widely observed homogeneity in recommender systems, these signals from other users are also vital for user representation. To this end, we propose a novel framework, Self-Supervised Learning based on Similar users for Sequential Recommendation (SSLSRec). We present a contrastive learning objective in SSLSRec to consider augmented views from the same user and similar users as positive samples. Moreover, we propose novel Insert and Substitute augmentation methods to construct more reasonable augmentation views for user sequences. Extensive experiments demonstrate the effectiveness of SSLSRec.