Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

Session-based recommender systems are increasingly applied to next-item recommendations. However, existing approaches encode the session information of each user independently and do not consider the interrelationship between users. This work is based on the intuition that dynamic groups of like-minded users exist over time. By considering the impact of latent user groups, we can learn a user's preference in a better way. To this end, we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups. Specifically, we utilize two network units to learn users' long and short-term sessions, respectively. Meanwhile, we employ two additional units to determine the affiliation of users with specific latent groups, followed by an aggregation of these latent group representations. Finally, user preference representations are shaped comprehensively by considering all these four aspects, based on an attention mechanism. Moreover, to avoid setting the number of groups manually, we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically. Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall, mean average precision (mAP), and area under curve (AUC) metrics.