GIMIRec: Global Interaction-aware Multi-Interest framework for sequential Recommendation

Sequential recommendation based on multi-interest framework is to model the user’s recent interaction sequence into multiple different interest vectors instead of a single low-dimensional vector, so as to fully represent the diversity of user interests. However, most of the existing models only intercept each user’s recent interaction behaviors as training data, without exploring the user’s historical interaction data and the co-occurrence relationship between items in the entire dataset. To address the problem, this paper proposes a Global Interaction-aware Multi-Interest framework for sequential Recommendation (GIMIRec). Specifically, a global context extraction module is firstly proposed to calculate a weighted co-occurrence matrix from the historical interaction sequences of all users to obtain the global context embedding of each item. Secondly, the time interval of each item pair in the recent interaction sequence of each user is captured and combined with the global context embeddings to get the personalized embeddings. Finally, a self-attention-based multi-interest framework is applied to learn the diverse interests of users for sequential recommendation. Extensive experiments on three real-world datasets show that GIMIRec significantly outperforms state-of-the-art methods.