Exploring multi-dimensional interests for session-based recommendation

Abstract

Session-based recommendation (SBR) aims to recommend the next clicked item to users by mining the user’s interaction sequences in the current session. It has received widespread attention recently due to its excellent privacy protection capabilities. However, existing SBR methods have the following limitations: (1) there exists noisy information in session sequences; (2) it is a challenge to simultaneously model both the long-term stable and dynamic changing interests of users; (3) the internal relationships between different interest representations are often neglected. To address the above issues, we propose an Exploring Multi-Dimensional Interests for session-based recommendation model, termed EMDI, which attempts to predict more accurate and complete user intentions from multiple dimensions of user interests. Specifically, the EMDI contains the following three aspects: (1) the interest enhancement module aims to filter noise and enhance the interest expressions in the user’s behavior sequences, providing high-quality item embeddings; (2) the interest mining module separately mines users’ multi-dimensional interests, including static interests, local dynamic interests, and global dynamic interests, to capture users’ tendencies in different dimensions of interest; (3) the interest fusion module is designed to dynamically aggregate users’ interest representations from different dimensions through a novel multi-layer gated fusion network so that the implicit association between interest representations can be captured. Extensive experimental results show that the EMDI performs significantly better than other state-of-the-art methods.