An Attribute-Driven Mirror Graph Network for Session-based Recommendation

作者：Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, Aixin Sun · 发表于：Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 年份：2022 · DOI：10.1145/3477495.3531935 · 被引用次数：50 · 研究领域：Recommender Systems and Techniques、Advanced Bandit Algorithms Research、Advanced Graph Neural Networks

Session-based recommendation (SBR) aims to predict a user's next clicked item based on an anonymous yet short interaction sequence. Previous SBR models, which rely only on the limited short-term transition information without utilizing extra valuable knowledge, have suffered a lot from the problem of data sparsity. This paper proposes a novel mirror graph enhanced neural model for session-based recommendation (MGS), to exploit item attribute information over item embeddings for more accurate preference estimation.