An Attribute-Driven Mirror Graph Network for Session-based Recommendation
Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, Aixin Sun
2022Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval49 citationsDOI
Abstract
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.
Topics & Concepts
Computer scienceSession (web analytics)ExploitGraphRecommender systemArtificial neural networkArtificial intelligenceMachine learningPreferenceData miningInformation retrievalTheoretical computer scienceWorld Wide WebMicroeconomicsEconomicsComputer securityRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchAdvanced Graph Neural Networks