Knowledge Graph-based Session Recommendation with Session-Adaptive Propagation
Authors: 
Yu Wang, Amin Javari, Janani Balaji, Walid Shalaby, Tyler Derr, Xiquan CuiAuthors Info & Claims
Pages 264 - 273
Abstract
Session-based recommender systems (SBRSs) predict users' next interacted items based on their historical activities. While most SBRSs capture purchasing intentions locally within each session, capturing items' global information across different sessions is crucial in characterizing their general properties. Previous works capture this cross-session information by constructing graphs and incorporating neighbor information. However, this incorporation cannot vary adaptively according to the unique intention of each session, and the constructed graphs consist of only one type of user-item interaction. To address these limitations, we propose knowledge graph-based session recommendation with session-adaptive propagation. Specifically, we build a knowledge graph by connecting items with multi-typed edges to characterize various user-item interactions. Then, we adaptively aggregate items' neighbor information considering user intention within the learned session. Experimental results demonstrate that equipping our constructed knowledge graph and session-adaptive propagation enhances session recommendation backbones by 10%-20%. Moreover, we provide an industrial case study showing our proposed framework achieves 2% performance boost over an existing well-deployed model at The Home Depot e-platform.
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Index Terms
- Knowledge Graph-based Session Recommendation with Session-Adaptive Propagation
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May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
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Published: 13 May 2024
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