Abstract
Predicting a user’s next click by utilizing a short anonymous behavior is a challenging problem in the real-life session-based recommendation (SBR). Most existing methods usually learn the users’ preference from current session. However, they seldom consider global context information or knowledge graph and failed to distill high-quality item from similar sessions. In this work, we combine Global Context information with Knowledge Graph, and develop a new framework to enhance session-based recommendation (GCKG). Technically, we model a global knowledge graph, exploiting a knowledge aware attention mechanism for better learning item embeddings. Then, we leverage an attention network and a gated recurrent unit to learn session representations. Furthermore, session representations are augmented simultaneously through constructing a similar session referral circle. Comprehensive experiments demonstrate that GCKG significantly outperforms the state-of-the-art methods of existing SBR.
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Acknowledgment
This work is supported by Gansu Natural Science Foundation Project (21JR7RA114), the National Natural Science Foundation of China (61762078, 61363058, U1811264, 61966004), Northwest Normal University Young Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2) and Northwest Normal University Postgraduate Research Funding Project (2021KYZZ02107).
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Zhang, X., Ma, H., Gao, Z., Li, Z., Chang, L. (2022). Enhancing Session-Based Recommendation with Global Context Information and Knowledge Graph. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_20
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DOI: https://doi.org/10.1007/978-3-031-00126-0_20
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