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23 May 2023 Integrating users’ global and local interest for session-based recommendation
Yiqiu Fang, Ning Wu, Junwei Ge
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Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126040V (2023) https://doi.org/10.1117/12.2674571
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China
Abstract
Conventional recommendation systems mostly recommend based on users’ global stable interest, while SR focuses on learning local active interest from the current session. Relevant research points out that integrating users’ global and local interest can improve model accuracy. However, most SR models are deficient in interest mining and fusion, and do not fully utilize the time interval content between sessions, result in inaccurate recommendations. In the paper, we propose a new session-based recommendation method called Global and Local Interest Integration Model (GLI-GNN). GLI-GNN introduces the concept of session time and attention mechanism to obtain global preferences from users’ previous sessions; it uses GNN to obtain local interest from current sessions; it uses attention mechanism to integrate both. The results indicate that GLI-GNN realizes new optimal performance in next-shot interactive recommendation. Experimental results show that GLI-GNN is superior to the existing SR models.
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Yiqiu Fang, Ning Wu, and Junwei Ge "Integrating users’ global and local interest for session-based recommendation", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126040V (23 May 2023); https://doi.org/10.1117/12.2674571
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KEYWORDS
Neural networks
Data modeling
Systems modeling
Design and modelling
Mathematical optimization
Matrices
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