A Survey on Session-Based Recommendation Methods with Graph Neural Network
Zhang Xiongtao1,Zhu Na2(),Guo Yuhui3
1School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China 2School of National Governance, Southwest University, Chongqing 400715, China 3School of Information, Renmin University of China, Beijing 100872, China
[Objective] This paper focuses on graph neural network technology,reviewing session-based recommendation methods to provide a reference for future research. [Coverage] We took “session-based recommendation” and “graph neural network” as search terms, and 82 domestic and foreign literatures were screened from databases such as “Web of Science” and “China National Knowledge Infrastructure”. [Methods] From the perspective of framework, evaluation and trend, this paper generalises and compares session-based recommendation methods based on graph neural networks, summarises the existing evaluation resources and discusses the future research trend. [Results] Graph Neural Network is the mainstream technology for implementing session-based recommender systems. The studies on session-based recommendation methods with graph neural network mainly focus on three core problems, session graph construction, session graph learning and session interest representation. [Limitations] Session-based recommendation methods with graph neural networks are constantly emerging, and the research reviewed is only the typical research and not all studies are listed. Future research can be deepened in terms of interpretability, robustness, diversity and fairness. [Conclusions] Graph Neural Network is the mainstream technology for session-based recommender systems. Existing research has conducted preliminary exploration from various aspects and provided sufficient evaluation resources. Future research should combine the characteristics of session recommendation scenarios and develop graph neural network technology to further improve the existing research deficiencies.
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