[目的]聚焦图神经网络技术,对会话推荐方法进行述评.[文献范围]分别以"Session-Based Recommendation""Graph Neural Network""会话推荐""图神经网络"为检索词,在Web of Science、中国知网等数据库中筛选出82篇国内外文献.[方法]从框架、评价和趋势三个视角,对基于图神经网络的会话推荐方法进行归纳与对比,总结现有评价资源,讨论未来研究趋势.[结果]图神经网络是当前实现会话推荐系统的主流技术,基于图神经网络的会话推荐方法主要围绕"会话图构建"、"会话图学习"和"会话兴趣表示"三个核心问题展开.[局限]本文仅评述主流研究,并未将所有研究逐一列出.未从可解释性、鲁棒性、多样性和公平性等方面深入研究.[结论]图神经网络是会话推荐系统的主流实现技术,未来可结合会话推荐的特定场景,通过发展图神经网络技术进一步改进现有研究不足.
A Survey on Session-Based Recommendation Methods with Graph Neural Network
[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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