图神经网络会话推荐系统综述

doi:10.3778/j.issn.1002-8331.2207-0397

摘要/Abstract

摘要： 会话推荐立足于目标用户的当前会话，根据项目类别、跨会话的上下文信息、多种用户行为等辅助信息学习项目间的依赖关系，从而捕捉用户的长短期偏好进行个性化推荐。近年来，流行的深度学习系列方法已经成为会话型推荐系统这个研究热点的前沿方法，尤其是图神经网络的引入，使会话推荐系统的性能得到了进一步提升。鉴于此，该综述从问题定义与会话推荐因素出发，从构图方面进行分析；将相关工作分为基于图卷积网络、门控图神经网络、图注意力网络和其他图神经网络架构的会话推荐系统，并进行归纳与对比；对各工作实验部分中的损失函数类别、所选用的数据集和模型性能评估指标三方面进行深入分析。重点从算法原理和性能分析两方面对各模型框架进行评估和梳理，旨在对近五年基于图神经网络的会话推荐系统相关工作进行评述、总结与展望。

关键词: 图神经网络, 会话推荐, 图卷积, 门控机制, 注意力

Abstract: Based on the current session of the target user, session recommenders learn the dependency relationship among items according to the auxiliary information such as item category, cross-session context, and user’s multi behaviors, to capture the long-term and short-term preferences of users for personalized recommendations. In recent years, a series of popular algorithms based on deep learning have become the forefront methods for session recommendation systems. The introduction of graph neural networks further improves the session recommendation system’s performance. Given this, the review starts with question definition, session recommendation factors, and composition analysis. Then the related works are divided into session recommendation systems based on graph convolution networks, gated graph neural networks, graph attention networks, and other architectures. After that, these works are summarized and compared. Finally, the categories of loss functions, selected data sets, and model performance evaluation indexes are deeply analyzed. The paper mainly evaluates and sorts out each model framework from algorithm principle and performance analysis, aiming at reviewing, summarizing, and looking into the related work of session recommendation systems based on graph neural networks in the last five years.

Key words: graph neural networks, session recommendation, graph convolution, gated mechanism, attention

朱志国, 李伟玥, 姜盼, 周沛瑶. 图神经网络会话推荐系统综述[J]. 计算机工程与应用, 2023, 59(5): 55-69.

ZHU Zhiguo, LI Weiyue, JIANG Pan, ZHOU Peiyao. Survey of Graph Neural Networks in Session Recommender Systems[J]. Computer Engineering and Applications, 2023, 59(5): 55-69.

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