广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (3): 151-160.doi: 10.16088/j.issn.1001-6600.2021071405

• 研究论文 • 上一篇    下一篇

面向强化当前兴趣的图神经网络推荐算法研究

孔亚钰1+, 卢玉洁1+, 孙中天2, 肖敬先1, 侯昊辰1, 陈廷伟1*   

  1. 1.辽宁大学 信息学院, 辽宁 沈阳 110036;
    2.杜伦大学, 英国 杜伦 DH1 3LE
  • 收稿日期:2021-07-14 修回日期:2021-09-09 出版日期:2022-05-25 发布日期:2022-05-27
  • 通讯作者: 陈廷伟(1974—),男,内蒙古赤峰人,辽宁大学教授, 博士。E-mail: twchen@lnu.edu.cn
  • 作者简介:+ 共同第一作者,对本文做出了同等贡献
  • 基金资助:
    国家自然科学基金(61802160)

Research on Graph Neural Network Recommendation Algorithms for Reinforcing Current Interest

KONG Yayu1+, LU Yujie1+, SUN Zhongtian2, XIAO Jingxian1, HOU Haochen1, CHEN Tingwei1*   

  1. 1. College of Information, Liaoning University, Shenyang Liaoning 110036, China;
    2. Durham University, Durham, DH1 3LE, UK
  • Received:2021-07-14 Revised:2021-09-09 Online:2022-05-25 Published:2022-05-27

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摘要： 在基于会话的推荐中,与传统序列建模相比,将会话序列建模为图结构在该领域表现得更为出色。但是,现有的研究方法仅利用图结构来挖掘项目之间转换特性,以此捕获用户当前兴趣的能力有限。本文提出一种面向强化当前兴趣的图神经网络推荐算法,通过引入位置嵌入,并与图神经网络相结合,从而互补顺序感知模型和图形感知模型的优势。会话序列被建模为图结构,并取原始序列的最后一次点击,通过多头注意力机制计算其对图节点信息的注意力权重,以更加准确地获取用户当前兴趣的表示。同时,在2个真实的数据集上进行验证实验,结果表明本文提出的方法实现了所有方法的最佳性能,特别是在Diginetica数据集上,所有评价指标都提升了7%以上,MRR@10指标甚至提升了9.52%,证明本文所提方法对基于会话推荐的正确性和有效性。

关键词: 基于会话的推荐, 会话图, 图神经网络, 多头注意力机制, 位置嵌入

Abstract: Compared with traditional sequence modeling in session-based recommendation, modeling the session sequence as a graph structure performs better in this field. However, the existing research methods are limited in their ability to capture user’s current interest by only using the graph structure to mine the session characteristics between items. A current interest reinforced Graph neural network for session-based recommendation is proposed in the paper. By introducing position embedding and combining with graph neural network, the advantages of sequential perception model and graph perception model are complemented. The session sequence is modeled as a graph structure. Take the last click of the original sequence, and calculate its attention weight for graph node information through the multi-head attention mechanism, so as to obtain user’s current interest expression more accurately. Extensive experiments on two real-world datasets show that, the proposed method achieved the best performance of all methods, especially on the Diginetica data set, all evaluation indicators have increased by more than 7%, and the MRR@10 indicator has even increased by 9.52%. These results show the correctness and effectiveness of the proposed method for session-based recommendation.

Key words: session-based recommendation, session graph, graph neural network, multi-head attention, position embedding

中图分类号: 

  • TP391.3
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