首页|循环神经网络和注意力增强的门控图神经网络会话推荐模型

循环神经网络和注意力增强的门控图神经网络会话推荐模型

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  • NETL
  • NSTL
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现有大部分基于图神经网络的会话推荐系统都可较好捕捉商品在会话图中的近邻上下文关系,但少有重点关注时序关系的系统.然而,这两种关系都对电商场景下的精准推荐具有重要作用.为此,文中基于双向长短期记忆网络和门控图神经网络,提出循环神经网络和注意力增强的门控图神经网络会话推荐模型,旨在实现不同网络结构的优势互补,充分学习用户在当前会话中表现的兴趣偏好.具体地,文中模型采用并行化框架结构,分别学习电商场景下用户会话点击流中商品间的近邻上下文特征和时序关系,再分别使用注意力机制进行去噪处理,最后基于门控机制实现这两种特征间的自适应融合.在3个真实数据集上的实验表明文中模型的性能较优.文中模型代码见 https://github.com/usernameAI/RAGGNN.
Recurrent Neural Network and Attention Enhanced Gated Graph Neural Network for Session-Based Recommendation
Most of existing session-based recommender systems with graph neural networks are capable of capturing the adjacent contextual relation of products effectively in the session graph.However,few of them focus on the sequential relation.Both relations are important for precise recommendations in e-commerce scenarios.To solve the problem,a recurrent neural network and attention enhanced gated graph neural network for session-based recommender system is proposed based on bidirectional long short-term memory.The model is designed to complement the advantages of different network structures and learn the user's interest preferences expressed during the current session more fully.Specifically,a parallel framework is adopted in the proposed model to learn the neighborhood contextual features and temporal relation among products respectively within user session clickstreams in e-commerce scenarios.Attention mechanisms are applied to denoise the features.Finally,the adaptive fusion method of both features is employed based on gating mechanism.Experiments on three real-world datasets show the superiority of the proposed model.The model code in the paper is available at https://github.com/usernameAI/RAGGNN.

Session-Based Recommendation SystemGraph Neural NetworkRecurrent Neural Net-workAttention Mechanism

李伟玥、朱志国、董昊、姜盼、高明

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东北财经大学 管理科学与工程学院 大连 116025

东北财经大学辽宁省大数据管理与优化决策重点实验室大连 116025

会话推荐系统 图神经网络 循环神经网络 注意力机制

国家自然科学基金面上项目国家自然科学基金面上项目国家自然科学基金面上项目教育部人文社会科学研究规划基金辽宁省教育厅基本科研项目

72172025721010517180202321YJAZH130LJKMZ20221606

2024

10.16451/j.cnki.issn1003-6059.202403001

模式识别与人工智能
中国自动化学会,国家智能计算机研究开发中心,中国科学院合肥智能机械研究所

模式识别与人工智能

CSTPCD北大核心
影响因子：0.954
ISSN：1003-6059
年,卷(期)：2024.37(3)
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