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
Recurrent Neural Network and Attention Enhanced Gated Graph Neural Network for Session-Based Recommendation
李伟玥 1朱志国 1董昊 1姜盼 1高明1
作者信息
- 1. 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
- 折叠
摘要
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.
Abstract
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.
关键词
会话推荐系统/图神经网络/循环神经网络/注意力机制Key words
Session-Based Recommendation System/Graph Neural Network/Recurrent Neural Net-work/Attention Mechanism引用本文复制引用
基金项目
国家自然科学基金面上项目(72172025)
国家自然科学基金面上项目(72101051)
国家自然科学基金面上项目(71802023)
教育部人文社会科学研究规划基金(21YJAZH130)
辽宁省教育厅基本科研项目(LJKMZ20221606)
出版年
2024
NETL
NSTL
万方数据