| 引用本文: | 洪明利,王靖,贾彩燕.多通道图注意力解耦社交推荐方法.[J].国防科技大学学报,2022,44(3):1-9.[点击复制] |
| HONG Mingli,WANG Jing,JIA Caiyan.Multi-channel graph attention network with disentangling capability for social recommendation[J].Journal of National University of Defense Technology,2022,44(3):1-9[点击复制] |
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| 多通道图注意力解耦社交推荐方法 |
| 洪明利1,2,王靖1,2,贾彩燕1,2 |
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(1. 北京交通大学 计算机与信息技术学院, 北京 100044;2. 北京交通大学 交通数据分析与挖掘北京市重点实验室, 北京 100044)
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| 摘要: |
| 提出具有解耦能力的多通道图注意力社交推荐模型,该模型主要包括深度聚类模块、多通道图注意力聚合模块和评分预测模块。其中,深度聚类模块用于对用户和项目进行分组,并利用聚类结果将用户社交图和用户项目图拆分成多个用户社交子图及用户项目子图,以学习用户兴趣分组及用户对不同类项目的兴趣；多通道图注意力聚合模块学习不同子图对预测结果的注意力；评分预测模块将学习到的用户表示向量和项目表示向量输入多层感知机进行评分预测。在多个真实数据集上的实验结果表明:提出的方法优于其他社交推荐算法。与最新的用于社交推荐的图神经网络方法相比,在Ciao和Epinions数据集上,均方根误差分别降低了2.26%和2.07%,平均绝对误差分别降低了2.58%和3.06%。 |
| 关键词: 推荐系统 社交网络 图神经网络 注意力网络 深度聚类 |
| DOI：10.11887/j.cn.202203001 |
| 投稿日期：2021-09-13 |
| 基金项目:国家自然科学基金资助项目(61876016,61632004)；中央高校基本科研业务费专项资金资助项目(2019JBZll0) |
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| Multi-channel graph attention network with disentangling capability for social recommendation |
| HONG Mingli1,2, WANG Jing1,2, JIA Caiyan1,2 |
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(1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;2. Beijing Key Laboratory of Traffic Data Analysis and Mining， Beijing Jiaotong University, Beijing 100044, China)
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| Abstract: |
| A Multi-channel graph attention network social recommendation model with disentangling capability was proposed. This model mainly included three modules:the deep clustering module, the aggregation module based on multi-channel graph attention network, and the rating prediction module. Among them, the deep clustering module was used to group users and items. The clustering results can be used to split user-user social graph and user-item interaction graph into multiple subgraph to learn user interest groups and users′ interests in different types of items. The aggregation module learns the attention of different sub-graphs to the prediction results. The rating prediction module input the learned user representation vector and item representation vector into the multilayer perceptron for rating prediction. Extensive experiments on multiple real-world datasets demonstrate that the proposed method is better than other social recommendation algorithms. Specifically, compared with the latest graph neural networks method for social recommendation, the root mean square error is respectively reduced by 2.26% and 2.07% on the Ciao and Epinions datasets, and the mean absolute error is respectively reduced by 2.58% and 3.06%. |
| Keywords: recommendation system social network graph neural network attention network deep clustering |
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