A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks
Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recom...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9071624 |
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| _version_ | 1832560026716209152 |
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| author | Rongmei Zhao Xi Xiong Xia Zu Shenggen Ju Zhongzhi Li Binyong Li |
| author_facet | Rongmei Zhao Xi Xiong Xia Zu Shenggen Ju Zhongzhi Li Binyong Li |
| author_sort | Rongmei Zhao |
| collection | DOAJ |
| description | Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-3dfb7948852e4a459a4b14a4df0d757a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3dfb7948852e4a459a4b14a4df0d757a2025-02-03T01:28:34ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/90716249071624A Hierarchical Attention Recommender System Based on Cross-Domain Social NetworksRongmei Zhao0Xi Xiong1Xia Zu2Shenggen Ju3Zhongzhi Li4Binyong Li5School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaSchool of Management, Chengdu University of Information Technology, Chengdu 610103, ChinaSchool of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, ChinaSearch engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.http://dx.doi.org/10.1155/2020/9071624 |
| spellingShingle | Rongmei Zhao Xi Xiong Xia Zu Shenggen Ju Zhongzhi Li Binyong Li A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks Complexity |
| title | A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks |
| title_full | A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks |
| title_fullStr | A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks |
| title_full_unstemmed | A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks |
| title_short | A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks |
| title_sort | hierarchical attention recommender system based on cross domain social networks |
| url | http://dx.doi.org/10.1155/2020/9071624 |
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