Topological Influence-Aware Recommendation on Social Networks
Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6325654 |
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| _version_ | 1849401607819624448 |
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| author | Zhaoyi Li Fei Xiong Ximeng Wang Hongshu Chen Xi Xiong |
| author_facet | Zhaoyi Li Fei Xiong Ximeng Wang Hongshu Chen Xi Xiong |
| author_sort | Zhaoyi Li |
| collection | DOAJ |
| description | Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence. |
| format | Article |
| id | doaj-art-882dc27af9f64cdf9bced761a56df021 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-882dc27af9f64cdf9bced761a56df0212025-08-20T03:37:44ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/63256546325654Topological Influence-Aware Recommendation on Social NetworksZhaoyi Li0Fei Xiong1Ximeng Wang2Hongshu Chen3Xi Xiong4School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Management and Economics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, ChinaUsers in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.http://dx.doi.org/10.1155/2019/6325654 |
| spellingShingle | Zhaoyi Li Fei Xiong Ximeng Wang Hongshu Chen Xi Xiong Topological Influence-Aware Recommendation on Social Networks Complexity |
| title | Topological Influence-Aware Recommendation on Social Networks |
| title_full | Topological Influence-Aware Recommendation on Social Networks |
| title_fullStr | Topological Influence-Aware Recommendation on Social Networks |
| title_full_unstemmed | Topological Influence-Aware Recommendation on Social Networks |
| title_short | Topological Influence-Aware Recommendation on Social Networks |
| title_sort | topological influence aware recommendation on social networks |
| url | http://dx.doi.org/10.1155/2019/6325654 |
| work_keys_str_mv | AT zhaoyili topologicalinfluenceawarerecommendationonsocialnetworks AT feixiong topologicalinfluenceawarerecommendationonsocialnetworks AT ximengwang topologicalinfluenceawarerecommendationonsocialnetworks AT hongshuchen topologicalinfluenceawarerecommendationonsocialnetworks AT xixiong topologicalinfluenceawarerecommendationonsocialnetworks |