›› 2012,Vol. 27 ›› Issue (3): 527-540.doi: 10.1007/s11390-012-1241-0

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Jun Hu (胡军), Bing Wang (王兵), Yu Liu (刘禹), Member, CCF, and De-Yi Li (李德毅), Fellow, CCF   

  • 收稿日期:2011-08-31 修回日期:2012-01-19 出版日期:2012-05-05 发布日期:2012-05-05

Personalized Tag Recommendation Using Social Influence

Jun Hu (胡军), Bing Wang (王兵), Yu Liu (刘禹), Member, CCF, and De-Yi Li (李德毅), Fellow, CCF   

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
  • Received:2011-08-31 Revised:2012-01-19 Online:2012-05-05 Published:2012-05-05
  • About author:Jun Hu is now a Ph.D. candidate in computer science and technology in Beihang University. He received his Master's degree in computer scie-nce from Information Engineering University, China, in 2007. His re-search interests include social net-work analysis, recommendation sys-tem, data mining methodologies and machine learning algorithms.
  • Supported by:

    This work was supported by the National Basic Research 973 Program of China under Grant No. 2007CB310803, the National Natural Science Foundation of China under Grant Nos. 61035004, 60974086, and the Project of the State Key Laboratory of Software Development Environment of China under Grant Nos. SKLSDE-2010ZX-16, SKLSDE-2011ZX-08.


Abstract: Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object, which provides a feasible solution for content-based multimedia information retrieval. In this paper, we study personalized tag recommendation in a popular online photo sharing site —— Flickr. Social relationship information of users is collected to generate an online social network. From the perspective of network topology, we propose node topological potential to characterize user's social influence. With this metric, we distinguish different social relations between users and find out those who really have influence on the target users. Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user's social network. We evaluate our method on large scale real-world data. The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks. We also analyze the further usage of our approach for the cold-start problem of tag recommendation.

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