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Jun Hu, Bing Wang, Yu Liu, De-Yi Li. Personalized Tag Recommendation Using Social Influence[J]. Journal of Computer Science and Technology, 2012, 27(3): 527-540. DOI: 10.1007/s11390-012-1241-0
Citation: Jun Hu, Bing Wang, Yu Liu, De-Yi Li. Personalized Tag Recommendation Using Social Influence[J]. Journal of Computer Science and Technology, 2012, 27(3): 527-540. DOI: 10.1007/s11390-012-1241-0

Personalized Tag Recommendation Using Social Influence

Funds: 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.
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  • Author Bio:

    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.

  • Received Date: August 30, 2011
  • Revised Date: January 18, 2012
  • Published Date: May 04, 2012
  • 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.
  • [1]
    Sigurbjörnsson B, van Zwol R. Flickr tag recommendationbased on collective knowledge. In Proc. the 17th Inter-national Conference on World Wide Web, Beijing, China,Apr. 21-25, 2008, pp.327-336.
    [2]
    Smeulders A, Worring M, Santini S, Gupta A, Jain R.Content-based image retrieval at the end of the early years.IEEE Transactions Pattern Analysis Machine Intelligence,2000, 22(12): 1349-1380.
    [3]
    Ames M, Naaman M. Why we tag: Motivations for annota-tion in mobile and online media. In Proc. the 2007 SIGCHIConference on Human Factors in Computing Systems, SanJose, USA, Apr. 28-May 3, 2007, pp.971-980.
    [4]
    Gemmell J, Ramezani M, Schimoler T, Christiansen L,Mobasher B. The impact of ambiguity and redundancy ontag recommendation in folksonomies. In Proc. the 3rd ACMConference on Recommender Systems, New York City, USA,Oct. 23-25, 2009, pp.45-52.
    [5]
    Vojnovic M, Cruise J, Gunawardena D, Marbach P. Rankingand suggesting popular items. IEEE Transactions on Know-ledge and Data Engineering, 2009, 21(8): 1133-1146.
    [6]
    Guan Z Y, Bu J J, Mei Q Z, Chen C, Wang C. Personalizedtag recommendation using graph-based ranking on multi-typeinterrelated objects. In Proc. the 32nd Annual InternationalACM SIGIR Conference on Research and Development in In-formation Retrieval, Boston, USA, July 19-23, 2009, pp.540-547.
    [7]
    Yin D W, Xue Z Z, Hong L J, Davison B D. A probabilisticmodel for personalized tag prediction. In Proc. the 16th ACM SIGKDD International Conference on Knowledge Discoveryand Data Ming, Washington, USA, Jul. 25-28, 2010, pp.959-968.
    [8]
    Lipczak M, Milios E. Learning in efficient tag recommenda-tion. In Proc. the 4th ACM Conference on RecommenderSystems, Barcelona, Spain, Sept. 26-30, 2010, pp.167-174.
    [9]
    Koren Y. Factorization meets the neighborhood: A multi-faceted collaborative filtering model. In Proc. the 14th ACMSIGKDD International Conference on Knowledge Discoveryand Data Mining, Las Vegas, USA, Aug. 24-27, 2008, pp.426-434.
    [10]
    J?aschke R, Marinho L, Hotho A, Schmidt-Thieme L, StummeG. Tag recommendations in folksonomies. In Proc. the 11thEuropean Conference on Principles and Practice of Know-ledge Discovery in Databases, Warsaw, Poland, September17-21, 2007, pp.506-514.
    [11]
    Symeonidis P, Nanopoulos A, Manolopoulos Y. Tag recom-mendations based on tensor dimensionality reduction. InProc. the 2nd ACM Conference on Recommender Systems,Lausanne, Switzerland, October 23-25, 2008, pp.43-50.
    [12]
    Rendle S, Marinho B L, Nanopoulos A, Schmidt-Thieme L.Learning optimal ranking with tensor factorization for tagrecommendation. In Proc. the 15th ACM SIGKDD Interna-tional Conference on Knowledge Discovery and Data Mining,Paris, France, Jun. 28-Jul. 1, 2009, pp.727-736.
    [13]
    Tatu M, Srikanth M, D'Silva T. Tag recommendations us-ing bookmark content. In Proc. the 2008 ECML/PKDDDiscovery Challenge Workshop, Antwerp, Belgium, Sep. 15-19, 2008, pp.96-107.
    [14]
    Koren Y. Collaborative filtering with temporal dynamics. InProc. the 15th ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining, Paris, France,Jun. 28-Jul. 1, 2009, pp.447-456.
    [15]
    Garg N, Weber I. Personalized, interactive tag recommen-dation for flickr. In Proc. the 2nd ACM Conference onRecommender Systems, Lausanne, Switzerland, October 23-25, 2008, pp.67-74.
    [16]
    Rae A, Sigurbjornsson B, van Zwol R. Improving tag reco-mmendation using social networks. In Proc. the 9th Inter-national Conference on Adaptivity, Personalization and Fu-sion of Heterogeneous Information, Paris, France, Apr. 28-30,2010, pp.92-99.
    [17]
    Ma H, King I, Lyu M R. Learning to recommend with explicitand implicit social relations. ACM Transactions on Intelli-gent Systems and Technology, 2011, 2(3): Article No. 29.
    [18]
    Lipczak M, Milios E. The impact of resource title on tagsin collaborative tagging systems. In Proc. the 21st ACMConference on Hypertext and Hypermedia, Toronto, Canada,Jun. 13-16, 2010, pp.179-188.
    [19]
    Mislove A, Koppula H S, Gummadi K P, Drusche P, Bhat-tacharjee B. Growth of the flickr social network. In Proc.the 1st Workshop on Online Social Networks, Seattle, USA,Aug. 18, 2008, pp.25-30.
    [20]
    Cha M Y, Mislove A, Gummadi K P. A measurement-drivenanalysis of information propagation in the flickr social net-work. In Proc. the 18th International Conference on WorldWide Web, Madrid, Spain, Apr. 20-24, 2009, pp.721-730.
    [21]
    Li D Y, Du Y. Artifical Intelligence With Uncertainty. BocaRaton, Florida: Chapman and Hall/CRC Press, 2008.
    [22]
    Wu L, Yang L J, Yu N H, Hua X S. Learning to tag. InProc. the 18th International Conference on World WideWeb, Madrid, Spain, Apr. 20-24, 2009, pp.361-370.
    [23]
    Liu N N, Yang Q. Eigenrank: A ranking-oriented approachto collaborative filtering. In Proc. the 31st Annual ACMSIGIR International Conference on Research and Develop-ment in Information Retrieval, Singapore, Singapore, Jul. 20-24, 2008, pp.83-90.
    [24]
    Shen D, Sun J T, Yang Q, Chen Z. A comparison of implicitand explicit links for web page classification. In Proc. the15th International Conference on World Wide Web, Edin-burgh, Scotland, May 23-26, 2006, pp.643-650.
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