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Personalized search on Flickr based on searcher's preference prediction

Published: 28 March 2011 Publication History

Abstract

In this paper, we propose a personalized search model to assist users in obtaining interested photos on Flickr, which exploits the favorite marks of the searcher's friends to predict the searcher's preference on the returned photos. The proposed model utilizes a co-clustering method to extract latent interest dimensions from users' implicit interests, and employs a discriminative learning method to predict searcher's preference on the returned photos. Preliminary experiments demonstrate the improvement of the proposed model compared to existing one-fit-all methods and a user-based collaborative filtering method.

References

[1]
http://blog.flickr.net/en/2010/09/19/5000000000/.
[2]
Lerman, K., Plangprasopchok, A. and Wong, C., 2008. Personalizing image search results on Flickr. In AAAI workshop on Information Integration'08.
[3]
Bender, M., Crecelius, T., Kacimi, M. and Michel, S. 2008. Exploiting social relations for query expansion and result ranking. In ICDE workshop'08, pp 501--506.
[4]
Zhou, D., Councill, Isaac., Zha, H. and Lee Giles, C. 2007. Discovering temporal communities from social network documents. In ICDM'07, pp745--750.
[5]
Elisseeff, A., Weston, J. 2002. A kernel method for multi-labelled classification. In Advances in Neural Information Processing Systems 14, Cambridge, MA: MIT Press, pp 681--687.
[6]
http://www.flickr.com/photos/tags/.
[7]
Xu, S., Bao, S., Fei, B., Su, Z. and Yu, Y. 2008. Exploring folksonomy for personalized search. In SIGIR'08, pp 155--162.
[8]
Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. 2000. Analysis of recommendation algorithms for e-commerce. In EC'00, pp285--295.

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    cover image ACM Other conferences
    WWW '11: Proceedings of the 20th international conference companion on World wide web
    March 2011
    552 pages
    ISBN:9781450306379
    DOI:10.1145/1963192

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 28 March 2011

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    Author Tags

    1. discriminative learning
    2. latent interest dimension
    3. personalized search
    4. preference prediction

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    WWW '11
    WWW '11: 20th International World Wide Web Conference
    March 28 - April 1, 2011
    Hyderabad, India

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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