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Prediction of favourite photos using social, visual, and textual signals

Published: 25 October 2010 Publication History

Abstract

This paper focuses on the prediction of users' favourite photos in Flickr. We propose a multi-modal, machine learned approach that combines social, visual and textual signals into a single prediction system. Although each individual user has different motivations for calling a photo a favourite, we show that the textual, visual, and social modalities effectively capture the needs of most active Flickr users.
We use gradient-boosted decision trees (GBDT) with a mod least squares loss function for the classification of a user's favourite photos, and evaluate the performance of our classifier with respect to the individual modalities and various combinations thereof. By using a combination of the social and visual modalities the GBDT creates a highly effective classifier. The addition of textual features allows us to significantly increase recall, with a slight trade off in precision.

References

[1]
N. Garg and I. Weber. Personalized, interactive tag recommendation for flickr. In Proceedings of the 2008 ACM Conference on Recommender Systems, pages 67--74, Lausanne, Switzerland, Oct. 2008. ACM.
[2]
C.Y.Kim,J.K.Lee,Y.H.Cho,andD.H.Kim. VISCORS: a Visual-Content recommender for the mobile web. IEEE Intelligent Systems, 19(6):32--39, 2004.
[3]
K. Lerman and L. Jones. Social browsing on flickr. In Proceedings of ICWSM, Dec. 2007.
[4]
J. S. Pedro and S. Siersdorfer. Ranking and classifying attractiveness of photos in folksonomies. In WWW, Madrid, Spain, Apr. 2009.
[5]
B. Sigurbjornsson and R. van Zwol. Flickr tag recommendation based on collective knowledge. In 17th international conference on World Wide Web, pages 327--336, Beijing, China, 2008. ACM.
[6]
R. van Zwol. Flickr: Who is looking? In IEEE/WIC/ACM International Conference on Web Intelligence, pages 184--190, Washington, DC, USA, 2007. IEEE Computer Society.
[7]
J. Ye, J.-H. Chow, J. Chen, and Z. Zheng. Stochastic gradient boosted distributed decision trees. In CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management, pages 2061--2064, New York, NY, USA, 2009. ACM.
[8]
Z. Zheng, K. Chen, G. Sun, and H. Zha. A regression framework for learning ranking functions using relative relevance judgments. In SIGIR '07, pages 287--294, New York, NY, USA, 2007. ACM.

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cover image ACM Conferences
MM '10: Proceedings of the 18th ACM international conference on Multimedia
October 2010
1836 pages
ISBN:9781605589336
DOI:10.1145/1873951
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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Author Tags

  1. Flickr
  2. GBDT
  3. favourite classification
  4. social
  5. textual
  6. visual

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MM '10
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MM '10: ACM Multimedia Conference
October 25 - 29, 2010
Firenze, Italy

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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