Learning to rank videos personally using multiple clues

Songhua Xu, Hao Jiang, Francis C.M. Lau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we introduce a new learning based video content similarity model. The model leverages on multiple clues on the contents of a video and can be used to rank videos in a personalized way. The key to produce a personalized video ranking is to have a good estimate of pairwise video content similarity, which is realized through meta-learning using a radial-basis function network. Four aspects of a video are considered in deriving the video content similarity in our method. The training data to our model are acquired in the form of user judged preference relationships regarding video content similarities. With the optimized video content similarity estimation obtained by our algorithm, we can produce a personalized video ranking that matches more closely an individual user's watching interest over a collection of videos. The video ranking results generated by our prototype system are compared with the groundtruth rankings supplied by the individual users as well as rankings by the commercial video website YouTube. The results confirm the advantages of our method in generating personalized video rankings.

Original languageEnglish (US)
Title of host publicationCIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval
Pages320-327
Number of pages8
DOIs
StatePublished - 2009
EventACM International Conference on Image and Video Retrieval, CIVR 2009 - Santorini Island, Greece
Duration: Jul 8 2009Jul 10 2009

Publication series

NameCIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval

Other

OtherACM International Conference on Image and Video Retrieval, CIVR 2009
CountryGreece
CitySantorini Island
Period7/8/097/10/09

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Keywords

  • Human factors in information retrieval
  • Learning to rank videos
  • Multi-modality video similarity fusion
  • Personalized video ranking
  • User feedback
  • Video similarity estimation

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  • Cite this

    Xu, S., Jiang, H., & Lau, F. C. M. (2009). Learning to rank videos personally using multiple clues. In CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval (pp. 320-327). (CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval). https://doi.org/10.1145/1646396.1646446