Use of Implicit Graph for Recommending Relevant Videos: A Simulated Evaluation

  • David Vallet
  • Frank Hopfgartner
  • Joemon Jose
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)


In this paper, we propose a model for exploiting community based usage information for video retrieval. Implicit usage information from a pool of past users could be a valuable source to address the difficulties caused due to the semantic gap problem. We propose a graph-based implicit feedback model in which all the usage information can be represented. A number of recommendation algorithms were suggested and experimented. A simulated user evaluation is conducted on the TREC VID collection and the results are presented. Analyzing the results we found some common characteristics on the best performing algorithms, which could indicate the best way of exploiting this type of usage information.


Recommendation Algorithm Video Retrieval Interaction Sequence Simulated Evaluation Recommendation Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David Vallet
    • 1
    • 2
  • Frank Hopfgartner
    • 2
  • Joemon Jose
    • 2
  1. 1.Universidad Autónoma de MadridMadridSpain
  2. 2.University of GlasgowGlasgowUK

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