Optimal Ranking for Video Recommendation

  • Zeno Gantner
  • Christoph Freudenthaler
  • Steffen Rendle
  • Lars Schmidt-Thieme
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 40)

Abstract

Item recommendation from implicit feedback is the task of predicting a personalized ranking on a set of items (e.g. movies, products, video clips) from user feedback like clicks or product purchases. We evaluate the performance of a matrix factorization model optimized for the new ranking criterion BPR-Opt on data from a BBC video web application. The experimental results indicate that our approach is superior to state-of-the-art models not directly optimized for personalized ranking.

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2010

Authors and Affiliations

  • Zeno Gantner
    • 1
  • Christoph Freudenthaler
    • 1
  • Steffen Rendle
    • 1
  • Lars Schmidt-Thieme
    • 1
  1. 1.Machine Learning LabUniversity of HildesheimHildesheimGermany

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