Factorization models for context-/time-aware movie recommendations

  • Authors:
  • Zeno Gantner;Steffen Rendle;Lars Schmidt-Thieme

  • Affiliations:
  • University of Hildesheim, Hildesheim, Germany;University of Hildesheim, Hildesheim, Germany;University of Hildesheim, Hildesheim, Germany

  • Venue:
  • Proceedings of the Workshop on Context-Aware Movie Recommendation
  • Year:
  • 2010

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Abstract

In the scope of the Challenge on Context-aware Movie Recommendation (CAMRa2010), context can mean temporal context (Task 1), mood (Task 2), or social context (Task 3). We suggest to use Pairwise Interaction Tensor Factorization (PITF), a method used for personalized tag recommendation, to model the temporal (week) context in Task 1 of the challenge. We also present an extended version of PITF that handles the week context in a smoother way. In the experiments, we compare PITF against different item recommendation baselines that do not take context into account, and a non-personalized context-aware baseline.