Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

  • Authors:
  • Fernando Díez;J. Enrique Chavarriaga;Pedro G. Campos;Alejandro Bellogín

  • Affiliations:
  • Universidad Autónoma de Madrid, Madrid, Spain;Universidad Autónoma de Madrid, Madrid, Spain;Universidad Autónoma de Madrid, Madrid, Spain and Universidad del Bío-Bío, Concepción, Chile;Universidad Autónoma de Madrid, Madrid, Spain

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

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Abstract

In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.