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Improving Cold-Start Recommendations with Social-Media Trends and Reputations

  • João Santos
  • Filipa Peleja
  • Flávio Martins
  • João Magalhães
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10584)

Abstract

In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings, it becomes difficult to relate it to other items or users. In this paper, we address the cold-start problem and propose to leverage on social-media trends and reputations to improve the recommendation of new items. The proposed framework models the long-term reputation of actors and directors, to better characterize new movies. In addition, movies popularity are deduced from social-media trends that are related to the corresponding new movie. A principled method is then applied to infer cold-start recommendations from these social-media signals. Experiments on a realistic time-frame, covering several movie-awards events between January 2014 and March 2014, showed significant improvements over ratings-only and metadata-only based recommendations.

Keywords

Cold-start Recommendation Online reputation Sentiment analysis Social-media 

Notes

Acknowledgements

This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0033/2014, by the H2020 ICT project COGNITUS with the grant agreement No 687605 and by the project NOVA LINCS Ref. UID/CEC/04516/2013.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • João Santos
    • 1
  • Filipa Peleja
    • 1
  • Flávio Martins
    • 1
  • João Magalhães
    • 1
  1. 1.NOVA Laboratory for Computer Science and InformaticsFCT NOVAAlmadaPortugal

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