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Ways of Computing Diverse Collaborative Recommendations

  • Derek Bridge
  • John Paul Kelly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)

Abstract

Conversational recommender systems adapt the sets of products they recommend in light of user feedback. Our contribution here is to devise and compare four different mechanisms for enhancing the diversity of the recommendations made by collaborative recommenders. Significantly, we increase diversity using collaborative data only. We find that measuring the distance between products using Hamming Distance is more effective than using Inverse Pearson Correlation.

Keywords

Active User Recommender System Target Item Near Neighbour Rating Matrix 
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 2006

Authors and Affiliations

  • Derek Bridge
    • 1
  • John Paul Kelly
    • 1
  1. 1.University College CorkCorkIreland

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