Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation

  • Daniel Valcarce
  • Javier Parapar
  • Álvaro Barreiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

Recently, Relevance-Based Language Models have been demonstrated as an effective Collaborative Filtering approach. Nevertheless, this family of Pseudo-Relevance Feedback techniques is computationally expensive for applying them to web-scale data. Also, they require the use of smoothing methods which need to be tuned. These facts lead us to study other similar techniques with better trade-offs between effectiveness and efficiency. Specifically, in this paper, we analyse the applicability to the recommendation task of four well-known query expansion techniques with multiple probability estimates. Moreover, we analyse the effect of neighbourhood length and devise a new probability estimate that takes into account this property yielding better recommendation rankings. Finally, we find that the proposed algorithms are dramatically faster than those based on Relevance-Based Language Models, they do not have any parameter to tune (apart from the ones of the neighbourhood) and they provide a better trade-off between accuracy and diversity/novelty.

Keywords

Recommender systems Collaborative filtering Query expansion Pseudo-Relevance Feedback 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Daniel Valcarce
    • 1
  • Javier Parapar
    • 1
  • Álvaro Barreiro
    • 1
  1. 1.Information Retrieval Lab, Computer Science DepartmentUniversity of A CoruñaA CoruñaSpain

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