A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems

  • Daniel Valcarce
  • Javier Parapar
  • Álvaro Barreiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

Abstract

Language Models have been traditionally used in several fields like speech recognition or document retrieval. It was only recently when their use was extended to collaborative Recommender Systems. In this field, a Language Model is estimated for each user based on the probabilities of the items. A central issue in the estimation of such Language Model is smoothing, i.e., how to adjust the maximum likelihood estimator to compensate for rating sparsity. This work is devoted to explore how the classical smoothing approaches (Absolute Discounting, Jelinek-Mercer and Dirichlet priors) perform in the recommender task. We tested the different methods under the recently presented Relevance-Based Language Models for collaborative filtering, and compared how the smoothing techniques behave in terms of precision and stability. We found that Absolute Discounting is practically insensitive to the parameter value being an almost parameter-free method and, at the same time, its performance is similar to Jelinek-Mercer and Dirichlet priors.

Keywords

Recommender systems Collaborative filtering Smoothing Relevance Models 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

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