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European Conference on Information Retrieval

ECIR 2016: Advances in Information Retrieval pp 602–613Cite as

Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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Notes

  1. 1.

    http://grouplens.org/datasets/movielens/.

  2. 2.

    http://webscope.sandbox.yahoo.com.

  3. 3.

    http://www.macle.nl/tud/LT/.

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Acknowledgments

This work was supported by the Ministerio de Economía y Competitividad of the Government of Spain under grants TIN2012-33867 and TIN2015-64282-R. The first author also wants to acknowledge the support of Ministerio de Educación, Cultura y Deporte of the Government of Spain under the grant FPU014/01724.

Author information

Authors and Affiliations

  1. Information Retrieval Lab, Computer Science Department, University of A Coruña, A Coruña, Spain

    Daniel Valcarce, Javier Parapar & Álvaro Barreiro

Authors
  1. Daniel Valcarce
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  2. Javier Parapar
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  3. Álvaro Barreiro
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Corresponding author

Correspondence to Daniel Valcarce .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Padua, Padova, Italy

    Nicola Ferro

  2. Faculty of Informatics, University of Lugano (USI), Lugano, Switzerland

    Fabio Crestani

  3. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  4. Systèmes d’informations, Big Data et Recherche d’Information, Institut de Recherche en Informatique de Toulouse IRIT/équipe SIG, Toulouse Cedex 04, France

    Josiane Mothe

  5. Yahoo! Labs London, London, UK

    Fabrizio Silvestri

  6. Department of Information Engineering, University of Padua, Padova, Italy

    Giorgio Maria Di Nunzio

  7. TU Delft - EWI/ST/WIS, Delft, The Netherlands

    Claudia Hauff

  8. Department of Information Engineering, University of Padua, Padova, Italy

    Gianmaria Silvello

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© 2016 Springer International Publishing Switzerland

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Valcarce, D., Parapar, J., Barreiro, Á. (2016). Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-30671-1_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30670-4

  • Online ISBN: 978-3-319-30671-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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