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PRIN: A Probabilistic Recommender with Item Priors and Neural Models

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Book cover Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

In this paper, we present PRIN, a probabilistic collaborative filtering approach for top-N recommendation. Our proposal relies on continuous bag-of-words (CBOW) neural model. This fully connected feedforward network takes as input the item profile and produces as output the conditional probabilities of the users given the item. With that information, our model produces item recommendations through Bayesian inversion. The inversion requires the estimation of item priors. We propose different estimates based on centrality measures on a graph that models user-item interactions. An exhaustive evaluation of this proposal shows that our technique outperforms popular state-of-the-art baselines regarding ranking accuracy while showing good values of diversity and novelty.

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Notes

  1. 1.

    https://gitlab.irlab.org/alfonso.landin/prin.

  2. 2.

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

  3. 3.

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

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Acknowledgments

This work has received financial support from project TIN2015-64282-R (MINECO/ERDF) and accreditation ED431G/01 (Xunta de Galicia/ERDF). The first author acknowledges the support of grant FPU17/03210 (MICIU) and the second author acknowledges the support of grant FPU014/01724 (MICIU).

Author information

Authors and Affiliations

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

    Alfonso Landin, Daniel Valcarce, Javier Parapar & Álvaro Barreiro

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

Correspondence to Alfonso Landin .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Landin, A., Valcarce, D., Parapar, J., Barreiro, Á. (2019). PRIN: A Probabilistic Recommender with Item Priors and Neural Models. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-15712-8_9

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