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Personalized Case-Based Explanation of Matrix Factorization Recommendations

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Abstract

Matrix factorization is an advanced recommendation strategy based on characterizing both items and users on a vector of latent factors inferred from rating patterns. These vectors represent, somehow, a characterization of the user preferences in a lower dimensionality space. Although matrix factorization is more accurate that other recommendation strategies, the main problem associated with this approach is that the discovered factors are opaque and difficult to explain to the final user. In this paper we propose a personalized case-based explanation strategy that uses the latent factors to find similar explanatory cases already rated by the user.

Supported by the UCM (Research Group 921330), the Spanish Committee of Economy and Competitiveness (TIN2017-87330-R) and the fundings provided by Banco Santander in UCM (CT17/17-CT17/18) and (CT42/18-CT43/18).

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Notes

  1. 1.

    https://www.imdb.com/.

  2. 2.

    This is the highest possible value as the dataset only contains 34 items rated with 2.5 as shown in Table 5.

  3. 3.

    Ratings 0.5 and 1.5 have been removed due to the low number of items.

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Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid, Madrid, Spain

    Jose Jorro-Aragoneses, Marta Caro-Martinez, Juan Antonio Recio-Garcia, Belen Diaz-Agudo & Guillermo Jimenez-Diaz

Authors
  1. Jose Jorro-Aragoneses
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  2. Marta Caro-Martinez
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  3. Juan Antonio Recio-Garcia
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  4. Belen Diaz-Agudo
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  5. Guillermo Jimenez-Diaz
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Corresponding authors

Correspondence to Jose Jorro-Aragoneses , Marta Caro-Martinez , Juan Antonio Recio-Garcia , Belen Diaz-Agudo or Guillermo Jimenez-Diaz .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Kerstin Bach

  2. Ohio University, Athens, OH, USA

    Cindy Marling

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Jorro-Aragoneses, J., Caro-Martinez, M., Recio-Garcia, J.A., Diaz-Agudo, B., Jimenez-Diaz, G. (2019). Personalized Case-Based Explanation of Matrix Factorization Recommendations. In: Bach, K., Marling, C. (eds) Case-Based Reasoning Research and Development. ICCBR 2019. Lecture Notes in Computer Science(), vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-29249-2_10

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  • Online ISBN: 978-3-030-29249-2

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