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An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs

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Case-Based Reasoning Research and Development (ICCBR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11680))

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Abstract

Explanations in recommender systems are essential to improve user confidence in the recommendation. Traditionally, recommendation algorithms are based on ratings or additional information about the item features or the user profile. But some of these approaches are implemented as black boxes where this information is not available to provide the explanations. In this work, we propose a case-based approach to support this kind of black-box recommenders in order to find explanatory examples. It is a knowledge-light approach that only requires the information extracted from the interactions between users and items. As these interaction graphs can be analyzed through social network analysis, we propose the use of link prediction techniques to find the most suitable explanatory cases for a recommended item.

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

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Notes

  1. 1.

    We are assuming link prediction metrics as similarity measures, although in our approach they are not normalized to [0,1] because we only need the resulting score to rank and compare items.

  2. 2.

    https://grouplens.org/datasets/movielens/100k/.

  3. 3.

    https://www.imdb.com/.

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Authors and Affiliations

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

    Marta Caro-Martinez, Juan A. Recio-Garcia & Guillermo Jimenez-Diaz

Authors
  1. Marta Caro-Martinez
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  2. Juan A. Recio-Garcia
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  3. Guillermo Jimenez-Diaz
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Corresponding author

Correspondence to Marta Caro-Martinez .

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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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Caro-Martinez, M., Recio-Garcia, J.A., Jimenez-Diaz, G. (2019). An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs. 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_2

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  • DOI: https://doi.org/10.1007/978-3-030-29249-2_2

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