Local model-agnostic explanations for black-box recommender systems using interaction graphs and link prediction techniques

  1. Marta Caro-Martínez 1
  2. Guillermo Jiménez-Díaz 1
  3. Juan A. Recio García 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 2

Páginas: 202-212

Tipo: Artículo

DOI: 10.9781/IJIMAI.2021.12.001 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

Explanations in recommender systems are a requirement to improve users’ trust and experience. Traditionally, explanations in recommender systems are derived from their internal data regarding ratings, item features, and user profiles. However, this information is not available in black-box recommender systems that lack sufficient data transparency. This current work proposes a local model-agnostic, explanation-by-example method for recommender systems based on knowledge graphs to leverage this knowledge requirement. It only requires information about the interactions between users and items. Through the proper transformation of these knowledge graphs into item-based and user-based structures, link prediction techniques are applied to find similarities between the nodes and to identify explanatory items for the user’s recommendation. Experimental evaluation demonstrates that these knowledge graphs are more effective than classical content-based explanation approaches but have lower information requirements, making them more suitable for black-box recommender systems.

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Fuente de los datos: Dialnet