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Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems

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Explainable Artificial Intelligence (xAI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2154))

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Abstract

Explanations in recommender systems play an essential role in enhancing transparency, trust, and persuasiveness. In that regard, Knowledge Graphs (KGs) model-agnostic explanations do not rely on user-inputted data such as reviews or require any changes in a recommendation algorithm to provide explanations. The state-of-the-art of model-agnostic KG explainable algorithms are based on syntactic approaches that consider the trade-off of attributes among the user-interacted items and the catalog to explain recommendations. In this study, we propose a novel model-agnostic KG algorithm for explanations. Our approach utilizes KG embeddings to rank explanations based on the path’s similarity to the user. Specifically, we train an embedding algorithm on a KG and compare path embeddings, composed of node and edge embeddings, to the user embedding derived from previously interacted item embeddings. Our proposed method is evaluated by comparing it against three baselines representing the state-of-the-art of KG explanation algorithms. We assess explanation quality using three metrics: diversity and popularity of attributes displayed in explanations and recency of interacted items. Results indicate that the embedding approach achieves a superior balance between attribute popularity and explanation diversity. Furthermore, our analysis emphasizes the importance of tailored metrics for evaluating explanations in recommender systems.

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Notes

  1. 1.

    https://github.com/andlzanon/lod-personalized-recommender.

  2. 2.

    https://www.wikidata.org.

  3. 3.

    https://www.dbpedia.org/.

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Acknowledgments

The authors acknowledge CAPES, CNPq, Fapesp (2022/07016-9), AWS and Fapemig for their funding and support of this research.

Author information

Authors and Affiliations

  1. Universidade de São Paulo, São Paulo, Brazil

    André Levi Zanon & Marcelo Garcia Manzato

  2. Universidade Federal de São João del-Rei, Minas Gerais, Brazil

    Leonardo Chaves Dutra da Rocha

Authors
  1. André Levi Zanon
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  2. Leonardo Chaves Dutra da Rocha
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  3. Marcelo Garcia Manzato
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Corresponding author

Correspondence to André Levi Zanon .

Editor information

Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Fraunhofer Institute for Telecommunications, Berlin, Germany

    Sebastian Lapuschkin

  3. University of Marburg, Marburg, Germany

    Christin Seifert

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Appendices

Appendix 1 - Recommender Systems Algorithms Metrics

(See Table 9)

Table 9. Mean accuracy and beyond accuracy metrics for the MovieLens100k and LastFM datasets on a 10-fold cross-validation. Bold values are the highest for a dataset. The NCF algorithms do not have beyond accuracy metrics because the leave-one-out evaluation was used as proposed in the original paper.
Full size table

Appendix 2 - RotatE KG Embedding Training Statistics

Table 10. Metrics for the training and testing of the RotatE KG embeddings.
Full size table

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Zanon, A.L., da Rocha, L.C.D., Manzato, M.G. (2024). Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2154. Springer, Cham. https://doi.org/10.1007/978-3-031-63797-1_1

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