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Hypergraphs with Attention on Reviews for Explainable Recommendation

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14608))

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Abstract

Given a recommender system based on reviews, the challenges are how to effectively represent the review data and how to explain the produced recommendations. We propose a novel review-specific Hypergraph (HG) model, and further introduce a model-agnostic explainability module. The HG model captures high-order connections between users, items, aspects, and opinions while maintaining information about the review. The explainability module can use the HG model to explain a prediction generated by any model. We propose a path-restricted review-selection method biased by the user preference for item reviews and propose a novel explanation method based on a review graph. Experiments on real-world datasets confirm the ability of the HG model to capture appropriate explanations.

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Notes

  1. 1.

    https://radimrehurek.com/gensim/parsing/preprocessing.html#gensim.parsing.preprocessing.stem_text.

  2. 2.

    All methods are available at https://github.com/PreferredAI/cornac.

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Acknowledgements

Katja Hose and Theis Jendal are supported by the Poul Due Jensen Foundation and the Independent Research Fund Denmark (DFF) under grant agreement no. DFF-8048-00051B. Hady W. Lauw and Trung-Hoang Le acknowledge that this research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2021-020).

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Authors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Theis E. Jendal, Matteo Lissandrini, Peter Dolog & Katja Hose

  2. Singapore Management University, Singapore, Singapore

    Trung-Hoang Le & Hady W. Lauw

  3. Technische Universität Wien, Vienna, Austria

    Katja Hose

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  1. Theis E. Jendal
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  2. Trung-Hoang Le
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Correspondence to Theis E. Jendal .

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Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Jendal, T.E., Le, TH., Lauw, H.W., Lissandrini, M., Dolog, P., Hose, K. (2024). Hypergraphs with Attention on Reviews for Explainable Recommendation. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14608. Springer, Cham. https://doi.org/10.1007/978-3-031-56027-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-56027-9_14

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