Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
3-2024
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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28
First Page
230
Last Page
246
ISBN
9783031560262
Identifier
10.1007/978-3-031-56027-9_14
Publisher
Springer
City or Country
Cham, Switzerland
Citation
JENDAL, Theis E.; LE, Trung Hoang; LAUW, Hady Wirawan; LISSANDRINI, Matteo; DOLOG, Peter; and HOSE, Katja.
Hypergraphs with attention on reviews for explainable recommendation. (2024). Proceedings of the 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28. 230-246.
Available at: https://ink.library.smu.edu.sg/sis_research/8724
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-031-56027-9_14