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Explainable recommender with geometric information bottleneck
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Yan, Hanqi, Gui, Lin, Wang, Menghan, Zhang, Kun and He, Yulan (2024) Explainable recommender with geometric information bottleneck. IEEE Transactions on Knowledge and Data Engineering . doi:10.1109/tkde.2024.3350447 ISSN 1041-4347. (In Press)
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WRAP-explainable-recommender-geometric-information-bottleneck-He-2024.pdf - Accepted Version - Requires a PDF viewer. Download (3880Kb) | Preview |
Official URL: https://doi.org/10.1109/tkde.2024.3350447
Abstract
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours.
Item Type: | Journal Article | |||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Z Bibliography. Library Science. Information Resources > ZA Information resources |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Data mining, Information retrieval, Sentiment analysis , Natural language processing (Computer science) | |||||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Knowledge and Data Engineering | |||||||||||||||||||||
Publisher: | IEEE Computer Society | |||||||||||||||||||||
ISSN: | 1041-4347 | |||||||||||||||||||||
Official Date: | 5 January 2024 | |||||||||||||||||||||
Dates: |
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DOI: | 10.1109/tkde.2024.3350447 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | In Press | |||||||||||||||||||||
Re-use Statement: | © 2024 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
Date of first compliant deposit: | 7 February 2024 | |||||||||||||||||||||
Date of first compliant Open Access: | 7 February 2024 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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