research-article

Aspect-Guided Syntax Graph Learning for Explainable Recommendation

Authors:

Yuan MiaoAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 35, Issue 8

Pages 7768 - 7781

https://doi.org/10.1109/TKDE.2022.3221847

Published: 01 August 2023 Publication History

Abstract

Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods generate textual explanations without explicitly considering the user's preferences on different aspects of the item. In this paper, we propose a novel explanation generation framework, namely <bold><underline>A</underline></bold>spect-guided <bold><underline>E</underline></bold>xplanation generation with <bold><underline>S</underline></bold>yntax <bold><underline>G</underline></bold>raph (<bold>AESG</bold>), for explainable recommendation. Specifically, AESG employs a review-based syntax graph to provide a unified view of the user/item details. An aspect-guided graph pooling operator is proposed to extract the aspect-relevant information from the review-based syntax graphs to model the user's preferences on an item at the aspect level. Then, an aspect-guided explanation decoder is developed to generate aspects and aspect-relevant explanations based on the attention mechanism. The experimental results on three real datasets indicate that AESG outperforms state-of-the-art explanation generation methods in both single-aspect and multi-aspect explanation generation tasks, and also achieves comparable or even better preference prediction accuracy than strong baseline methods.

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Cited By

Ariza-Casabona ABoratto LSalamó M(2024)A Comparative Analysis of Text-Based Explainable Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688069(105-115)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688069
Barkan OBogina VGurevitch LAsher YKoenigstein NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Counterfactual Framework for Learning and Evaluating Explanations for Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645560(3723-3733)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645560

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Published: 01 August 2023

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Cited By

Ariza-Casabona ABoratto LSalamó M(2024)A Comparative Analysis of Text-Based Explainable Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688069(105-115)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688069
Barkan OBogina VGurevitch LAsher YKoenigstein NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Counterfactual Framework for Learning and Evaluating Explanations for Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645560(3723-3733)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645560

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