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Title: Aspect-guided syntax graph learning for explainable recommendation
Authors: Hu, Yidan
Liu, Yong
Miao, Chunyan
Lin, Gongqi
Miao, Yuan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Hu, Y., Liu, Y., Miao, C., Lin, G. & Miao, Y. (2022). Aspect-guided syntax graph learning for explainable recommendation. IEEE Transactions On Knowledge and Data Engineering.
Project: AISG-GC-2019-003 
Journal: IEEE Transactions on Knowledge and Data Engineering 
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.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2022.3221847
Schools: School of Computer Science and Engineering 
Research Centres: Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) 
Rights: © 2022 IEEE. 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. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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