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Title: Learning hierarchical review graph representations for recommendation
Authors: Liu, Yong
Yang, Susen
Zhang, Yinan
Miao, Chunyan
Nie, Zaiqing
Zhang, Juyong
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Liu, Y., Yang, S., Zhang, Y., Miao, C., Nie, Z. & Zhang, J. (2021). Learning hierarchical review graph representations for recommendation. IEEE Transactions On Knowledge and Data Engineering.
Project: AISG-GC-2019-003
Journal: IEEE Transactions on Knowledge and Data Engineering
Abstract: The user review data have been demonstrated to be effective in solving different recommendation problems. Previous review-based recommendation methods usually employ sophisticated compositional models, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic representations from the review data for recommendation. However, these methods mainly capture the local dependency between neighboring words in a word window, and they treat each review equally. Therefore, they may not be effective in capturing the global dependency between words and tend to be easily biased by noise review information. In this paper, we propose a novel review-based recommendation model, named Review Graph Neural Network (RGNN). Specifically, RGNN builds a specific review graph for each individual user/item, which provides a global view about the user/item properties to help weaken the biases caused by noise review information. A type-aware graph attention mechanism is developed to learn semantic embeddings of words. Moreover, a personalized graph pooling operator is proposed to learn hierarchical representations of the review graph to form the semantic representation for each user/item. We compared RGNN with state-of-the-art review-based recommendation approaches on two real-world datasets. The experimental results indicate that RGNN consistently outperforms baseline methods, in terms of Mean Square Error (MSE).
ISSN: 1041-4347
DOI: 10.1109/TKDE.2021.3075052
Rights: © 2021 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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