Learning hierarchical review graph representations for recommendation
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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/156038 |
| _version_ | 1824455756653527040 |
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| author | Liu, Yong Yang, Susen Zhang, Yinan Miao, Chunyan Nie, Zaiqing Zhang, Juyong |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liu, Yong Yang, Susen Zhang, Yinan Miao, Chunyan Nie, Zaiqing Zhang, Juyong |
| author_sort | Liu, Yong |
| collection | NTU |
| description | 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). |
| first_indexed | 2025-02-19T03:43:16Z |
| format | Journal Article |
| id | ntu-10356/156038 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:43:16Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1560382022-03-31T07:38:49Z Learning hierarchical review graph representations for recommendation Liu, Yong Yang, Susen Zhang, Yinan Miao, Chunyan Nie, Zaiqing Zhang, Juyong School of Computer Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Alibaba-NTU Singapore Joint Research Institute Engineering::Computer science and engineering Review-Based Recommendation Hierarchical Graph Representation Learning 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). National Research Foundation (NRF) Submitted/Accepted version This research is supported, in part, by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore. This research is also supported, in part, by the National Research Foundation, Prime Minister’s Office, Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003) and under its NRF Investigatorship Programme (NRFI Award No. NRF-NRFI05-2019-0002). 2022-03-31T07:38:48Z 2022-03-31T07:38:48Z 2021 Journal Article 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. https://dx.doi.org/10.1109/TKDE.2021.3075052 1041-4347 https://hdl.handle.net/10356/156038 10.1109/TKDE.2021.3075052 2-s2.0-85105057167 en AISG-GC-2019-003 NRF-NRFI05-2019-0002 IEEE Transactions on Knowledge and Data Engineering © 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: https://doi.org/10.1109/TKDE.2021.3075052. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Review-Based Recommendation Hierarchical Graph Representation Learning Liu, Yong Yang, Susen Zhang, Yinan Miao, Chunyan Nie, Zaiqing Zhang, Juyong Learning hierarchical review graph representations for recommendation |
| title | Learning hierarchical review graph representations for recommendation |
| title_full | Learning hierarchical review graph representations for recommendation |
| title_fullStr | Learning hierarchical review graph representations for recommendation |
| title_full_unstemmed | Learning hierarchical review graph representations for recommendation |
| title_short | Learning hierarchical review graph representations for recommendation |
| title_sort | learning hierarchical review graph representations for recommendation |
| topic | Engineering::Computer science and engineering Review-Based Recommendation Hierarchical Graph Representation Learning |
| url | https://hdl.handle.net/10356/156038 |
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