A Two-tier Shared Embedding Method for Review-based Recommender Systems
Authors: 
Zhen Yang, Junrui Liu, Tong Li, Di Wu, Shiqiu Yang, 
Huan LiuAuthors Info & Claims
Zhen Yang, Junrui Liu, Tong Li, Di Wu, Shiqiu Yang, Huan LiuAuthors Info & ClaimsPages 2928 - 2938
Abstract
Reviews are valuable resources that have been widely researched and used to improve the quality of recommendation services. Recent methods use multiple full embedding layers to model various levels of individual preferences, increasing the risk of the data sparsity issue. Although it is a potential way to deal with this issue that models homophily among users who have similar behaviors, the existing approaches are implemented in a coarse-grained way. They calculate user similarities by considering the homophily in their global behaviors but ignore their local behaviors under a specific context. In this paper, we propose a two-tier shared embedding model (TSE), which fuses coarse- and fine-grained ways of modeling homophily. It considers global behaviors to model homophily in a coarse-grained way, and the high-level feature in the process of each user-item interaction to model homophily in a fine-grained way. TSE designs a whole-to-part principle-based process to fuse these ways in the review-based recommendation. Experiments on five real-world datasets demonstrate that TSE significantly outperforms state-of-the-art models. It outperforms the best baseline by 20.50% on the root-mean-square error (RMSE) and 23.96% on the mean absolute error (MAE), respectively. The source code is available at https://github.com/dianziliu/TSE.git.
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Index Terms
- A Two-tier Shared Embedding Method for Review-based Recommender Systems
Recommendations
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October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
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Published: 21 October 2023
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- Beijing Municipal Education Commission
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- National Natural Science Foundation of China
- Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
Birmingham, United Kingdom
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