The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
Open accessAbstract
A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.
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Cited by
Deep Learning Based Recommender System: A Survey and New Perspectives
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2020) · ACM Computing Surveys · doi:10.1145/3285029 · Cited by 1,511
Heterogeneous Information Network Embedding for Recommendation
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Self-citationDRN: A Deep Reinforcement Learning Framework for News Recommendation
Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N. J., Xie, X., & Li, Z. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3185994 · Cited by 630
Neural Attentional Rating Regression with Review-level Explanations
Chen, C., Zhang, M., Liu, Y., & Ma, S. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186070 · Cited by 622
Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022) · Proceedings of the 16th ACM Conference on Recommender Systems · doi:10.1145/3523227.3546767 · Cited by 556
Metadata
- Authors
- Zheng, L., Noroozi, V., & Yu, P. S.
- Venue
- Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2017-02-02
- Pages
- 425-434
- OpenAlex
- W2575006718
- Cited by
- 1,075
- References
- 38
- FWCI
- 88.10
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Zheng, L., Noroozi, V., & Yu, P. S. (2017). Joint Deep Modeling of Users and Items Using Reviews for Recommendation. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3018661.3018665
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