The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
On Deep Learning for Trust-Aware Recommendations in Social Networks
Abstract
With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.
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Cites
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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
Stewardship of global collective behavior
Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025764118 · Cited by 289
A Deep Graph Neural Network-Based Mechanism for Social Recommendations
Guo, Z., & Wang, H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2986316 · Cited by 269
A Survey of Graph Neural Networks for Social Recommender Systems
Sharma, K., Lee, Y.-C., Nambi, S., Salian, A., Shah, S., Kim, S.-W., & Kumar, S. (2024) · ACM Computing Surveys · doi:10.1145/3661821 · Cited by 268
A synergetic trust model based on SVM in underwater acoustic sensor networks
Han, G., He, Y., Jiang, J., Wang, N., Guizani, M., & Ansere, J. A. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2939179 · Cited by 106
Metadata
- Authors
- Deng, S., Huang, L., Xu, G., Wu, X., & Wu, Z.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2017-05
- Volume
- 28
- Issue
- 5
- Pages
- 1164-1177
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2343644403
- Cited by
- 309
- References
- 26
- FWCI
- 77.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- Deng, S., Huang, L., Xu, G., Wu, X., & Wu, Z. (2017). On Deep Learning for Trust-Aware Recommendations in Social Networks. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2016.2514368
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