On deep learning for trust-aware recommendations in social networks

Shuiguang DENG, Longtao HUANG, Guandong XU, Xindong WU, Zhaohui WU

Research output: Contribution to journalArticlespeer-review

255 Citations (Scopus)


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. Copyright © 2016 IEEE.

Original languageEnglish
Pages (from-to)1164-1177
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Early online dateFeb 2016
Publication statusPublished - May 2017


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, 28(5), 1164-1177. https://doi.org/10.1109/TNNLS.2016.2514368


  • Deep learning
  • Recommender systems (RSs)
  • Social network
  • Trust


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