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dTrust: a deep learning approach for social recommendation

Quang-Vinh Dang 1 Claudia-Lavinia Ignat 1
1 COAST - Web Scale Trustworthy Collaborative Service Systems
Inria Nancy - Grand Est, LORIA - NSS - Department of Networks, Systems and Services
Abstract : Recommender systems play an important role in modern e-commerce systems. Rating prediction is an important task for recommender systems. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationship. However, these approaches mostly rely on user personal information to make a prediction. Due to privacy concerns, we should avoid using user personal information. In this paper, we present a rating prediction approach relying on deep learning. The approach is easy to implement and does not reveal any personal information. Experiments on real-world data sets showed that the approach outperforms state-of-the-art in both warm-start and cold-start problems.
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Submitted on : Tuesday, August 29, 2017 - 7:56:56 AM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM


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  • HAL Id : hal-01578316, version 1


Quang-Vinh Dang, Claudia-Lavinia Ignat. dTrust: a deep learning approach for social recommendation. The 3rd IEEE International Conference on Collaboration and Internet Computing (CIC-17), Oct 2017, San Jose, United States. ⟨hal-01578316v1⟩



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