Skip to Main content Skip to Navigation
Conference papers

dTrust: a simple 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 : Rating prediction is a key task of e-commerce recommendation mechanisms. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationship. However, these prediction approaches mostly rely on user personal information which is a privacy threat. In this paper, we present a simple rating prediction approach relying on deep neural networks that does not reveal any personal information. Experiments on real-world data sets showed that the approach outperforms state-of-the-art in terms of RMSE and MAE scores for both warm-start and cold-start problems.
Document type :
Conference papers
Complete list of metadata

Cited literature [59 references]  Display  Hide  Download
Contributor : Quang Vinh Dang Connect in order to contact the contributor
Submitted on : Wednesday, September 6, 2017 - 1:26:17 PM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM


Files produced by the author(s)


  • HAL Id : hal-01578316, version 2


Quang-Vinh Dang, Claudia-Lavinia Ignat. dTrust: a simple 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-01578316v2⟩



Les métriques sont temporairement indisponibles