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Information 2018, 9(8), 191; https://doi.org/10.3390/info9080191

Leveraging Distrust Relations to Improve Bayesian Personalized Ranking

,
†,* , * and
School of Software Engineering, South China University of Technology, Guangzhou 510641, China
These authors contributed equally to this work.
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Authors to whom correspondence should be addressed.
Received: 6 June 2018 / Revised: 25 July 2018 / Accepted: 25 July 2018 / Published: 27 July 2018
(This article belongs to the Section Information Systems)
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Abstract

Distrust based recommender systems have drawn much more attention and became widely acceptable in recent years. Previous works have investigated using trust information to establish better models for rating prediction, but there is a lack of methods using distrust relations to derive more accurate ranking-based models. In this article, we develop a novel model, named TNDBPR (Trust Neutral Distrust Bayesian Personalized Ranking), which simultaneously leverages trust, distrust, and neutral relations for item ranking. The experimental results on Epinions dataset suggest that TNDBPR by leveraging trust and distrust relations can substantially increase various performance evaluations including F1 score, AUC, Precision, Recall, and NDCG. View Full-Text
Keywords: distrust relations; trust relations; Bayesian personalized ranking distrust relations; trust relations; Bayesian personalized ranking
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xu, Y.; Xu, K.; Cai, Y.; Min, H. Leveraging Distrust Relations to Improve Bayesian Personalized Ranking. Information 2018, 9, 191.

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