Leveraging Distrust Relations to Improve Bayesian Personalized Ranking

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.

Mobile Phone Recommender System Using Information Retrieval Technology by Integrating Fuzzy OWA and Gray Relational Analysis

“…The RS based on distrust proposed by Xu et al [19] has attracted much attention and has been widely accepted in recent years. A previous work investigated the use of trust information to establish a better rating prediction model, but any method where the distrust relationship is used to obtain a more accurate ranking-based model is lacking.…”