文章基本信息

标题：Leveraging Distrust Relations to Improve Bayesian Personalized Ranking
本地全文：下载
作者：Yangjun Xu ; Ke Xu ; Yi Cai 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2018
卷号：9
期号：8
页码：191
DOI：10.3390/info9080191
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：distrust relations; trust relations; Bayesian personalized ranking distrust relations ; trust relations ; Bayesian personalized ranking