File(s) under permanent embargo

A novel fine-grained user trust relation prediction for improving recommendation accuracy

conference contribution
posted on 2017-01-11, 00:00 authored by S Zhang, Xiao LiuXiao Liu, Y Jiang, M Zhang
Recommender Systems (RSs) are playing an important role in improving user satisfaction as they can recommend items which might be highly interested to users. In recent years, it has been observed that social relations including factors such as trust and distrust among users are very useful in improving recommendation accuracy. However, traditional recommendation methods like Collaborative Filtering (CF) usually neglect social relations and even for those methods which considered social relations often fail to uncover different types of positive and negative social relations, which hinders the improvement of recommendation accuracy. To solve such a problem, in this paper, we first divide user trust relations into four fine-grained types, including strong trust, weak trust, strong distrust and weak distrust, which help to thoroughly exploit the trust and distrust relations among users. Afterwards, we propose a trust prediction framework based on a SVD algorithm to obtain weighted social relations. Finally, we employ two examples on rating prediction to demonstrate how to use fine-grained user trust relations. Experimental results based on Extended Epinions dataset show that our proposed approach based on fine-grained user trust relations can achieve better accuracy than other conventional approaches.



Advanced Cloud and Big Data. International Conference (2016 : Chengdu, China)


164 - 171




Chengdu, China

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2016, IEEE

Title of proceedings

CBD 2016 : Proceedings of the 2016 International Conference on Advanced Cloud and Big Data