Trust-aware Social Recommender System Design

Peixin Gao, John S. Baras, Jennifer Golbeck


Recommender systems are designed to overcome the problem of information overload created by the Internet. However, current approaches for recommender system still suffer from the problems such as sparse information, cold start, and adversary attacks. On the other hand, social network sites (SNS), like Facebook and Epinions, offer a good source of knowledge for recommendation. The idea of integrating signals from social network to improve the performance of the recommendation algorithm has been well accepted and has attracted an increasing amount of research in both academia and industry. In this work, we develop a trust-aware recommender system. We interpret connections in SNS as trust relationships among users, and establish a trust network based on the social graph aligned with the recommender system. Specially, we handle indirect trust in our model, which could enlarge the information source to a large amount. We also discuss the issue of distrust and propose a way to consider both trust and distrust in our model. We also consider integrating our trust-aware recommendation framework with classic collaborative filtering to take advantage of both approaches and further improve the performance in rating prediction and item recommendation.


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Paper Citation

in Harvard Style

Gao P., S. Baras J. and Golbeck J. (2015). Trust-aware Social Recommender System Design . In Doctoral Consortium - DCISSP, (ICISSP 2015) ISBN , pages 19-28

in Bibtex Style

author={Peixin Gao and John S. Baras and Jennifer Golbeck},
title={Trust-aware Social Recommender System Design},
booktitle={Doctoral Consortium - DCISSP, (ICISSP 2015)},

in EndNote Style

JO - Doctoral Consortium - DCISSP, (ICISSP 2015)
TI - Trust-aware Social Recommender System Design
SN -
AU - Gao P.
AU - S. Baras J.
AU - Golbeck J.
PY - 2015
SP - 19
EP - 28
DO -