A survey of trust and reputation systems for online service provision
Decision Support Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A model of a trust-based recommendation system on a social network
Autonomous Agents and Multi-Agent Systems
TREPPS: A Trust-based Recommender System for Peer Production Services
Expert Systems with Applications: An International Journal
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Applied Intelligence
Efficient routing on finding recommenders for trust-aware recommender systems
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
Extract reputable users for trust-aware applications
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Existing models of the Trust-Aware Recommender System (TARS) build personalized trust networks for the active users to predict ratings. These models have reasonable rating prediction performances, while suffer from high computational complexity. One solution is to utilize the global rating prediction mechanism for TARS, in which an intuitive assumption is that more reputable recommenders give more accurate recommendations. In addition, due to the scale-freeness of the trust network, some users have and continuously have superior reputations than others. However, we show via comprehensive experiments on the real TARS data that the recommendations given by recommenders with higher reputations do not tend to be more accurate. Furthermore, even the recommendations given by the recommenders with superior high reputations do not tend to more accurate. Our experimental study provides promising directions for the future research on the rating prediction mechanism of TARS.