Developing trust in recommender agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Proceedings of the 10th international conference on Intelligent user interfaces
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Attacks and Remedies in Collaborative Recommendation
IEEE Intelligent Systems
TREPPS: A Trust-based Recommender System for Peer Production Services
Expert Systems with Applications: An International Journal
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A recommender mechanism for service selection in service-oriented environments
Future Generation Computer Systems
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Due to the open nature of collaborative recommender systems, they can not effectively prevent malicious users from injecting fake profile data into the ratings database, which can significantly bias the system's output. With this problem in mind, in this paper we introduce the social trust of the users into the recommender system and build the trust relation between them. The values of trust among users are adjusted by using the reinforcement learning algorithm. On the basis of this, a user trust-based collaborative filtering recommendation algorithm is proposed. It uses the combined similarity to generate recommendation, which considers not only the similarity between user profiles but user trust as well. Experimental results show that the proposed algorithm outperforms the traditional user-based and item-based collaborative filtering algorithm in recommendation accuracy, especially in the face of malicious profile injection attacks.