GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
Modern Information Retrieval
Proceedings of the 10th international conference on Intelligent user interfaces
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
Reputation-Oriented Trustworthy Computing in E-Commerce Environments
IEEE Internet Computing
Collaborative filtering recommender systems
The adaptive web
Using trust in collaborative filtering recommendation
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
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Collaborative Filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interests to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into the CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust value based on users' past ratings on items. A user is more trustworthy if he has contributed more accurate predictions than other users. Nevertheless, none of them derive the trust value based on a sequence of user's ratings on items. We propose a sequence-based trust model to derive the trust value based on users' sequences of ratings on documents. In knowledge-intensive environments, users normally have various information needs to access required documents over time, which forms a sequence of documents ordered according to their access time. The model considers two factors - time factor and document similarity in computing the trustworthiness of users. The proposed model is incorporated into standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method comparing to other trust-based recommender systems.