GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multiple criteria optimization-based data mining methods and applications: a systematic survey
Knowledge and Information Systems
Robust ensemble learning for mining noisy data streams
Decision Support Systems
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With the rapid growth of information on the World Wide Web, recommender system has been receiving increasing attention. In academic literature recommendation applications, existing methods recommend papers merely based on their contents or cited frequencies, and none of them consider user's personalized requirements, such as authority, popularity, time, etc. To this end, in this paper, we propose a utility-based recommendation method. Experiments on a real-world data set show that our approach can obtain personalized recommendations without losing much quality.