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Rating Aggregation in Collaborative Filtering Systems

By Florent Garcin, Boi Faltings, Radu Jurca and Nadine Joswig

Abstract

Recommender systems based on user feedback rank items by aggregating users ’ ratings in order to select those that are ranked highest. Ratings are usually aggregated using a weighted arithmetic mean. However, the mean is quite sensitive to outliers and biases, and thus may not be the most informative aggregate. We compare the accuracy and robustness of three different aggregators: the mean, median and mode. The results show that the median may often be a better choice than the mean, and can significantly improve recommendation accuracy and robustness in collaborative filtering systems

Topics: Performance, Reliability, Security
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.184.5228
Provided by: CiteSeerX
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