Rating aggregation in collaborative filtering systems

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.

Published in:
Proceedings of the third ACM conference on Recommender systems - RecSys '09, 349-352
Presented at:
3rd ACM conference on Recommender systems (RecSys 09), New York, New York, USA, 23-25 October 2009
New York, New York, USA, ACM Press

Note: The status of this file is: Anyone

 Record created 2010-04-28, last modified 2020-07-30

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