Rating aggregation in collaborative filtering systems

Authors:
Florent Garcin;Boi Faltings;Radu Jurca;Nadine Joswig
Affiliations:
Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Google Inc., Zurich, Switzerland;Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

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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.