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.
A study by REF suggests that among mean, median, and mode, the median is often a better choice as it is not as sensitive to outliers as the mean.
Rating aggregation in collaborative filtering systems
RecSys '09
Computer Science