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.


Publié dans:
Proceedings of the third ACM conference on Recommender systems - RecSys '09, 349-352
Présenté à:
3rd ACM conference on Recommender systems (RecSys 09), New York, New York, USA, 23-25 October 2009
Année
2009
Publisher:
New York, New York, USA, ACM Press
Mots-clefs:
Laboratoires:




 Notice créée le 2010-04-28, modifiée le 2019-12-05

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