Rating aggregation in collaborative filtering systems

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Rating aggregation in collaborative filtering systems

Full text

Pdf (325 KB)

Source

ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents

New York, New York, USA

SESSION: Short papers table of contents

Pages 349-352

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Florent Garcin	Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Boi Faltings	Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Radu Jurca	Google Inc., Zurich, Switzerland
Nadine Joswig	Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 17, Downloads (12 Months): 17, Citation Count: 0

Additional Information:

abstract references index terms

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1639714.1639785 What is a DOI?

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	L. Candillier, F. Meyer, and F. Fessant. Designing specific weighted similarity measures to improve collaborative filtering systems. In ICDM '08, 2008.
	2	F. Garcin, B. Faltings, and R. Jurca. Aggregating reputation feedback. In ICORE 09, 2009.
	3	J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating Collaborative Filtering Recommender Systems. ACM Trans. on Inf. Sys., 22:5 -- 53, 2004.
	4	R. Jurca, F. Garcin, A. Talwar, and B. Faltings. Reporting incentives and biases in online review forums. ACM Transaction on the Web. to appear.
	5	B. Mehta, T. Hofmann, and W. Nejdl. Robust Collaborative Filtering. In RecSys 07, 2007.
	6	B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol., 7(4):23, 2007.
	7	M. O'Mahony, N. Hurley, N. Kushmerick, and G. Silvestre. Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol., 4(4):344--377, 2004.
	8	P. Resnick and R. Sami. The influence limiter: provably manipulation-resistant recommender systems. In RecSys 07, 2007.
	9	P. Resnick and R. Sami. The information cost of manipulation-resistance in recommender systems. In RecSys 08, 2008.
	10	K. Walsh and E. G. Sirer. Fighting peer-to-peer spam and decoys with object reputation. In P2PECON 05, 2005.