Ensemble methods for improving the performance of neighborhood-based collaborative filtering

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Ensemble methods for improving the performance of neighborhood-based collaborative filtering

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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 261-264

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Alon Schclar	Deutsche Telekom Laboratories at Ben-Gurion University, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Alexander Tsikinovsky	Deutsche Telekom Laboratories at Ben-Gurion University, Beer-Sheva, Israel
Lior Rokach	Ben-Gurion University of the Negev, Beer-Sheva, Israel
Amnon Meisels	Ben-Gurion University of the Negev, Beer-Sheva, Israel
Liat Antwarg	Deutsche Telekom Laboratories at Ben-Gurion University, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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ACM New York, NY, USA

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ABSTRACT

Recommender systems provide consumers with ratings of items. These ratings are based on a set of ratings that were obtained from a wide scope of users. Predicting the ratings can be formulated as a regression problem. Ensemble regression methods are effective tools that improve the results of simple regression algorithms by iteratively applying the simple algorithm to a diverse set of inputs. The present paper describes a simple and effective ensemble regressor for the prediction of missing ratings in recommender systems. The ensemble method is an adaptation of the AdaBoost regression algorithm for recommendation tasks. In all iterations, interpolation weights for all nearest neighbors are simultaneously derived by minimizing the root mean squared error. From iteration to iteration instances that are hard to predict are reinforced by manipulating their weights in the goal function that needs to be minimized. The experimental evaluation demonstrates that the ensemble methodology significantly improves the predictive performance of single neighborhood-based collaborative filtering.

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.

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