An incentive-based architecture for social recommendations

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An incentive-based architecture for social recommendations

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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 229-232

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Rajat Bhattacharjee	Google Inc., Mountain View, CA, USA
Ashish Goel	Stanford University, Stanford, CA, USA
Konstantinos Kollias	Stanford University, Stanford, CA, USA

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 19, Downloads (12 Months): 19, Citation Count: 0

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ABSTRACT

We present an incentive-based architecture for providing recommendations in a social network. We maintain a distinct reputation system for each individual and we rely on users to identify appropriate correlations and rate the items using a system-provided recommendation language. The key idea is to design an incentive structure and a ranking system such that any inaccuracy in the recommendations implies the existence of a profitable arbitrage opportunity, hence making the system resistant to malicious spam and presentation bias. We also show that, under mild assumptions, our architecture provides users with incentive to minimize the Kullback-Leibler divergence between the ratings and the actual item qualities, quickly driving the system to an equilibrium state with accurate recommendations.

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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