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. Copyright 2009 ACM.
|Original language||English (US)|
|Title of host publication||Proceedings of the third ACM conference on Recommender systems - RecSys '09|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||4|
|State||Published - 2009|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research conducted while at Stanford University. Researchsupported by NSF ITR grant 0428868 and NSF award0339262.Department of Management Science and Engineering and(by courtesy) Computer Science, Stanford University. Researchsupported by NSF ITR grant 0428868 and gifts fromGoogle, Microsoft, and Cisco.Department of Management Science and Engineering,Stanford University. Research supported by an A. G. LeventisFoundation Scholarship and the Stanford-KAUST alliancefor excellence in academics.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.