An incentive-based architecture for social recommendations

Authors:
Rajat Bhattacharjee;Ashish Goel;Konstantinos Kollias
Affiliations:
Google Inc., Mountain View, CA, USA;Stanford University, Stanford, CA, USA;Stanford University, Stanford, CA, USA
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

Citing 4
Cited 0

Shilling recommender systems for fun and profit

Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis

ACM Transactions on Internet Technology (TOIT)
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Algorithms and incentives for robust ranking

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms

Quantified Score

Hi-index	0.00

Visualization

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.