On the security of pay-per-click and other Web advertising schemes
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms
Journal of the ACM (JACM)
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Communications of the ACM
Combinatorial Information Market Design
Information Systems Frontiers
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Convex Optimization
Duplicate detection in click streams
WWW '05 Proceedings of the 14th international conference on World Wide Web
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
Avoiding ballot stuffing in eBay-like reputation systems
Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Evolution of page popularity under random web graph models
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Proceedings of the 3rd international workshop on Economics of networked systems
Manipulation-resistant recommender systems through influence limits
ACM SIGecom Exchanges
Sharing Online Advertising Revenue with Consumers
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Comparing both relevance and robustness in selection of web ranking functions
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Manipulation-resistant collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
An incentive-based architecture for social recommendations
Proceedings of the third ACM conference on Recommender systems
A spatial model for collaborative filtering of comments in an online discussion forum
Proceedings of the third ACM conference on Recommender systems
Incorporating robustness into web ranking evaluation
Proceedings of the 18th ACM conference on Information and knowledge management
Monitoring algorithms for negative feedback systems
Proceedings of the 19th international conference on World wide web
Global budgets for local recommendations
Proceedings of the fourth ACM conference on Recommender systems
Who moderates the moderators?: crowdsourcing abuse detection in user-generated content
Proceedings of the 12th ACM conference on Electronic commerce
Robust ranking models via risk-sensitive optimization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Spam in the form of link spam and click spam has become a major obstacle in the effective functioning of ranking and reputation systems. Even in the absence of spam, difficulty in eliciting feedback and self-reinforcing nature of ranking systems are known problems. In this paper, we make a case for sharing with users the revenue generated by such systems as incentive to provide useful feedback and present an incentive based ranking scheme in a realistic model of user behavior which addresses the above problems. We give an explicit ranking algorithm based on user feedback. Our incentive structure and ranking algorithm ensure that there is a profitable arbitrage opportunity for the users of the system in correcting the inaccuracies of the ranking. The system is oblivious to the source of inaccuracies (benign or malicious), thus making it robust to spam as well as the problems of eliciting feedback and self-reinforcement.