ABSTRACT
There are many online systems where millions of users post original content such as videos, reviews of items such as products, services and businesses, etc. While there are general rules for good behavior or even formal Terms of Service, there are still users who post content that is not suitable. Increasingly, online systems rely on other users who view the posted content to provide feedback.
We study online systems where users report negative feedback, i.e., report abuse; these systems are quite distinct from much studied, traditional reputation systems that focus on eliciting popularity of content by various voting methods. The central problem that we study here is how to monitor the quality of negative feedback, that is, detect negative feedback which is incorrect, or perhaps even malicious. Systems address this problem by testing flags manually, which is an expensive operation. As a result, there is a tradeoff between the number of manual tests and the number of errors defined as the number of incorrect flags the monitoring system misses.
Our contributions are as follows:
We initiate a systematic study of negative feedbacks systems. Our framework is general enough to be applicable for a variety of systems. In this framework, the number of errors the system admits is bounded over the worst case of adversarial users while simultaneously the system performs only small amount of manual testing for multitude of standard users who might still err while reporting.
Our main contribution is a randomized monitoring algorithm that we call Adaptive Probabilistic Testing (APT), that is simple to implement and has guarantees on expected number of errors. Even for adversarial users, the total expected error is bounded by "N over N flags for a given e < 0. Simultaneously, the number of tests performed by the algorithm is within a constant factor of the best possible algorithm for standard users.
Finally, we present empirical study of our algorithm that shows its performance on both synthetic data and real data accumulated from a variety of negative feedback systems at Google. Our study indicates that the algorithm performs better than the analysis above shows.
- B. Adler and L. de Alfaro. A content-driven reputation system for the Wikipedia. In Proc. of the 16th Intl. World Wide Web Conf.(WWW 2007). Google ScholarDigital Library
- D. Berry and B. Fristedt. Bandit Problems. Chapman and Hall, 1985.Google ScholarCross Ref
- R. Bhattacharjee and A. Goel. Avoiding ballot stuffing in eBay-like reputation systems. In Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems, pages 133--137. ACM New York, NY, USA, 2005. Google ScholarDigital Library
- R. Bhattacharjee and A. Goel. Algorithms and incentives for robust ranking. In SODA, pages 425--433. Society for Industrial and Applied Mathematics Philadelphia, PA, USA, 2007. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer networks and ISDN systems, 30(1-7):107--117, 1998. Google ScholarDigital Library
- N. Cesa-Bianchi and G. Lugosi. Prediction, learning, and games. Cambridge University Press, 2006. Google ScholarDigital Library
- D. Helmbold, N. Littlestone, and P. Long. Apple tasting. Information and Computation, 161(2):85--139, 2000. Google ScholarDigital Library
- S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina. The eigentrust algorithm for reputation management in p2p networks. In WWW '03: Proceedings of the 12th international conference on World Wide Web, pages 640--651, New York, NY, USA, 2003. ACM. Google ScholarDigital Library
- J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999. Google ScholarDigital Library
- E. Zheleva, A. Kolcz, and L. Getoor. Trusting spam reporters: A reporter-based reputation system for email filtering. ACM Trans. Inf. Syst., 27(1):1--27, 2008. Google ScholarDigital Library
Index Terms
Monitoring algorithms for negative feedback systems
Recommendations
Negative feedback: the forsaken nature available for re-ranking
COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics: PostersRe-ranking for Information Retrieval aims to elevate relevant feedbacks and depress negative ones in initial retrieval result list. Compared to relevance feedback-based re-ranking method widely adopted in the literature, this paper proposes a new method ...
A study of methods for negative relevance feedback
SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrievalNegative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback ...
When humans and computers induce social stress through negative feedback: Effects on performance and subjective state
AbstractPeople increasingly work with autonomous systems, which progressively take over functions previously performed exclusively by humans. This may lead to situations in which automated agents give negative performance feedback, which ...
Highlights- Negative performance feedback from both human and computer did not impair subsequent performance on a wide range of tasks.
Comments