Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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)
On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Social Information Processing in News Aggregation
IEEE Internet Computing
Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges
IEEE Internet Computing
User Participation in Social Media: Digg Study
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
A few bad votes too many?: towards robust ranking in social media
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Constructing folksonomies from user-specified relations on flickr
Proceedings of the 18th international conference on World wide web
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Click fraud resistant methods for learning click-through rates
WINE'05 Proceedings of the First international conference on Internet and Network Economics
The "top N" news recommender: count distortion and manipulation resistance
Proceedings of the fifth ACM conference on Recommender systems
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In a social news website people share content they found on the web, called news, then vote for those they like the most. Voting for a news is then considered as a recommendation, and news with a sufficient number of recommendations are displayed on a front page. Malicious users of such websites boost their own content by manipulating the votes. We present SpotRank, an algorithm that can demote the effect of manipulations, thus leading to a better quality of service. We also present a website that implement this algorithm and show evidence of the efficiency of the approach, both from a statistical and human point of view.