Balls and bins models with feedback
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Shuffling a stacked deck: the case for partially randomized ranking of search engine results
VLDB '05 Proceedings of the 31st international conference on Very large data bases
The "top N" news recommender: count distortion and manipulation resistance
Proceedings of the fifth ACM conference on Recommender systems
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In prior work we addressed a major problem faced by media sites with popularity based recommender systems such as the top-10 list of most liked or most clicked posts. We showed that the hard cutoff used in these systems to generate the "Top N"lists is prone to unduly penalizing good articles that may have just missed the cutoff. A solution to this was to generate recommendations probabilistically, which is an approach that has been shown to be robust against some manipulation techniques as well. The aim of this research is to introduce a class of probabilistic news recommender systems that incorporates widely practiced recommendation techniques as a special case. We establish our results in a special case of two articles using the urn models with feedback mechanism from probability theory.