Speaker: Chadi El Kari
 
Day:  Wednesday, 11/17/2004
Room: ITE 336
Time:  4:05pm-4:40pm 

Title: collaborative filtering
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Abstract: 
A collaborative filtering system analyzes data on the past behavior of its users so as to make recommendations 
tailored to specific user interests.
A well-known example of collaborative filtering in a practical setting is Amazon's purchase recommendations
which is based on rules of the form "users who are interested in item X are also likely to be interested in item Y"
In this talk a recommendation algorithm with strong provable performances (as the amount of data increases, the quality
of its recommendations approach those of the optimal omniscient algorithm) will be presented. 

based on the paper By J. Kleinberg, M. Sandler "Using Mixture Models for Collaborative Filtering" , STOC'04

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