Jester 2.0 : Evaluation of a New Linear Time Collaborative Filtering Algorithm

Jester is a WWW-based system that allows users to retrieve jokes based on their ratings of sample jokes. Our emphasis is on a new principal component analysis (PCA) and clustering-based linear time collaborative filtering algorithm for efficient and effective personalized information retrieval. Let m be the number of users in the database (currently over 12000) and n be the number of jokes rated by a user to characterize his or her preference (currently 10). We report new results comparing Jester 1.0ʹs 0(nm) algorithm with Jester 2.0ʹs 0(n) algorithm: the latter improves the retrieval effectiveness by more than 40% and reduces retrieval time by a factor of 12,000.