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Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a(More)
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(More)
1. INRODUCTION Jester 2.0 is a WWW-based system that allows users to retrieve jokes baaed on their ratings of sample jokes. It predicts the humor preferences of a user and presents a set of jokes that the user might find " funny ". The heart of the recommendation scheme is a new principal component analysis (PCA) and clustering-based linear time(More)
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