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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. Eigentaste was patented by UC Berkeley in 2003. It has many possible applications, such as the recommendation of books, movies, toys, stocks, and music. It was originally used in an online joke recommendation system called Jester, which recommends new jokes to users based on their ratings of an initial set.