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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.

DandyID provides a set of tools that help you to collect, manage, and own your online identity. Just like what defines you in the offline world is a collection of the things you do, the people you interact with, and the places you go, your online self is a collection of the things you do and the people you meet across the web. Unfortunately because the web is so big, that identity is usually scattered around a bunch of different sites that don't always communicate with one another. DandyID helps you bring together all the pieces that make up the real you, and share the bits you want to share.

social networking site for researchers