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Explore recommender systems using existing information, ratings, and prediction to understand how they work. Learn about collaborative filtering, rating-to-rating, and semantic/content based filtering. Identify caveats and current algorithm shortcomings with improvements seen in Netflix. Discover datasets from GroupLens, MovieLens, Book-Crossing, Baylor Library, and more. Test the system with data sets and measure results with mean absolute error and coverage criteria.
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Domain Integration Techniques for Discovering Hidden Clusters using Collaborative Filtering Brandy Brewster
What is a Recommender System? • Existing information • Ratings • Prediction • User/Item pairs
How do they work? • Collaborative Filtering • Rating-to-Rating • Amazon.com • Semantic/Content Based Filtering • Item-to-Item • Netflix.com
Caveats • Shortcomings of current algorithms • Netflix 8.5% improvement • Scarcity of data
The Data • GroupLens • MovieLens • Book-Crossing • Baylor Library • Marc Records • Amazon • Web Services
The Data • GroupLens • MovieLens • Book-Crossing • Baylor Library • Marc Records • Amazon • Web Services
The Data • GroupLens • MovieLens • Book-Crossing • Baylor Library • Marc Records • Amazon • Web Services
Testing the System • Data Set • 278,858 users total • Test Set • 27,081
Measuring the Results • Mean Absolute Error • Coverage