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Domain Integration Techniques for Hidden Cluster Discovery in Collaborative Filtering

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 Hidden Cluster Discovery in Collaborative Filtering

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  1. Domain Integration Techniques for Discovering Hidden Clusters using Collaborative Filtering Brandy Brewster

  2. What is a Recommender System? • Existing information • Ratings • Prediction • User/Item pairs

  3. How do they work? • Collaborative Filtering • Rating-to-Rating • Amazon.com • Semantic/Content Based Filtering • Item-to-Item • Netflix.com

  4. Caveats • Shortcomings of current algorithms • Netflix 8.5% improvement • Scarcity of data

  5. The Data • GroupLens • MovieLens • Book-Crossing • Baylor Library • Marc Records • Amazon • Web Services

  6. The Data • GroupLens • MovieLens • Book-Crossing • Baylor Library • Marc Records • Amazon • Web Services

  7. The Data • GroupLens • MovieLens • Book-Crossing • Baylor Library • Marc Records • Amazon • Web Services

  8. Domain Integration

  9. Testing the System • Data Set • 278,858 users total • Test Set • 27,081

  10. Measuring the Results • Mean Absolute Error • Coverage

  11. Preliminary Results

  12. Future Work

  13. Questions?

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