1 / 13

Domain Integration Techniques for Discovering Hidden Clusters using Collaborative Filtering

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

medea
Download Presentation

Domain Integration Techniques for Discovering Hidden Clusters using Collaborative Filtering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related