Module 1

Collaborative Filtering Manual A person who sends emails Automatic Historically U. Minesota GroupLens Project MIT FireFly Music BellCore Video recommender

Movie Lens Ratings are stored in database. Correlations for users are calculated.

Interfaces of recommenders: Filtering Recommender Prediction


Java Maven LensKit InteliJ

Analytical Framework for analysis of recommendation systems

  1. What are we recommending?

Text? Products or bundles? Other people? Sequences?

New items or re-recommending old ones.

  1. Purpose of recommendation?

  2. Recommendations themselves

  3. Education
  4. Communitity building

ReferalWeb recomends another user in one's social graph based on request.

  1. What the user is doing at the time of recommendation?

  2. Whose opinion?

  3. Experts

  4. Ordinary people
  5. People like me

  6. Personalization level

  7. Generic

  8. Based on demographics
  9. Matching on current activity
  10. Matching long-term interests

  11. Privacy and Trustworthiness

  12. Who wants to know about me?

  13. Is the recommendation honest?

  14. Interfaces

  15. Types of output

  16. Types of input