1. Recommender Systems Based Rajaraman and Ullman: Mining Massive Data Sets & Francesco Ricci et al. Recommender Systems Handbook.

2. Recommender System

4. RS – Major Approaches 135 144 423 354 443

5. RS – Approaches

7. Collaborative Filtering

8. Key concepts/questions How is user f/b expressed: ratings or implicit? How to measure similarity? How many nearest neighbors to pick (if memory- or neighborhood-based). How to predict unknown ratings? Distinguished (also called active) user and (target) item.

9. A Naïve Algorithm (memory-based)

10. An Example

12. Prediction using Memory/Neighborhood- based approaches

13. User-User vs Item-Item.

14. Simpler Alternatives for Rating Estimation

15. Item-based CF

16. Item-based CF Computation Illustrated Similarities: computing sim. b/w all pairs of items is prohibitive! But do we need to? How efficiently can we compute the sim. of all pairs of items for which the sim. Is positive? XXXXXXXX …

17. Item-based CF – Recommendation Generation XXXXXXXX X X X X X similar items? How efficiently can we generate recommendations for a given user?

18. Some empirical facts re. user-based vs. item-based CF User profiles are typically thinner than item profiles; depends on application domain. – Certainly holds for movies (Netflix).  as users provide more ratings, user-user sim. can chage more dyamically than item-item sim. Can we precompute item-item sim. and speed up prediction computation? What about refreshing sim. against updates? Can we do it incrementally? How often should we do this? Why not do this for user-user?

19. User & Item-based CF are both personalized Non-personalized would estimate an unknown rating as a global average. Every user gets the same recommendation list, modulo items s/he may have already rated. Personalized clearly leads to better predictions.