1. Recommender Systems Eric Nalisnick CSE 435

3. …

4. How can businesses direct customers to groups of similar, interesting, relevant, and undiscovered items?

5. Recommender Systems!

6. Method #1: Memory-Based Collaborative Filtering

7. ABCDE

8. = 0 1 0 0 C

9. PieIce CreamSoupEgg Rolls A1100 B1100 C0100 D0011 E0010 Customer—Item Matrix

11. = 0 0 B

12. PieIce CreamSoupEgg Rolls A5100 B2500 C0400 D0033 E0040 Sim.44 2.13 - 0 Customer—Item Matrix with User Reviews

13. Evaluation of Memory-Based Collaborative Filtering

14. 1. Best for post-purchase recommendations.

15. 2. Does not scale well. Customers Items

16. 3. Very popular and very unpopular items are problematic. *In practice, can multiply values by inverse frequency

17. 4. Cold Start Problem How do we recommend new items? How do we make recommendations for new users?

18. 5. Susceptible to Black and Gray Sheep

19. Method #2: Knowledge-Based Collaborative Filtering

20. Like traditional CBR systems…

21. Similarity function?

23. 15 13 17 9 1 7 12

24. *Director, year, and color had unstable or negative weights.

25. Evaluation of Knowledge- Based Collaborative Filtering

26. 1. Better at pre-purchase recommendations than Memory-Based.

27. 2. Efficient runtime. Can be as simple as descending K-D Tree.

28. 3. Cold Start problem and popularity of an item are not an issue.

29. 4. Not good at modeling the general preferences of a user.

30. Method #3: Hybrid Item-to-Item Collaborative Filtering

31. ABCDE

33. Item-to-Item Collaborative Filtering Algorithm For each item i 1 : For each customer c who has bought i 1 : For each item i 2 bought by c: Sim(i 1, i 2 )

34. PieIce CreamSoupEgg Rolls A1100 B1100 C0100 D0011 E0010 Customer—Item Matrix

35. Industry Example: The Netflix Prize

36. $1,000,000 prize

37. Winning Team: “Bellkor’s Pragmatic Chaos” RMSE Reduction: 10.9%

38. Lessons Learned… 1. Baseline Predictors

39. Lessons Learned… 2. Binary view of Data: Rated or not rated.

40. Lessons Learned… 3. Restricted Boltzmann Machines.

42. Lessons Learned… 4. No one recommendation technique is best. Need to combine several.

43. Summary 1.Memory-Based CF is best for post-purchase 2.Knowledge-Based CF is best for pre-purchase. 3.Hybrid methods generally work best 4.The data is as important as the algorithm

44. Questions?