1. Rachid Guerraoui, EPFL

2. What is a good recommendation system? Recommendation systems are good

3. A good recommendation system is one that provides good recommendations What is a good recommendation?

4. You know it when you see it “ I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand description ["hard-core pornography"]; and perhaps I could never succeed in intelligibly doing so. But I know it when I see it, and the motion picture involved in this case is not that” Justice Potter Stewart, US Supreme Court, 1964

7. Ideally: Build and deploy your system Pragmatic: Transform past into future What is a good recommendation system ?

8. Example Members of program committee (20) want to evaluate the submitted papers (200) Nobody has enough time to read all papers Each researcher is assigned a subset of papers A recommendation system uses the scores to find the opinion of all members about all papers

9. What is a good recommendation? It depends on the correlation Theory to the rescue

10. n users k * n objects For each user and object: a grade – The grades of a user form his preference vector – The vectors of users form the preference matrix – Grades may be binary, discrete, continuous General recommendation model

11. Vectors of grades: v(p) (known partially to the players) Input? Vectors of grades: w(p) (seeking to approximate v(p)) Output?

12. Ideal output Target output w(p) = v(p) Minimize max |w(p)-v(p)| (Hamming distance)

13. Compare with a perfect on-line algorithm How to account for the level of correlation?

14. Shared billboard (1) All players know all partial vectors The perfect on-line algorithm

15. (2) Chooses elements of the partial vectors to fill (B budget) The player is initially indulgent (learning phase) The algorithm assigns initial papers

16. (3) Knows the level of correlation Hamming diameter of a set P The perfect on-line algorithm

17. 20 pc members; 200 papers Every member can read 10 papers All have the same taste Perfect solution possible?

18. 20 pc members; 200 papers Two clusters of 10 have the same taste Perfect solution possible? Every member needs to read 20

19. Assume player p can probe B objects n/B*k – 1 How many other players does p need to collaborate with to fill its vector?

20. 20 pc members; 200 papers 4 clusters of 5 with diameter 8 Every member reads 20 What is the minimal error rate?

21. Ideal algorithm (k=1) A player p has to use ideas of (n/B)-1 other players to estimate her/his preferences In the worst case, p cannot do better The rate of error for p depends on the hamming distance between p and the other (n/B) players This is with a constant factor of the diameter of these n/B players

22. Claim For every B-algorithm, there is some distribution of preferences such that (with constant probability)

23. Proof (sketch) Consider a constant D > 2B Define a preference vector as follows: Let P be a set of players of size n/B - Let p in P with a random preference vector -Assign a random preference vector outside P Choose a set S of D objects. For every player q in P, v(q)=v(p) except in S which is random

24. Proof (sketch) Probes outside P provide no information to p Probes inside P provide no information to p w.r.t S Since p probes at most B objects and S contains D > 2B objects, there are at least D/2 objects for which p has no information No algorithm can do better than guess preferences in S The rate of error is at least D/4 and the diameter of P is less than D

25. Optimality An algorithm is (B,c)-optimal if for every input set of preferences

26. So what? The best we can do is find clusters of players that are - Small enough (small diameter) to provide “accurate” preferences And - Big enough to cover all objects Practically speaking? - Try different sizes of clusters

27. Optimality Assume each player can evaluate B objects. Given B, and the level of correlation among players, there is a minimum rate of error that can be achieved. There is an algorithm that obtains a constant approximation of this error-rate, and each player evalutes O(B.Polylog(n)) objects.

28. Definition of Optimality An algorithm is asymptotically optimal in terms of error rate, if for every player p we have: |w(p)-v(p)| n/B-1 cD(P) Where c is a constant and D(P) is the diameter of set P. P can be any set of players with size at least n/B.