1. Recsplorer: Recommendation Algorithms Based on Precedence Mining ACM SIGMOD Conference 2010 1

2. Outline  Introduction  Approach  Algorithms Popularity Algorithm Single Item Max-Confidence Algorithm Joint Probabilities Algorithm Approximation Joint Probabilities Support Variant Joint Probabilities Hybrid Variant Joint Probabilities Hybrid Reranked Variant  Evaluation Conclusions 2

3. Introduction  Recommender systems provide advice on products, movies…,and so on.  collaborative filtering (CF) without regard to order few items are rated by few users  precedence mining based on temporal does not suffer from the sparsity of ratings problem 3

4. Approach 4

5. Approach_Collaborative Filtering 5

6. Approach_ Precedence relationships 6

7. definition 7

8. 8

9. 9

10. Top-k Recommendation Problem 10

11. RECOMMENDATION ALGORITHMS 11

12. example  D = {a, b, c, d}  n = 50 students 12

13. RECOMMENDATION ALGORITHMS 13

14. example  D = {a, b, c, d}, T={a, b}  n = 50 students 14

15. RECOMMENDATION ALGORITHMS 15

16. example  D = {a, b, c, d}, T={a, b}  n = 50 students 16

17. RECOMMENDATION ALGORITHMS 17

18. RECOMMENDATION ALGORITHMS 18

19. RECOMMENDATION ALGORITHMS 19

20. RECOMMENDATION ALGORITHMS 20

21. RECOMMENDATION ALGORITHMS 21

22. RECOMMENDATION ALGORITHMS 22

23. EVALUATION 23

24. EVALUATION 24

25. CONCLUSIONS  The Single Item Max Confidence approach has the highest precision when we have little information about the student.  Joint Prob. Hybrid works best with more information at hand.  we found that algorithms beat popularity-based recommendations and collaborative filtering. 25