Recsplorer: Recommendation Algorithms Based on Precedence Mining ACM SIGMOD Conference

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

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

Approach 4

Approach_Collaborative Filtering 5

Approach_ Precedence relationships 6

definition 7

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Top-k Recommendation Problem 10

RECOMMENDATION ALGORITHMS 11

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

RECOMMENDATION ALGORITHMS 13

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

RECOMMENDATION ALGORITHMS 15

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

RECOMMENDATION ALGORITHMS 17

RECOMMENDATION ALGORITHMS 18

RECOMMENDATION ALGORITHMS 19

RECOMMENDATION ALGORITHMS 20

RECOMMENDATION ALGORITHMS 21

RECOMMENDATION ALGORITHMS 22

EVALUATION 23

EVALUATION 24

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