GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A small approximately min-wise independent family of hash functions
Journal of Algorithms
An Approximate L1-Difference Algorithm for Massive Data Streams
SIAM Journal on Computing
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
International Journal of Approximate Reasoning
Sketching techniques for collaborative filtering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Fingerprinting ratings for collaborative filtering: theoretical and empirical analysis
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Exponential time improvement for min-wise based algorithms
Information and Computation
Exponential time improvement for min-wise based algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Bottom-k and priority sampling, set similarity and subset sums with minimal independence
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Sketching for big data recommender systems using fast pseudo-random fingerprints
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
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Collaborative filtering (CF) shares information between users to provide each with recommendations. Previous work suggests using sketching techniques to handle massive data sets in CF systems, but only allows testing whether users have a high proportion of items they have both ranked. We show how to determine the correlation between the rankings of two users, using concise "sketches" of the rankings. The sketches allow approximating Kendall's Tau, a known rank correlation, with high accuracy *** and high confidence 1 *** *** . The required sketch size is logarithmic in the confidence and polynomial in the accuracy.