Sketching techniques for collaborative filtering

Yoram Bachrach*, Ely Porat, Jeffrey S. Rosenschein

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Scopus citations


Recommender systems attempt to highlight items that a target user is likely to find interesting. A common technique is to use collaborative filtering (CF), where multiple users share information so as to provide each with effective recommendations. A key aspect of CF systems is finding users whose tastes accurately reflect the tastes of some target user. Typically, the system looks for other agents who have had experience with many of the items the target user has examined, and whose classification of these items has a strong correlation with the classifications of the target user. Since the universe of items may be enormous and huge data sets are involved, sophisticated methods must be used to quickly locate appropriate other agents. We present a method for quickly determining the proportional intersection between the items that each of two users has examined, by sending and maintaining extremely concise "sketches" of the list of items. These sketches enable the approximation of the proportional intersection within a distance of ε, with a high probability of 1 - δ. Our sketching techniques are based on random min-wise independent hash functions, and use very little space and time, so they are well-suited for use in large-scale collaborative filtering systems.

Original languageAmerican English
Title of host publicationIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages6
ISBN (Print)9781577354260
StatePublished - 2009
Event21st International Joint Conference on Artificial Intelligence, IJCAI 2009 - Pasadena, United States
Duration: 11 Jul 200916 Jul 2009

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Conference21st International Joint Conference on Artificial Intelligence, IJCAI 2009
Country/TerritoryUnited States


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