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Fast top-k similarity queries via matrix compression
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
In this paper, we propose a novel method to efficiently compute the top-K most similar items given a query item, where similarity is defined by the set of items that have the highest vector inner products with the query. The task is related to the classical k-Nearest-Neighbor problem, and is widely applicable in a number of domains such as information retrieval, online advertising and collaborative filtering. Our method assumes an in-memory representation of the dataset and is designed to scale
doi:10.1145/2396761.2398574
dblp:conf/cikm/LowZ12
fatcat:3uxmzb5ubrandkcaysdliocroe