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Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
A prominent approach in collaborative filtering based recommender systems is using dimensionality reduction (matrix factorization) techniques to map users and items into low-dimensional vectors. In such systems, a higher inner product between a user vector and an item vector indicates that the item better suits the user's preference. Traditionally, retrieving the most suitable items is done by scoring and sorting all items. Real world online recommender systems must adhere to strict
doi:10.1145/2645710.2645741
dblp:conf/recsys/BachrachFGKKNP14
fatcat:qwptdh3mefcmjkvpe4y34ca2jm