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How does high dimensionality affect collaborative filtering?
2009
Proceedings of the third ACM conference on Recommender systems - RecSys '09
A crucial operation in memory-based collaborative filtering (CF) is determining nearest neighbors (NNs) of users/items. This paper addresses two phenomena that emerge when CF algorithms perform NN search in high-dimensional spaces that are typical in CF applications. The first is similarity concentration and the second is the appearance of hubs (i.e. points which appear in k-NN lists of many other points). Through theoretical analysis and experimental evaluation we show that these phenomena are
doi:10.1145/1639714.1639771
dblp:conf/recsys/NanopoulosRI09
fatcat:j6o54f4fvje33dqagrqn54jrzq