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Item-based top-n recommendation algorithms

by: M. Deshpande, G. Karypis
(2004)  Key: citeulike:1998071

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Abstract

this paper we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation...


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