FISM: factored item similarity models for top-N recommender systems


The effectiveness of existing top-<i>N</i> recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-<i>N</i> recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned… (More)
DOI: 10.1145/2487575.2487589
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