Exploiting matrix factorization to asymmetric user similarities in recommendation systems

    Knowledge-Based Systems, Volume 83, Issue C, 2015, Pages 51-57.

    Cited by: 40|Bibtex|Views25|Links
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    Abstract:

    Although collaborative filtering is widely applied in recommendation systems, it still suffers from several major limitations, including data sparsity and scalability. Sparse data affects the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity...More

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