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• W3034100983 abstract "In the modern world, people face an explosion of information and difficulty finding the right choice for their interests. Nowadays, people prefer online shopping for their needs. Recently, the recommender system has become one of the key technology for the online purchasing system. The collaborative filtering technique has been extensively applied for the Recommender Systems. However, collaborative filtering is suffering from data sparsity, cold start problems, and inaccuracy problems. To overcome these problems, we propose a novel approach of the Matrix Distributive collaborative filtering with ensemble integration. The experimental results illustrate the increase in performance against the existing methods." @default.
• W3034100983 created "2020-06-12" @default.
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• W3034100983 date "2020-04-24" @default.
• W3034100983 modified "2023-10-18" @default.
• W3034100983 title "Large-scale Distributive Matrix Collaborative Filtering for Recommender System" @default.
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• W3034100983 doi "https://doi.org/10.1145/3398329.3398360" @default.
• W3034100983 hasPublicationYear "2020" @default.
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