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[IEEE 2019 International Vacuum Electronics Conference (IVEC) - Busan, Korea (South) (2019.4.28-2019.5.1)] 2019 International Vacuum Electronics Conference (IVEC) - Notice of Removal: Design of Coaxial Waveguide TEM to Circular Waveguide TM <sub/>0n</sub> Mode Transducer
摘要: An inherently non-negative latent factor model is proposed to extract non-negative latent factors from non-negative big sparse matrices efficiently and effectively. A single-element-dependent sigmoid function connects output latent factors with decision variables, such that non-negativity constraints on the output latent factors are always fulfilled and thus successfully separated from the training process with respect to the decision variables. Consequently, the proposed model can be easily and fast built with excellent prediction accuracy. Experimental results on an industrial size sparse matrix are given to verify its outstanding performance and suitability for industrial applications.
关键词: Latent factors,recommender system,non-negative big sparse matrix,non-negativity,big data,matrix factorization
更新于2025-09-16 10:30:52