- 标题
- 摘要
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Analysis of the Asymptotic Behavior of the Local Ideality Factor
摘要： With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative ?ltering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we ?rst propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classi?ed as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework.
关键词： Mobile Internet,collaborative ?ltering,recommender system
更新于2025-09-23 15:19:57
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Low-Cost Medical Diagnostics Exploiting Different Kinds of Receptors on Plasmonic Plastic Optical Fiber Sensors
摘要： With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative ?ltering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we ?rst propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classi?ed as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework.
关键词： collaborative filtering,Mobile Internet,recommender system
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE CPMT Symposium Japan (ICSJ) - Kyoto, Japan (2019.11.18-2019.11.20)] 2019 IEEE CPMT Symposium Japan (ICSJ) - Single-mode glass waveguide substrate for PIC packaging
摘要： With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative ?ltering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we ?rst propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classi?ed as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework.
关键词： Mobile Internet,collaborative ?ltering,recommender system
更新于2025-09-19 17:13:59
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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