Analysis for National Natural Science Foundation of China Funded Projects on Recommender System Research Area

doi:10.3778/j.issn.1002-8331.1801-0249

Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (9): 259-263.DOI: 10.3778/j.issn.1002-8331.1801-0249

Previous Articles Next Articles

Analysis for National Natural Science Foundation of China Funded Projects on Recommender System Research Area

CHU Hanting1, XING Xing1，2, MENG Zhixin1, JIA Zhichun1

1.College of Information Science and Technology, Bohai University, Jinzhou, Liaoning 121013, China
2.Center for Science Communication, National Natural Science Foundation of China, Beijing 100085, China

Online:2019-05-01 Published:2019-04-28

国家自然科学基金推荐系统研究领域资助分析

楚涵婷1，邢星1，2，孟志鑫1，贾志淳1

1.渤海大学信息科学与技术学院，辽宁锦州 121013
2.国家自然科学基金委员会科学传播中心，北京 100085

Abstract

Abstract: The paper statistics funding situation in the national natural science foundation of recommended system area about surface projects and youth projects in the past five years and comparison of the funding of various projects in the recommended system area and analyzes the distribution of subsidy projects supported by NSFC’s recommendation system and summarizes the characteristics of research funded projects that provides reference for the application of the researchers’ natural science fund project in this field.

Key words: National Natural Science Foundation of China（NSFC）, funding situation, recommended system, project funded, statistical analysis

摘要： 通过统计近五年国家自然科学基金推荐系统领域中面上项目、青年项目的资助情况和该领域中其他项目的资助情况，分析国家自然科学基金推荐系统领域资助项目依托单位的分布情况和该领域中资助项目的研究特点，总结推荐系统研究领域的研究特点，为推荐系统领域的相关研究人员申请国家自然科学基金项目和开展下一步研究提供参考。

关键词: 国家自然科学基金, 资助情况, 推荐系统, 项目资助, 统计分析

CHU Hanting1, XING Xing1，2, MENG Zhixin1, JIA Zhichun1. Analysis for National Natural Science Foundation of China Funded Projects on Recommender System Research Area[J]. Computer Engineering and Applications, 2019, 55(9): 259-263.

楚涵婷1，邢星1，2，孟志鑫1，贾志淳1. 国家自然科学基金推荐系统研究领域资助分析[J]. 计算机工程与应用, 2019, 55(9): 259-263.

[1]	CAI Nian, LIU Guangcong, CAI Hongdan. Improved Model Combining Improved Matrix Decomposition and Convolutional Neural Networks [J]. Computer Engineering and Applications, 2019, 55(19): 178-184.
[2]	LUO Jun, ZHU Wenqi. User similarity function considering weight of items similarity [J]. Computer Engineering and Applications, 2015, 51(8): 123-127.
[3]	LU Chun1, HONG Anbang2, GONG Jian1. Research on collaborative filtering recommendation method based on PSO algorithm [J]. Computer Engineering and Applications, 2014, 50(5): 101-107.
[4]	LI Gai1，2，3，LI Lei2，3. Collaborative filtering algorithm based on matrix decomposition [J]. Computer Engineering and Applications, 2011, 47(30): 4-7.
[5]	LENG Lu^1，2，LI Ming¹，ZHANG Jia-shu². 2D histogram segmentation based on graphic statistical analysis [J]. Computer Engineering and Applications, 2010, 46(4): 156-158.
[6]	ZHANG Su-mei. Statistical analysis software driven reconfigurable SPC system [J]. Computer Engineering and Applications, 2008, 44(18): 203-206.
[7]	WU Qing-tao¹,ZHANG Chun-yang¹,SHAO Zhi-qing²,LIU Bai-xiang². Detecting distributed denial-of-service attacks based on statistical analysis [J]. Computer Engineering and Applications, 2007, 43(33): 167-169.

Analysis for National Natural Science Foundation of China Funded Projects on Recommender System Research Area

国家自然科学基金推荐系统研究领域资助分析

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 7

Recommended Articles

Metrics