国家自然科学基金推荐系统研究领域资助分析
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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。
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.
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.
引文
[1]Ricci F,Rokach L,Shapira B,et al.Recommender systems handbook[M].US:Springer,2011.
[2]Resnick P,Varian H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[3]Adomavicius G,Tuzhilin A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[4]Park D H,Kim H K,Choi I Y,et al.A literature review and classification of recommender systems research[J].Expert Systems with Applications,2012,39(11):10059-10072.
[5]Konstas I,Stathopoulos V,Jose J M.On social networks and collaborative recommendation[C]//Proceedings of the32nd International ACM SIGIR Conference on Research and Development in Information Retrieval,Boston,USA,Jul 19-23,2009.New York,NY,USA:ACM,2009:195-202.
[6]Quijano-Sanchez L,Recio-Garcia J A,Diaz-Agudo B,et al.Social factors in group recommender systems[J].ACM Transactions on Intelligent Systems&Technology,2013,4(1).
[7]Zhang D,Hsu C H,Chen M,et al.Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems[J].IEEE Transactions on Emerging Topics in Computing,2017,2(2):239-250.
[8]Guo L,Ma J,Chen Z M,et al.Learning to recommend with social relation ensemble[C]//Proceedings of the ACMInternational Conference on Information and Knowledge Management.[S.l.]:ACM Press,2012:2599-2602.
[9]任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017(1):28-51.
[10]孙光福,吴乐,刘淇,等.基于时序行为的协同过滤推荐算法[J].软件学报,2013,24(11):2721-2733.
[11]周光礼.“双一流”建设中的学术突破--论大学学科、专业、课程一体化建设[J].教育研究,2016,37(5):72-76.
[12]国家自然科学基金项目指南[EB/OL].(2017).http://www.nsfc.gov.cn/nsfc/cen/xmzn/2017xmzn/index.html.
[13]Wang H,Wang N,Yeung D Y.Collaborative deep learning for recommender systems[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2015:1235-1244.
[14]Sarwar B,Karypis G,Konstan J,et al.Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web,2001:285-295.
[15]Lin H W,Tegmark M,Rolnick D.Why does deep and cheap learning work so well?[J].Journal of Statistical Physics,2017,168(6):1-25.
[16]王智圣,李琪,汪静,等.基于隐式用户反馈数据流的实时个性化推荐[J].计算机学报,2016(1):52-64.
[17]Le H S.Dealing with the new user cold-start problem in recommender systems:a comparative review[J].Information Systems,2016,58(5):87-104.
[18]Covington P,Adams J,Sargin E.Deep neural networks for youtube recommendations[C]//Proceedings of the10th ACM Conference on Recommender Systems,2016:191-198.
[19]Wei J,He J,Chen K,et al.Collaborative filtering and deep learning based recommendation system for cold start items[J].Expert Systems with Applications,2017,69:29-39.
[20]黄立威,刘艳博,李德毅.基于深度学习的推荐系统[J].计算机学报,2017:1-29.
[21]Bian T,Hu J,Deng Y.Identifying influential nodes in complex networks based on AHP[J].Physica A Statistical Mechanics&Its Applications,2017,479(4):422-436.
[22]陈关荣.复杂动态网络环境下控制理论遇到的问题与挑战[J].自动化学报,2013,39(4):312-321.
[23]Barabási A,Albert R.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.
[24]刘建国,任卓明,郭强,等.复杂网络中节点重要性排序的研究进展[J].物理学报,2013,62(17):9-18.
[25]王文平,刘云,何颖,等.国际科技合作对跨学科研究影响的评价研究--基于文献计量学分析的视角[J].科研管理,2015,36(3):127-137.
[26]王立才,孟祥武,张玉洁.上下文感知推荐系统[J].软件学报,2012,23(1):1-20.
[2]Resnick P,Varian H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[3]Adomavicius G,Tuzhilin A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[4]Park D H,Kim H K,Choi I Y,et al.A literature review and classification of recommender systems research[J].Expert Systems with Applications,2012,39(11):10059-10072.
[5]Konstas I,Stathopoulos V,Jose J M.On social networks and collaborative recommendation[C]//Proceedings of the32nd International ACM SIGIR Conference on Research and Development in Information Retrieval,Boston,USA,Jul 19-23,2009.New York,NY,USA:ACM,2009:195-202.
[6]Quijano-Sanchez L,Recio-Garcia J A,Diaz-Agudo B,et al.Social factors in group recommender systems[J].ACM Transactions on Intelligent Systems&Technology,2013,4(1).
[7]Zhang D,Hsu C H,Chen M,et al.Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems[J].IEEE Transactions on Emerging Topics in Computing,2017,2(2):239-250.
[8]Guo L,Ma J,Chen Z M,et al.Learning to recommend with social relation ensemble[C]//Proceedings of the ACMInternational Conference on Information and Knowledge Management.[S.l.]:ACM Press,2012:2599-2602.
[9]任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017(1):28-51.
[10]孙光福,吴乐,刘淇,等.基于时序行为的协同过滤推荐算法[J].软件学报,2013,24(11):2721-2733.
[11]周光礼.“双一流”建设中的学术突破--论大学学科、专业、课程一体化建设[J].教育研究,2016,37(5):72-76.
[12]国家自然科学基金项目指南[EB/OL].(2017).http://www.nsfc.gov.cn/nsfc/cen/xmzn/2017xmzn/index.html.
[13]Wang H,Wang N,Yeung D Y.Collaborative deep learning for recommender systems[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2015:1235-1244.
[14]Sarwar B,Karypis G,Konstan J,et al.Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web,2001:285-295.
[15]Lin H W,Tegmark M,Rolnick D.Why does deep and cheap learning work so well?[J].Journal of Statistical Physics,2017,168(6):1-25.
[16]王智圣,李琪,汪静,等.基于隐式用户反馈数据流的实时个性化推荐[J].计算机学报,2016(1):52-64.
[17]Le H S.Dealing with the new user cold-start problem in recommender systems:a comparative review[J].Information Systems,2016,58(5):87-104.
[18]Covington P,Adams J,Sargin E.Deep neural networks for youtube recommendations[C]//Proceedings of the10th ACM Conference on Recommender Systems,2016:191-198.
[19]Wei J,He J,Chen K,et al.Collaborative filtering and deep learning based recommendation system for cold start items[J].Expert Systems with Applications,2017,69:29-39.
[20]黄立威,刘艳博,李德毅.基于深度学习的推荐系统[J].计算机学报,2017:1-29.
[21]Bian T,Hu J,Deng Y.Identifying influential nodes in complex networks based on AHP[J].Physica A Statistical Mechanics&Its Applications,2017,479(4):422-436.
[22]陈关荣.复杂动态网络环境下控制理论遇到的问题与挑战[J].自动化学报,2013,39(4):312-321.
[23]Barabási A,Albert R.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.
[24]刘建国,任卓明,郭强,等.复杂网络中节点重要性排序的研究进展[J].物理学报,2013,62(17):9-18.
[25]王文平,刘云,何颖,等.国际科技合作对跨学科研究影响的评价研究--基于文献计量学分析的视角[J].科研管理,2015,36(3):127-137.
[26]王立才,孟祥武,张玉洁.上下文感知推荐系统[J].软件学报,2012,23(1):1-20.
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