The problem of employment is a significant livelihood issue in today's society. A large amount of unemployed persons do not know how to choose works,and have no ideas about learning what kinds of professional knowledge about their ideal works. Nevertheless,libraries have the advantages of information organization,information management and information consultation and have the ability of analyzing and researching massive recruitment information. This paper proposes personalized services of libraries for employment,and builds a market-oriented library resources organization system for employment. With the using of some algorithms of data mining and intelligent Web,it could recommend the latest and most suitable library resources that meet the current market demands for vocational skills,and help users enhance their employ abilities. It also can improve the utilization of library resources,and enhance the personalized service levels of libraries.
SUN Hui,BAI Yang,LI Cheng-long
. The Research of Recommendation of Library Resources Based on the Orientation of Employment Market[J]. Journal of Library and Information Science in Agriculture, 2014
, 26(11)
: 14
-18
.
DOI: 10.13998/j.cnki.issn1002-1248.2014.11.003
[1] Cohen S,Fereira J,et al.Personalized electronic services in the Cornell University Library[J].D-Lib Magazine,2000,6(4):1082-9873.
[2] Schafer J B,Konstan J A,Riedl J.E-commerce recommendation applications[M].Applications of Data Mining to Electronic Commerce.Springer US.2001:115-153.
[3] Koren Y. Tutorial on recent progress in collaborative filtering[J].RecSys.2008,(8):333-334.
[4] Yu K,Schwaighofer A,Tresp V,Kriegel H P.Probabilistic memory-basedcollaborative filtering[J].IEEE Trans.On Knowledge and Data Engineering.2004,16(1):56-69.
[5] Rashid A M,Albert I,Cosley D,et al.Getting to know you: learning new user preferences in recommender systems[C].Proceedings of the 7th international conference on Intelligent user interfaces,ACM.2002:127-134.
[6] Gori M,Pucci A.ItemRank:A Random-Walk Based Scoring Algorithm for Recommender Engines[C].IJCAI.2007,7:2766-2771.
[7] Schafer J B,Frankowski D,Herlocker J,et al.Collaborative filtering recommender systems[M].The adaptive web. Springer Berlin Heidelberg.2007:291-324.
[8] Zhang Y,Callan J,Minka T.Novelty and redundancy detection in adaptive filtering[C].Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval.ACM.2002:81-88.
[9] 谷远勇.从信息不对称理论视角看就业信息对大学生就业观的影响[J].中国电力教育,2012,(1):135-136,138.
[10] 张继明,吴智鹏.高等教育信息不对称对策研究——高校信息公开的视角[J].教育学术月刊,2010,(11):66-69.
[11] 周军,王艳红.一种基于词典的中文分词法的设计与实现[J].黑龙江科技信息,2008,(25):70-70.
[12] 郑逢斌,付征叶,乔保军,等.HENU汉语自动分词系统中歧义字段消除算法[J].河南大学学报:自然科学版,2005,34(4):49-52.
[13] Han Jianwei.Data mining concepts and techniques[M].北京:机械工业出版社,2001,30-50.
[14] 贾巍,瞿垄.知识表示视野下网络课程知识点关系研究.继续教育研究,2008,(5),62-64.
