DocumentCode :
3223285
Title :
Researches on the best-fitted talents recommendation algorithm
Author :
Shijun Yao ; Zhuo Yi ; Liang Zhang
Author_Institution :
Sixth Fac. of Inf. Eng., Zhengzhou Univ., Zhengzhou, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
4247
Lastpage :
4252
Abstract :
Talent allocation, as a vital part of talent strategies, plays an important role in improving the utilization of talents. However, it has encountered the problems, such as the lack of talent supply, low rate of talent utilization, etc. To realize the optimal allocation of talents, a best-fitted talent optimal allocation mechanism is proposed. In this mechanism, the concepts of talent-post match degree and talent utilization rate are introduced as evaluation criterion of talent optimal allocation. Meanwhile, time sequence model is applied to explore the distribution law of talents and predict number of talents in the future. Based on the talent demand and the number of talents predicted by time sequence model, a dynamic planning algorithm is adopted after formula derivation to recommend best-fitted talents list. Experimental results show that this best-fitted talent recommendation mechanism possesses higher utilization and is of use to the government department in talent management.
Keywords :
human resource management; recommender systems; best-fitted talent optimal allocation mechanism; best-fitted talents recommendation algorithm; dynamic planning algorithm; evaluation criterion; government department; talent allocation; talent management; talent strategy; talent utilization; talent utilization rate; talent-post match degree concept; talents distribution law; time sequence model; Education; Heuristic algorithms; Indexes; Nickel; Prediction algorithms; Predictive models; Resource management; Best-fitted; Talent Distribution; Talent Forecast; Talent Recommendation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162676
Filename :
7162676
Link To Document :
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