Human resource recommendation algorithm based on ensemble learning and Spark

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Cong, Zihan^1 ; Zhang, Xingming^1 ; Wang, Haoxiang^1 ; Xu, Hongjie^1

School of Computer Science and Engineering, South China University of Technology, Guangzhou

510006, China^1

关键词: Ensemble learning;    Information overloads;    Job seekers;    Learning methods;    Recommendation algorithms;    Resource recommendation;    Resources industries;    User interest model;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012048/pdf
DOI  :  10.1088/1742-6596/887/1/012048

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Aiming at the problem of "information overload" in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.