Human resource recommendation algorithm based on ensemble learning and Spark

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 cir...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 887; no. 1; pp. 12048 - 12054
Main Authors Cong, Zihan, Zhang, Xingming, Wang, Haoxiang, Xu, Hongjie
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.08.2017
Subjects
Algorithms
Ensemble learning
Human resources
Machine learning
Parallel processing
Physics
Recommender systems
Teaching methods
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/887/1/012048