The user’s filtering costs and time costs were increased because of a large number of search results in the context of big data technology. Therefore, personalized recommendation technology is needed to improve the utilization ratio of information resources, which carries out accurate and efficient information filtering. This paper focuses on the employment recommendation algorithm hoping to maximize the true intention of job applicants in the recommended job list. As an important tool in machine learning, ensemble learning can improve the prediction accuracy and adaptability effectively in job recommendation algorithms. The main work of this paper is to carry out some research on the algorithm concept related to recommendation algorithm and gradient promotion decision tree algorithm based on recommendation algorithm, and build an employment recommendation algorithm. The employment recommendation algorithm is evaluated by the TOPN list experiment.


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    Title :

    Employment Recommendation Algorithm Based on Ensemble Learning


    Contributors:
    Lin, Yubei (author) / Huang, Yinhua (author) / Chen, Pingping (author)


    Publication date :

    2019-10-01


    Size :

    140116 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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