Online job vacancy attractiveness: Increasing views, reactions and conversions
作者：
Highlights：
• We study the predictors of online job posting attractiveness: views, reactions and conversions.
• In terms of the MSE and MAE, the linear models do not perform well.
• Random forest consistently outperforms the benchmark and linear models.
• Variable importance is tested within an MCS approach.
• Job classification and benefits help predict attractiveness.
• The morphological characteristics of the job title are also a useful predictor.
摘要
•We study the predictors of online job posting attractiveness: views, reactions and conversions.•In terms of the MSE and MAE, the linear models do not perform well.•Random forest consistently outperforms the benchmark and linear models.•Variable importance is tested within an MCS approach.•Job classification and benefits help predict attractiveness.•The morphological characteristics of the job title are also a useful predictor.
论文关键词：Online job vacancy,Webpage attractiveness,Webpage views,Machine learning,E-recruitment,Morphological text analysis
论文评审过程：Received 21 January 2022, Revised 23 June 2022, Accepted 10 August 2022, Available online 19 August 2022, Version of Record 26 August 2022.
论文官网地址：https://doi.org/10.1016/j.elerap.2022.101192
