{"title":"生存随机森林预测时间填充","authors":"Summer M. Husband, J. Roberts","doi":"10.1109/ICDMW.2017.32","DOIUrl":null,"url":null,"abstract":"Traditionally, the time-to-fill metric is used as a scorecard for past performance. An organization may use time to fill to assess the performance of its internal recruiting team, or as a way to set service level agreements with outsourced recruiting partners. By first developing a set of quantifiable job features and then applying survival analysis to historical time-to-fill data, we build a predictor to assess the probability a job will remain open beyond its target time-to-fill date, enabling us to commit additional resources to high risk jobs at the beginning of the recruiting process.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Survival Random Forest to Predict Time to Fill\",\"authors\":\"Summer M. Husband, J. Roberts\",\"doi\":\"10.1109/ICDMW.2017.32\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditionally, the time-to-fill metric is used as a scorecard for past performance. An organization may use time to fill to assess the performance of its internal recruiting team, or as a way to set service level agreements with outsourced recruiting partners. By first developing a set of quantifiable job features and then applying survival analysis to historical time-to-fill data, we build a predictor to assess the probability a job will remain open beyond its target time-to-fill date, enabling us to commit additional resources to high risk jobs at the beginning of the recruiting process.\",\"PeriodicalId\":389183,\"journal\":{\"name\":\"2017 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2017.32\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2017.32","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Traditionally, the time-to-fill metric is used as a scorecard for past performance. An organization may use time to fill to assess the performance of its internal recruiting team, or as a way to set service level agreements with outsourced recruiting partners. By first developing a set of quantifiable job features and then applying survival analysis to historical time-to-fill data, we build a predictor to assess the probability a job will remain open beyond its target time-to-fill date, enabling us to commit additional resources to high risk jobs at the beginning of the recruiting process.