关键词: 用户活跃度预测, 在线招聘, 分类, 数据分析

Abstract: In the era of Internet, a lot of recruitments happen on online recruitment platforms. These platforms recommend jobs for applicants and meanwhile recommend resumes to corporations. However, it is almost impossible for the platforms to know whether the applicant has found a job. As a result, resumes are still being recommended to corporations even if the applicant has found a job, which leads to a waste of the platform resource as well as unsatisfactory user experience. This paper formalizes the resume activeness prediction problem in online recruitment scenarios, which aims to find highly active applicants so that the platform recommends the active ones and discards the inactive ones and therefore escalates user experience. Current solutions for user activity level predication are often restricted to certain scenarios like social networks, and they utilize scenario-aware features. Unfortunately, these features are not applicable in online recruitment scenarios. With a careful study of real-world recruitment data, this paper summarizes four characteristics of online recruitment scenarios, which are hyper-dynamism, low user viscosity, bidirectional matching and the priority of recall over precision in the predication result. Based on these characteristics, this paper proposes a model named RAP (resume activeness prediction), which carefully handles these characteristics and also provides a parameter γ to deal with the priority of recall. The extensive experiments on real-world data from 58 Recruitment Website demonstrate that the AUC of RAP can achieve 0.817.

Key words: user activeness prediction, online recruitment, classification, data analysis