Job recommendation in askstory: Experiences, methods, and evaluation

Citations

SCOPUS

6

초록

AskStory is an e-recruitment site that maintains a large number of resumes and job openings. Job seekers in AskStory have difficulty in finding proper job openings that she/he is likely to be interested in. We discuss an approach to recommend job openings to jobs seekers. We identify the properties of the dataset used in job recommendation, discover the problems caused by the properties, and propose the methods for alleviating the problems. We evaluate our approach through extensive experiments. The results show that our approach is effective in alleviating the problems and provides recommendation accuracy satisfactory to job seekers.

키워드

E-recruitment sitesJob matchingJob recommendationJob matchingJob recommendationJob seekersJob-openingsRecommendation accuracyComputation theory
제목
Job recommendation in askstory: Experiences, methods, and evaluation
저자
Lee, Yeon-ChangHong, JiwonKim, Sang-Wook
DOI
10.1145/2851613.2851862
발행일
2016-04
유형
Conference Paper
저널명
Proceedings of the ACM Symposium on Applied Computing
04-08-April-2016
페이지
780 ~ 786