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Job Recommendation in AskStory: Experiences, Methods, and Evaluation

Title
Job Recommendation in AskStory: Experiences, Methods, and Evaluation
Author
김상욱
Keywords
e-recruitment sites; job matching; job recommendation
Issue Date
2016-04
Publisher
ACM SAC
Citation
SAC '16 Proceedings of the 31st Annual ACM Symposium on Applied Computing, Page. 780-786
Abstract
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.
URI
https://dl.acm.org/citation.cfm?id=2851862http://hdl.handle.net/20.500.11754/49578
ISBN
978-1-4503-3739-7
DOI
10.1145/2851613.2851862
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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