Job recommendation in AskStory
Yeon-Chang Lee, Jiwon Hong, Sang‐Wook Kim · 2016
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.