Matching Medical Students to Pairs of Hospitals: A New Variation on a Well-Known Theme
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
iHR: an online recruiting system for Xiamen Talent Service Center
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.00 |
We address the problem of recommending suitable jobs to people who are seeking a new job. We formulate this recommendation problem as a supervised machine learning problem. Our technique exploits all past job transitions as well as the data associated with employees and institutions to predict an employee's next job transition. We train a machine learning model using a large number of job transitions extracted from the publicly available employee profiles in the Web. Experiments show that job transitions can be accurately predicted, significantly improving over a baseline that always predicts the most frequent institution in the data.