Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2043932.2043994 | RecSys |
Keywords | Field | DocType |
suitable job,supervised machine,available employee profile,next job transition,large number,job recommendation,frequent institution,new job,recommendation problem,past job transition,job transition,machine learning | Data science,Data mining,Job analysis,Computer science,Job design,Job shadow,Exploit,Job rotation,Job performance | Conference |
Citations | PageRank | References |
32 | 1.25 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Paparrizos | 1 | 101 | 11.59 |
| B. Barla Cambazoglu | 2 | 735 | 38.87 |
| Aristides Gionis | 3 | 6808 | 386.81 |