A job recommendation method optimized by position descriptions and resume information

Abstract

With the development of Internet technology, online job-hunting plays an increasingly important role in job-searching. It is difficult for job hunters to solely rely on keywords retrieving to find positions which meet their needs. To solve this issue, we adopted item-based collaborative filtering algorithm for job recommendations. In this paper, we optimized the algorithm by combining position descriptions and resume information. Specifically, job preference prediction formula is optimized by historical delivery weight calculated by position descriptions and similar user weight calculated by resume information. The experiments tested on real data set have shown that our methods have a significant improvement on job recommendation results.