Matching Resumes to Jobs via Deep Siamese Network

Abstract

In this paper we investigate the important and challenging task of recommending appropriate jobs for job seeking candidates by matching semi structured resumes of candidates to job descriptions. To perform this task, we propose to use a siamese adaptation of convolutional neural network. The proposed approach effectively captures the underlying semantics thus enabling to project similar resumes and job descriptions closer to each other, and make dissimilar resumes and job descriptions distant from each other in the semantic space. Our experimental results on a set of 1314 resumes and a set of 3809 job descriptions (5,005,026 resume-job description pairs) demonstrate that our approach is better than the current state-of-the-art approaches.