ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning

A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes, and helps a job seeker find relevant jobs from a list of job posts. However, since job seekers apply only to a few jobs, interaction records in resume-job datasets are sparse. Different from many prior work that use complex modeling techniques, we tackle this sparsity problem using data augmentations and a simple contrastive learning approach. ConFit first formulates resume-job datasets as a sparse bipartite graph, and creates an augmented dataset by paraphrasing specific sections in a resume or a job post. Then, ConFit finetunes pre-trained encoders with contrastive learning to further increase training samples from B pairs per batch to <Formula format="inline"><TexMath><?TeX $\mathcal {O}(B^2)$?></TexMath><AltText>Math 1</AltText><File name="recsys24-110-inline1" type="svg"/></Formula> per batch. We evaluate ConFit on two real-world datasets and find it outperforms prior methods (including BM25 and OpenAI text-ada-002) by up to 19% and 31% absolute in nDCG@10 for ranking jobs and ranking resumes, respectively. We believe ConFit’s simple yet highly performant approach lays a strong foundation for future research in modeling person-job fit.1