C on F it v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining (2025)

Abstract: A reliable resume-job matching system helps a company recommend 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 labels in resume-job datasets are sparse. We introduce ConFit v2, an improvement over ConFit to tackle this sparsity problem. We propose two techniques to enhance the encoder’s contrastive training process: augmenting job data with hypothetical reference resume generated by a large language model; and creating high-quality hard negatives from unlabeled resume/job pairs using a novel hard-negative mining strategy. This method also simplifies the representation space of the encoder. We evaluate ConFit v2 on two real-world datasets and demonstrate that it outperforms ConFit and prior methods (including BM25 and OpenAI text-embedding-003), achieving an average absolute improvement of 13.8% in recall and 17.5% in nDCG across job-ranking and resume-ranking tasks.

## introduction online recruitment platforms like linkedin serve over 990 million users and 65 million businesses, processing more than 100 million job applications each month @xcite . as these platforms continue to grow, there is a rising need for efficient and robust resume-job matching systems. a practical system that reliably identifies suitable candidates/jobs from large pools will save considerable effort for both employers and job seekers. since both resume and job posts are often stored as text data, there has been an increased interest in using transformer models to model resume-job fit (or referred to as "person-job fit"). many prior works @xcite @xcite @xcite focus on designing complex modeling techniques to model resume-job matching. for example, apjfnn (qin * denotes equal contribution et al., 2018) uses hierarchical recurrent neural networks to process the job and resume content, and dpgnn @xcite uses a dual-perspective graph neural network to model the relationship between resumes and jobs. unlike these manual model/feature engineering methods, more recently @xcite shows that dense retrieval techniques like contrastive learning achieve significant improvements without relying on any complex designs. by introducing dense retrieval methods to resume-job matching, confit @xcite presented a simple yet effective method to rank thousands of resumes/jobs in milliseconds, by combining training neural encoders such as e5 @xcite with inner-product search algorithms such as faiss @xcite . we propose confit v2, an enhanced baseline that achieves an average absolute improvement of 13.8% in recall and 17.5% in ndcg on ranking resumes and jobs compared to confit (see figure 1 ). we introduce two key improvements to enhance the neural encoder's training process: 1) hypothetical resume embedding (hyre) that leverages a large language model (llm) to generate a hypothetical resume and augment the job post (see figure 2 ), providing implicit details to reduce the burden on encoder training; and 2) runner-up mining (rum) that mines a large set of highquality hard negatives to help the encoder better discern positive with near-positive samples. we train confit v2 using hyre and rum on two real-world datasets, and show that confit v2 achieves new state-of-the-art performance on ranking resumes and jobs, outperforming confit and other prior work (including bm25 and openai text-embedding-003-large). we will open-source our code and data (under license agreements) to provide a strong baseline for future research in empowering resume-job matching systems with dense retrieval techniques. ## background a resume-job matching (or often called personjob fit) system models the compatibility between a resume r and a job post j, allowing the systems to recommend the most suitable candidates for a given job, or suggest the most relevant jobs for a given candidate @xcite @xcite . since resumes and job posts are often stored as text data, many prior works consider using neural networks (e.g., encoders) to quantify the compatibility between resumes and jobs: score(r, j) = f θ (r, j) → r, where f θ could be directly modeled by a neural network @xcite @xcite , or by computing inner product/cosine similarity @xcite between a resume embedding f θ (r) and a job embedding f θ (j). despite the rapid growth of online recruitment platforms and the increasing availability of job posts and resume data, there are very few interaction labels between any resumes and job posts @xcite . this is because a candidate usually applies to only a few positions, and a job interviewer often reviews only a few resumes for a given job post. often, the resulting dataset @xmath0, j i , y i } has size |d| ≪ n r × n j , where n r and n j are the total number of resumes and jobs respectively, and y @xmath1, 1} is a binary signal representing whether a resume r i is short listed for a job j i . for example, in both the recruiting and aliyun datasets (section 4.1) used in this work, less than 0.05% of the total possible (resume, job) pairs are annotated. this label sparsity poses challenges in: 1) crafting high-quality training data/hard negatives for training a neural encoder f θ ; and 2) learning a representation space that generalizes well across diverse resumes and job posts. ## approach we propose confit v2, a simple and generalpurpose approach that improves confit @xcite to model resume-job compatibility. similar to confit, we use an encoder to produce an embedding of a given resume or job post, and model the matching score between an ⟨r, j⟩ pair as the cosine similarity of their representations. unlike confit, confit v2 uses a simpler encoder architecture, and substantially improves ranking performance by using 1) hypothetical reference resume generated by an llm to help improve the encoder's job data understanding; and 2) a novel hardnegative mining method to enhance encoder contrastive learning. we provide a high-level overview of confit v2 during inference in figure 2 . we detail each modification below. ## experiments we evaluate confit v2 on two real-world personjob fit datasets, and measure its performance on ranking resumes and ranking jobs. ## analysis in this section, we provide both qualitative and quantitative analysis on the ranking output produced by confit v2. we mainly focus on the recruiting dataset as it is more challenging. ## conclusion we propose confit v2, an improvement of con-fit to model person-job fit. similar to confit, we model person-job fit using dense retrieval techniques such as contrastive learning. unlike con-fit, we first simplified the encoder architecture, and improved encoder training using 1) hypothetical reference resume generated by an llm to augment a job post; and 2) a new hard-negative mining strategy that selects "runner-up" resume-job pairs to avoid false negatives used for encoder training. we evaluate confit v2 on two real-world datasets, and demonstrate that it outperforms con-fit and prior methods, achieving an average absolute improvement of 13.8% in recall and 17.5% in ndcg across both ranking resume and ranking job tasks. we believe our work lays a strong foundation for future resume-job matching systems to leverage the latest advancements in dense text retrieval.