# Learning Effective Representations for Person-Job Fit by Feature Fusion

arXiv: https://arxiv.org/abs/2006.07017 · HTML: https://mlsanity.com/paper/2006.07017/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 16 of 16)

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- [Toward a traceable, explainable, and fairJD/Resume recommendation system](https://mlsanity.com/paper/2202.08960/) (2022)
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- [MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model](https://mlsanity.com/paper/2508.13676/) (2025)
- [Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching](https://mlsanity.com/paper/2602.15752/) (2026)
- [Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data](https://mlsanity.com/paper/2604.17738/) (2026)
- [Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation](https://mlsanity.com/paper/2604.21264/) (2026)

## Abstract (arXiv)

Person-job fit is to match candidates and job posts on online recruitment platforms using machine learning algorithms. The effectiveness of matching algorithms heavily depends on the learned representations for the candidates and job posts. In this paper, we propose to learn comprehensive and effective representations of the candidates and job posts via feature fusion. First, in addition to applying deep learning models for processing the free text in resumes and job posts, which is adopted by existing methods, we extract semantic entities from the whole resume (and job post) and then learn features for them. By fusing the features from the free text and the entities, we get a comprehensive representation for the information explicitly stated in the resume and job post. Second, however, some information of a candidate or a job may not be explicitly captured in the resume or job post. Nonetheless, the historical applications including accepted and rejected cases can reveal some implicit intentions of the candidates or recruiters. Therefore, we propose to learn the representations of implicit intentions by processing the historical applications using LSTM. Last, by fusing the representations for the explicit and implicit intentions, we get a more comprehensive and effective representation for person-job fit. Experiments over 10 months real data show that our solution outperforms existing methods with a large margin. Ablation studies confirm the contribution of each component of the fused representation. The extracted semantic entities help interpret the matching results during the case study.