# Tripartite Vector Representations for Better Job Recommendation

arXiv: https://arxiv.org/abs/1907.12379 · HTML: https://mlsanity.com/paper/1907.12379/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness](https://mlsanity.com/paper/2410.05006/) (2024)
- [Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning](https://mlsanity.com/paper/2206.02782/) (2022)
- [Toward a traceable, explainable, and fairJD/Resume recommendation system](https://mlsanity.com/paper/2202.08960/) (2022)
- [Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs](https://mlsanity.com/paper/2411.15182/) (2024)

## Abstract (arXiv)

Job recommendation is a crucial part of the online job recruitment business. To match the right person with the right job, a good representation of job postings is required. Such representations should ideally recommend jobs with fitting titles, aligned skill set, and reasonable commute. To address these aspects, we utilize three information graphs ( job-job, skill-skill, job-skill) from historical job data to learn a joint representation for both job titles and skills in a shared latent space. This allows us to gain a representation of job postings/ resume using both elements, which subsequently can be combined with location. In this paper, we first present how the presentation of each component is obtained, and then we discuss how these different representations are combined together into one single space to acquire the final representation. The results of comparing the proposed methodology against different base-line methods show significant improvement in terms of relevancy.