# Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning

arXiv: https://arxiv.org/abs/2009.07429 · HTML: https://mlsanity.com/paper/2009.07429/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 17 of 17)

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- [Career Path Prediction using Resume Representation Learning and Skill-based Matching](https://mlsanity.com/paper/2310.15636/) (2023)
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- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning](https://mlsanity.com/paper/2202.10739/) (2022)
- [Learning Job Title Representation from Job Description Aggregation Network](https://mlsanity.com/paper/2406.08055/) (2024)
- [Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning](https://mlsanity.com/paper/2206.02782/) (2022)
- [Fake Resume Attacks: Data Poisoning on Online Job Platforms](https://mlsanity.com/paper/2402.14124/) (2024)
- [Retrieval-Augmented Feature Generation for Domain-Specific Classification](https://mlsanity.com/paper/2406.11177/) (2024)
- [Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment](https://mlsanity.com/paper/2112.03024/) (2021)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [Improving Next-Application Prediction with Deep Personalized-Attention Neural Network](https://mlsanity.com/paper/2111.11296/) (2021)
- [STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling](https://mlsanity.com/paper/2607.11722/) (2026)
- [Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics](https://mlsanity.com/paper/2509.04942/) (2025)
- [Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs](https://mlsanity.com/paper/2509.09522/) (2025)

## Abstract (arXiv)

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor-intensive. Recently, the rapid development of Online Professional Graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e.g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view, (2)semantic view, (3) job transition balance view, and (4) job transition duration view. We fuse the multi-view representations in the encode-decode paradigm to obtain a unified optimal representation for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.