# Career Path Prediction using Resume Representation Learning and Skill-based Matching

arXiv: https://arxiv.org/abs/2310.15636 · HTML: https://mlsanity.com/paper/2310.15636/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning](https://mlsanity.com/paper/2009.07429/) (2020)
- [conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers](https://mlsanity.com/paper/2109.06501/) (2021)

## Built on by (most cited first, 12 of 12)

- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations](https://mlsanity.com/paper/2410.08319/) (2024)
- [KARRIEREWEGE: A Large Scale Career Path Prediction Dataset](https://mlsanity.com/paper/2412.14612/) (2024)
- [Learning Job Title Representation from Job Description Aggregation Network](https://mlsanity.com/paper/2406.08055/) (2024)
- [JobHop: A Large-Scale Dataset of Career Trajectories](https://mlsanity.com/paper/2505.07653/) (2025)
- [An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit](https://mlsanity.com/paper/2601.10321/) (2026)
- [JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes](https://mlsanity.com/paper/2607.11715/) (2026)
- [STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling](https://mlsanity.com/paper/2607.11722/) (2026)
- [NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching](https://mlsanity.com/paper/2506.19058/) (2025)
- [On Reasoning Behind Next Occupation Recommendation](https://mlsanity.com/paper/2604.21204/) (2026)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)
- [When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction](https://mlsanity.com/paper/2610.20186/) (2026)

## Abstract (arXiv)

The impact of person-job fit on job satisfaction and performance is widely acknowledged, which highlights the importance of providing workers with next steps at the right time in their career. This task of predicting the next step in a career is known as career path prediction, and has diverse applications such as turnover prevention and internal job mobility. Existing methods to career path prediction rely on large amounts of private career history data to model the interactions between job titles and companies. We propose leveraging the unexplored textual descriptions that are part of work experience sections in resumes. We introduce a structured dataset of 2,164 anonymized career histories, annotated with ESCO occupation labels. Based on this dataset, we present a novel representation learning approach, CareerBERT, specifically designed for work history data. We develop a skill-based model and a text-based model for career path prediction, which achieve 35.24% and 39.61% recall@10 respectively on our dataset. Finally, we show that both approaches are complementary as a hybrid approach achieves the strongest result with 43.01% recall@10.