# Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning

arXiv: https://arxiv.org/abs/2206.02782 · HTML: https://mlsanity.com/paper/2206.02782/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning](https://mlsanity.com/paper/2009.07429/) (2020)
- [Tripartite Vector Representations for Better Job Recommendation](https://mlsanity.com/paper/1907.12379/) (2019)
- [IPOD: An Industrial and Professional Occupations Dataset and its Applications to Occupational Data Mining and Analysis](https://mlsanity.com/paper/1910.10495/) (2019)
- [Improving Next-Application Prediction with Deep Personalized-Attention Neural Network](https://mlsanity.com/paper/2111.11296/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [Challenges and Opportunities of NLP for HR Applications: A Discussion Paper](https://mlsanity.com/paper/2405.07766/) (2024)

## Abstract (arXiv)

Works on learning job title representation are mainly based on textitJob-Transition Graph, built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation. Specifically, we construct textitJob-Transition-Tag Graph, a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities). Along this line, we reformulate job title representation learning as the task of learning node embedding on the textitJob-Transition-Tag Graph. Experiments on two datasets show the interest of our approach.