Learning Job Titles Similarity from Noisy Skill Labels

Abstract

Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.

Accepted to the International workshop on Fair, Effective And Sustainable Talent management using data science (FEAST) as part of ECML-PKDD 2022

Cited by in corpus (2)

• VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain
• TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management