Learning Job Titles Similarity from Noisy Skill Labels
This addresses the need for job recommendation systems by providing a method that avoids the requirement for labeled equivalent job title pairs, though it is incremental as it builds on unsupervised learning techniques.
The paper tackled the problem of measuring semantic similarity between job titles for automatic job recommendations by proposing an unsupervised representation learning method that uses noisy skill labels, showing it is highly effective for tasks like text ranking and job normalization.
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.