Learning Job Titles Similarity from Noisy Skill Labels

Understand

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.