Job and Employee Embeddings: A Joint Deep Learning Approach

The accumulated massive job and employee data at various platforms such as LinkedIn and Glassdoor are very valuable for many online applications such as job/employee search and recommendations. In order to exploit these data, an interesting and practical problemis how to learn effective job and employee representations, which could be further utilized for many computing tasks such as searching for similar jobs and employees. Yet this problem is very challenging because these user-generated job and employee data are semi-structured and created without standards, which makes them very messy, sparse, and difficult to model. Developing novel and advanced methods to learn job and employee representations has become an urgent need. To this end, in this paper, we develop a novel neural network model for job and employee embeddings. Our proposed approach consists of three components to model career data from three levels of granularity: job content, job context, and job sequence. We fine-tune a transformer model to learn the semantics of massive text in job content, build a shallow neural network to accumulate contextual information in job sequences, and develop an RNN encoder-decoder model to learn representations of employees' career paths. To evaluate the proposed method, we conduct two experimental tasks: job similarity and employee similarity searches. The experimental results with a real-world dataset demonstrate the superiority of the developed approach.