Job Recommendation Based on Extracted Skill Embeddings

With the increasing popularity of online recruiting platforms in modern industry, most employers choose these platforms as a means of connecting with potential candidates for open positions. Developing job recommendation systems can significantly help both employers and job seekers in speeding up this process and finding the best matches. Using skill phrases extracted from unformatted and unstructured CVs and Job Descriptions, we propose two approaches with different similarity metrics, namely Word Mover's Distance and Cosine Similarity. We selected TF-IDF with Cosine Similarity as a baseline and evaluated our methods on the real data from an online recruitment company, Kariyer.net. Our results suggest that the previously unstudied Word Mover's Distance-based approach outperforms Cosine Similarity-based approaches and gives promising results in the job recommendation domain.