Tackling Cold Start for Job Recommendation with Heterogeneous Graphs

Recruiting changed drastically with the emergence of professional social networks that bring together many people and companies. It is a chance as it helps to increase the adequacy between a position and a candidate. However, it creates new challenges. First, the many possible combinations make it hard to find the perfect match. Second, it brings together talents with many different skills and backgrounds that can be hard to understand for a recruiter. In particular, in computer science, technologies tend to change quickly and can be obscure for non-technical employees. Therefore, using automatic tools is crucial to guide the recruiting process. More specifically, a recommender system that matches candidates with open positions can improve the overall satisfaction of all the agents in our system. Yet, job-matching data suffers from the cold start problem: Once a person gets a position, they are very unlikely to obtain a new one soon. Thus, traditional techniques based on collaborative filtering are very limited, and we must rely on the unique characteristics of each candidate. In this paper, we propose a new recommender system based on a recruiting heterogeneous graph. This graph brings together information about a job posting and the personal knowledge graph of the candidates. We tested our model on a new real-world dataset, and we showed that it outperforms state-of-the-art methods.