An Exploration of Sentence-Pair Classification for Algorithmic Recruiting

Recent years have seen a rapid increase in the application of computational approaches to different HR tasks, such as algorithmic hiring, skill extraction, and monitoring of employee satisfaction. Much of the recent work on estimating the fit between a person and a job has used representation learning to represent both resumes and job vacancies computationally and determine the degree to which they match. A common approach to this task is Sentence-BERT, which uses a Siamese network to encode resumes and job descriptions into fixed-length vectors and estimates how well they match based on the similarity between those vectors. In our paper, we adapt BERT’s next-sentence prediction task—predicting whether one sentence is likely to follow another in a given context—to the task of matching resumes with job descriptions. Using historical data on past (mis)matches between job-resume pairs, we fine-tune BERT for this downstream task. Through a combination of offline and online experiments on data from a large Scandinavian job portal, we show that this approach performs significantly better than Sentence-BERT and other state-of-the-art approaches for determining person-job fit.