Matching Recruiters and Jobseekers on Twitter
Aparup Khatua, Wolfgang Nejdl · 2020
An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.