Connecting candidates and jobs to promote real placement opportunities is one of the most impacting and challenging scenarios for Recommender Systems (RSs). At SEEK, we work to provide such personalized solution for millions of people in different countries. A major concern for us when building RSs is ensuring placement opportunities for all candidates and jobs on the system as soon as possible. Indeed, long waiting periods cause financial damages to both sides. We refer to these scenarios where candidates or jobs suffer from the absence of matching in the system as the Problem of Matching Scarcity (PMS). This talk introduces the PMS, discussing the reasons we consider PMS as a recurring threat to recruitment services, and our efforts to identify, characterize and mitigate it on real scenarios.
Short Bio
Fernando Mourão is a Senior Data Scientist at SEEK, currently working with applied ML projects related to Search and Recommendation for online recruitment. He was a professor at the Universidade Federal de São João del Rey and visiting researcher at the University of Minnesota (USA) and Rensselaer Polytechnic Institute (USA). He has experience in scientific research focused on Recommendation Systems, Data Mining, Information Retrieval, and Machine Learning, presenting over 50 publications and 300 citations.