Algorithms in E-recruitment Systems

Search engines and recommender systems have an important influence on the recruitment process, both for recruiters and job seekers. For both, they determine which job seekers/vacancies are recommended in what order, on résumé databases or recruitment websites. In doing so, they influence which vacancies job seeker apply to, or which job applicants are selected for an interview. To determine which vacancies/candidates to show and in what order, the search engine/recommender system uses estimations of the relevance of a vacancy/candidate for a job seeker/recruiter. To estimate this relevancy, the search engine/recommender system often only have partial information. In particular, only the search queries are known, and the click interaction on the search result. This interaction data shows which vacancies/candidates have been clicked on by users of the search engine/recommender system. This dissertation considers multiple algorithms, with the goal of improving estimations of relevance in the context of job/candidate recommender systems and search engines.