Implementing a Resume Database with Online Learning to Rank

Abstract: Learning to Rank is a research area within Machine Learning. It is mainly used in Information Retrieval and has been applied to, among other systems, web search engines and in computational advertising. The purpose of the Learning to Rank model is to rank a list of items, placing the most relevant at the top of the list, according to the users' requirements. Online Learning to Rank is a type of this model, that learns directly from the users' interactions with the system. In this thesis a resume database is implemented, where the search engine applies an Online Learning to Rank algorithm, to rank consultant's resumes, when queries with required skills and competences are issued to the system. The implementation of the Resume Database and the ranking algorithm, as well as an evaluation, is presented in this report. Results from the evaluation indicates that the performance of the search engine, with the Online Learning to Rank algorithm, could be desirable in a production environment.