Ranking job offers for candidates: learning hidden knowledge from Big Data

• Main Authors: Poch, Marc, Bel Rafecas, Núria, Espeja, Sergio, Navío, Felipe
• Format: Conference Object
• Language: English
• Published: ACL (Association for Computational Linguistics)
• Subjects: Multilingual data; E-recruiting; LDA clustering methods; Ranking methods; Iceland
• Online Access: http://hdl.handle.net/10230/36781

Comunicació presentada a: 9th International Conference on Language Resources and Evaluation celebrada del 26 al 31 de maig de 2014 a Reykjavik, Iceland. This paper presents a system for suggesting a ranked list of appropriate vacancy descriptions to job seekers in a job board web site. In particular our work has explored the use of supervised classifiers with the objective of learning implicit relations which cannot be found with similarity or pattern based search methods that rely only on explicit information. Skills, names of professions and degrees, among other examples, are expressed in different languages, showing high variation and the use of ad-hoc resources to trace the relations is very costly. This implicit information is unveiled when a candidate applies for a job and therefore it is information that can be used for learning a model to predict new cases. The results of our experiments, which combine different clustering, classification and ranking methods, show the validity of the approach.