Toward the next generation of recruitment tools: An online social network-based job recommender system - Diaby, Mamadou; Viennet, Emmanuel; Launay, Tristan (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining : ASONAM 2013 : Niagara Falls, Canada, August 25-28, 2013, 2013)
This paper presents a content-based recommender system which proposes jobs to Facebook and LinkedIn users. A variant of this recommender system is currently used by Work4, a San Francisco-based software company that offers Facebook recruitment solutions. Work4 is the world leader in social recruitment technology; to use its applications, Facebook or LinkedIn users explicitly grant access to some parts of their data, and they are presented with the jobs whose descriptions are matching their profiles the most. The profile of a user contains two types of data: interactions data (user's own data) and social connections data (user's friends data). Furthermore the users profiles and the description of jobs are divided into several parts called fields. Our experiments suggest that to predict the users interests for jobs, using basic similarity measures together with their interactions data collected by Work4 can be improved upon. The second part of this study presents a method to estimate the importance of each field of users and jobs in the task of job recommendation. Finally, the third part is devoted to the use of a machine learning algorithm in order to improve the results obtained with similarity measures: we trained a linear SVM (Support Vector Machines). Our results show that using this supervised learning procedure increases the performance of our content-based recommender system.