Taxonomy-based job recommender systems on Facebook and LinkedIn profiles

This paper presents taxonomy-based recommender systems that propose relevant jobs to Facebook and LinkedIn users; they are being developed by Work4, a San Francisco-based software company and the Global Leader in Social and Mobile Recruiting that offers Facebook recruitment solutions; 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. In this paper, we use the O*NET-SOC taxonomy, a taxonomy that defines the set of occupations across the world of work, to develop a new taxonomy-based vector model for social network users and job descriptions suited to the task of job recommendation; we propose two similarity functions based on the AND and OR fuzzy logic's operators, suited to the proposed vector model. We compare the performance of our proposed vector model to the TF-IDF model using our proposed similarity functions and the classic heuristic measures; the results show that the taxonomy-based vector model outperforms the TF-IDF model. We then use SVMs (Support Vector Machines) with a mechanism to handle unbalanced datasets, to learn similarity functions from our data; the learnt models yield better results than heuristic similarity measures. The comparison of our methods to two methods of the literature (a matrix factorization method and the Collaborative Topic Regression) shows that our best method yields better results than those two methods in terms of AUC. The proposed taxonomy-based vector model leads to an efficient dimensionality reduction method in the task of job recommendation.