Rémy Kessler

Job Offer Management: How Improve the Ranking of Candidates

The market of online job search sites grows exponentially. This implies volumes of information (mostly in the form of free text) become manually impossible to process. An analysis and assisted categorization seems relevant to address this issue. We present E-Gen, a system which aims to perform assisted analysis and categorization of job offers and of the responses of candidates. This paper presents several strategies based on vectorial and probabilistic models to solve the problem of profiling applications according to a specific job offer. Our objective is a system capable of reproducing the judgement of the recruitment consultant. We have evaluated a range of measures of similarity to rank candidatures by using ROC curves. Relevance feedback approach allows to surpass our previous results on this task, difficult, diverse and higly subjective.

A hybrid approach to managing job offers and candidates

The evolution of the job market has resulted in traditional methods of recruitment becoming insufficient. As it is now necessary to handle volumes of information (mostly in the form of free text) that are impossible to process manually, an analysis and assisted categorization are essential to address this issue. In this paper, we present a combination of the E-Gen and Cortex systems. E-Gen aims to perform analysis and categorization of job offers together with the responses given by the candidates. E-Gen system strategy is based on vectorial and probabilistic models to solve the problem of profiling applications according to a specific job offer. Cortex is a statistical automatic summarization system. In this work, E-Gen uses Cortex as a powerful filter to eliminate irrelevant information contained in candidate answers. Our main objective is to develop a system to assist a recruitment consultant and the results obtained by the proposed combination surpass those of E-Gen in standalone mode on this task.

Automatic Profiling System for Ranking Candidates Answers in Human Resources

The exponential growth of Internet allowed the development of a market of online job search sites. This work aims at presenting the E-Gen system (Automatic Job Offer Processing system for Human Resources). E-Gen will implement several complex tasks: an analysis and categorization of jobs offers which are unstructured text documents (e-mails of job offers possibly with an attached document), an analysis and a relevance ranking of the candidate answers. We present a strategy to resolve the last task: After a process of filtering and lemmatisation, we use vectorial representation and different similarity measures. The quality of ranking obtained is evaluated using ROC curves.

Classification automatique de courriers électroniques par des méthodes mixtes d'apprentissage

A class of radiolabeled compounds to be used in tissue imaging that exhibits rapid brain uptake, good brain:blood radioactivity ratios, and long retention times. The imaging agents are more specifically radioiodinated aromatic amines attached to dihydropyridine carriers, that exhibit heart as well as brain specificity. In addition to the radiolabeled compounds, classes of compounds are also described that are used as precursors and intermediates in the preparation of the imaging agents.

E-Gen : traitement automatique d'informations de ressources humaines

L’internet est au cœur du marche du travail et son utilisation s’etend a mesure qu’augmente le nombre d’internautes. La recherche d’emploi au travers des « bourses a l’emploi electroniques » et l’e-recrutement se sont banalises. Cette explosion d’informations pose divers problemes pour leur traitement rapide et efficace. Nous presentons le projet EGen, qui permet d’analyser les offres d’emploi de maniere automatique ou assistee. Base sur des classifieurs pilotes par un automate de Markov, le systeme obtient de tres bons resultats. Nous proposons egalement une strategie afin d’assister les recruteurs dans la tâche – difficile et d’une grande subjectivite – de classement de candidatures. Nous evaluons differentes mesures de similarite afin d’effectuer un classement pertinent des candidatures. L’utilisation d’un modele de relevance feedback a permis de surpasser nos resultats.