Many employment services base their online job recommendations to users based solely on explicit data in their profiles. The implicit data of what users for example click on, save and mark as irrelevant goes unused. Instead of making recommendations based on user behavior they make a direct comparison between user preferences and job ad attributes. A reason for this is the concern that the inclusion of implicit data can give odd recommendations resulting in a loss of credibility for the service. However, as research has shown this to be of great advantage to recommender systems. In this paper I implement a job recommender and test it both with user data including interaction history with job ads as well as with only explicit data. The results of the recommender with implicit data got better overall performance, but negligible gain in the ratio between true and false positives, or in other words the ratio between correct and incorrect recommendations.

Ce mémoire tente de répondre à une problématique très importante dans le domaine de recrutement : l’appariement entre offre d’emploi et candidats. Dans notre cas nous disposons de milliers d’offres d’emploi et de millions de profils ramassés sur les sites dédiés et fournis par un industriel spécialisé dans le recrutement. Les offres d’emploi et les profils de candidats sur les réseaux sociaux professionnels sont généralement destinés à des lecteurs humains qui sont les recruteurs et les chercheurs d’emploi. Chercher à effectuer une sélection automatique de profils pour une offre d’emploi se heurte donc à certaines difficultés que nous avons cherché à résoudre dans le présent mémoire. Nous avons utilisé des techniques de traitement automatique de la langue naturelle pour extraire automatiquement les informations pertinentes dans une offre d’emploi afin de construite une requête qui nous permettrait d’interroger notre base de données de profils. Pour valider notre modèle d’extraction de métier, de compétences et de d’expérience, nous avons évalué ces trois différentes tâches séparément en nous basant sur une référence cent offres d’emploi canadiennes que nous avons manuellement annotée. Et pour valider notre outil d’appariement nous avons fait évaluer le résultat de l’appariement de dix offres d’emploi canadiennes par un expert en recrutement. / Our work seeks to address a very important issue in the recruitment field: matching jobs postings and candidates. We have thousands of jobs postings and millions of profiles collected from internet provided by a specialized firm in recruitment. Job postings and candidate profiles on professional social networks are generally intended for human readers who are recruiters and job seekers. We use natural language processing (NLP) techniques to automatically extract relevant information in a job offer. We use the extracted information to build automatically a query on our database. To validate our information retrieval model of occupation, skills and experience, we use hundred Canadian jobs postings manually annotated. And to validate our matching tool we evaluate the result of the matching of ten Canadian jobs by a recruitment expert.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / With a rapid growth of internet technologies, many companies have transformed from the old traditional ways of recruiting employees to electronic recruitment (e-recruitment). E-recruiting channels achieved a solid advantage for both employers and job applicants by dropping advertising cost, applying cost as well as hiring time. Job recommender systems aim to help in people – job matching. In this research, a proposed mobile job employment recommender system is a client – server application that uses content – based filtering algorithm to enable the initial selection of a suitable leisure job seeker to a temporary job at a particular place and vice versa. A prototype of a mobile job recommendation application was developed to evaluate the algorithm. The evaluation matrix used to assess the prototype are precision, recall and the F-measure. The precision value was found to be 0.994, the recall value was 0.975 and the F1- score was 0.984. The experimental results of the proposed algorithm show the effectiveness of the system to recommend suitable candidates for jobs at a specified area. The recommender system was able to achieve its main aim of enabling the initial selection of suitable temporary job seekers to a temporary job at a particular place and vice versa. Thus, the results of the proposed algorithm are satisfactory.