Abstract : Since the 90s, Internet is at the heart of the labor market. First mobilized on specific expertise, its use spreads as increase the number of Internet users in the population. Seeking employment through "electronic employment bursary" has become a banality and e-recruitment something current. This information explosion poses various problems in their treatment with the large amount of information difficult to manage quickly and effectively for companies. We present in this PhD thesis, the work we have developed under the E-Gen project, which aims to create tools to automate the flow of information during a recruitment process.We interested first to the problems posed by the routing of emails. The ability of a companie to manage efficiently and at lower cost this information flows becomes today a major issue for customer satisfaction. We propose the application of learning methods to perform automatic classification of emails to their routing, combining technical and probabilistic vector machines support. After, we present work that was conducted as part of the analysis and integration of a job ads via Internet. We present a solution capable of integrating a job ad from an automatic or assisted in order to broadcast it quickly. Based on a combination of classifiers systems driven by a Markov automate, the system gets very good results. Thereafter, we present several strategies based on vectorial and probabilistic models to solve the problem of profiling candidates according to a specific job offer to assist recruiters. 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.