In recent years, e-commerce has become a significant social and cultural phenomenon. Many organizations now offer their services online. Within this context, online recruitment services play an important role in supporting individuals in job searches and assisting companies in finding personnel. Conversely, the potential that AI forecasting systems offer in distinct application fields is well known using increasingly high-performance algorithms that are particularly promising and certainly used successfully in job searches. In this field, in general, companies insert their job offers (job proposals) into a database; individuals are supported in their search for a job offer using a search engine based on classic Information Retrieval (IR) techniques. Regarding this, it is presumable that the Information Retrieval techniques currently used by recruitment services, which do not involve the use of rich user profiles, can provide a single individual with a large number of job offers, many of which are of little interest for him. This result could cause strong dissatisfaction for the user, and his renunciation of the use of such services. The Geomatics Laboratory, in the context of forecasting studies in the territorial and environmental fields, is experimenting with forecasting systems also in the business and economics fields to create a customized search engine, proposing a Recommender System developed using intelligent agents and based on XML. This system leverages detailed user profiles to assist users in personalized job offer searches, which combines classic Information Retrieval techniques with User Modeling techniques and artificial intelligence techniques. As a result, in generic domains, our system quickly achieves satisfactory results; however, it struggles to maintain this performance after many sessions. Conversely, in specialized domains, while our system requires many sessions to reach satisfactory performance initially, it consistently delivers outstanding performance once this phase is complete.
