**6. Conclusions**

This chapter presents a modelling methodology for developing an e-learning recommender system. The proposed methodology includes the definition of a mathematical user model, as formulated in the context of the shipping industry and its employment and training environment. The implementation of the central component of this recommender system, namely the Training Advisor, is explained as based on discrete choice models and Bayesian theory. In particular, the development of an e-learning recommender system, such as the electronic training advisor proposed will help trainees in choosing the appropriate elearning courses matching their particular characteristics, preferences and needs and based on their expected professional development. The training process as assisted by the proposed training advisor; it takes into account the peculiarities of the seafaring profession, applicable career paths and respective seafarers' training needs. To formally model these requirements we developed a knowledgebase for accumulating the basic knowledge regarding the shipping work and training environment and registered the information of a 5000 users- sample population, furthermore we used statistical analysis to support the choices of each individual separately. In specific, our e-learning recommender framework is based on advanced choice models and Bayesian techniques and is considered as an intelligent system that can be tested and reused in different e-learning settings, favouring intense personalization and recommendation value-adding features. The foundational techniques used in our system offer the strong competitive advantage of a comparatively detailed, user-focused e-learning attributes modelling framework (advanced choice theory) and a competent system learning capability (Bayesian theory), that improves over time the performance of the recommender system itself .
