This thesis addresses the design and development of an advanced recommendation system for CNH Industrial’s B2B portal, a complex context characterized by anonymous transactional data, extensive catalogs and the need for technically relevant suggestions. The existing system, based on association rules (FP-Growth), although effective in identifying the most frequent co-occurrences, shows limits in terms of accuracy and ability to cover the vast long tail of the catalogue, highlighting a classic trade-off between precision and coverage. To overcome these limitations, a tailor-made hybrid architecture was proposed and validated. The model synergistically blends two components: the explicit knowledge extracted from FP-Growth, which provides a solid basis of interpretable rules, and the latent semantic knowledge learned through a Word2Vec distributional model (Prod2Vec), which represents each product as a vector in a contextual space.