Tasks as physical training planning, computer hardware configuration or fully dietary advice, are problems that exhibit multiple choices, composed in turn of simpler items (specific exercises, components or recipes). An ideal recommender system would not only recommend simple items based on the user’s tastes, but would offer a set of items that suit the user’s needs and preferences so that they form a meaningful structure that can evolve in time. Taking this idea as our main cornerstone, this Ph.D. face two objectives: being able to generate an item with a complex structure from simpler items, and integrating the user’s limitations and preferences to develop an adaptive recommender system.