Evolutionary Approach in Recommendation Systems for Complex Structured Objects

Abstract

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. This work proposes the incorporation of item generation systems as a preprocessing stage in the recommendation process through evolutionary algorithms. We found that genetic algorithms are still an interesting and powerful computational tool that have not been fully developed for this task. We a theoretical model that can deal with problems from different areas, incorporating the ability to recommend complex objects with flexible restrictions and specific structures that depend on the user an its evolution. Finally this approach will be tested in different scenarios, as Stance4Health European project, where a personalized nutrition service based on user microbiota is being developed based on this concept.