A Chain Composite Item Recommender for Lifelong Pathways

This work addresses the problem of recommending lifelong pathways, i.e., sequences of actions pertaining to health, social or professional aspects, for fulfilling a personal lifelong project. This problem raises some specific challenges, since the recommendation process is constrained by the user profile, the time they can devote to the actions in the pathway, the obligation to smooth the learning curve of the user. We model lifelong pathways as particular chain composite items and formalize the recommendation problem as a form of orienteering problem. We adapt classical evaluation criteria for measuring the quality of the recommended pathways. We experiment with both artificial and real datasets, showing our approach is a promising building block of an interactive lifelong pathways recommender system.

Automated summary

This work addresses the problem of recommending lifelong pathways, modeling them as particular chain composite items and formalizing the recommendation problem as an orienteering problem. The approach is experimented with artificial and real datasets, showing promising results as a building block for an interactive lifelong pathways recommender system.