Towards learning travelers’ preferences in a context-aware fashion

Providing personalized offers, and services in general, for the users of a system requires perceiving the context in which the users’ preferences are rooted. Accordingly, context modeling is becoming a relevant issue and an expanding research field. Moreover, the frequent changes of context may induce a change in the current preferences; thus, appropriate learning methods should be employed for the system to adapt automatically. In this work, we introduce a methodology based on the so-called Context Dimension Tree—a model for representing the possible contexts in the very first stages of Application Design—as well as an appropriate conceptual architecture to build a recommender system for travelers.