  In this work a novel recommender system (RS) for Tourism is presented. The RS
is context aware as is now the rule in the state-of-the-art for recommender
systems and works on top of a tourism ontology which is used to group the
different items being offered. The presented RS mixes different types of
recommenders creating an ensemble which changes on the basis of the RS's
maturity. Starting from simple content-based recommendations and iteratively
adding popularity, demographic and collaborative filtering methods as rating
density and user cardinality increases. The result is a RS that mutates during
its lifetime and uses a tourism ontology and natural language processing (NLP)
to correctly bin the items to specific item categories and meta categories in
the ontology. This item classification facilitates the association between user
preferences and items, as well as allowing to better classify and group the
items being offered, which in turn is particularly useful for context-aware
filtering.
