Ontology-based Context Aware Recommender System Application for Tourism
This is an incremental improvement for tourism applications, addressing the need for better item-user matching in context-aware systems.
The authors developed a tourism recommender system that adapts its ensemble of recommendation methods based on system maturity, using an ontology and NLP for item classification, resulting in improved context-aware filtering.
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.