Ontology-based Context Aware Recommender System Application for Tourism

TL;DR

This paper introduces a dynamic, ontology-based, context-aware recommender system for tourism that evolves by integrating multiple recommendation techniques and leverages NLP for accurate item classification.

Contribution

It presents a novel ensemble recommender system that adapts over time, utilizing tourism ontologies and NLP for improved context-aware recommendations.

Findings

The system effectively combines multiple recommendation methods.

Ontology and NLP improve item classification and grouping.

The recommender adapts dynamically as data density increases.

Abstract

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.