Personalized Travel Recommendation System Using an Ontology

The rapid growth of the web and its applications has created immense importance for recommender systems. Recommender systems were designed to generate suggestions for items or services based on user interests with the applications to different domains. However, the integration of multiple data sources while resolving semantic ambiguity of entities involved in the integration has been overlooked in many recommender systems developed for travel recommendation. This research proposes an ontology-based travel recommender system to overcome such deficiencies in the current travel recommender systems. The developed ontology facilitates the integration of multi-model data for personalized travel recommendations. The similarity analysis of entities to be interconnected is performed by using a semantic data classification technique that integrates a hybrid filtering approach to classify similar entities, including tours and visitors. The proposed ontology-based approach for travel recommendation outperforms other methods and with higher accuracies.