Exploring Hybrid Recommender Systems for Personalized Travel Applications

The recent research in the recommender systems domain has attracted many researchers due to its increasing demands in the real world. To bridge the real-world issues of the users with the problems of the researchers in the digital world, we present hybrid recommendation techniques in e-Tourism domain. In this paper, we have explained the research problems in the e-Tourism applications and presented the possible solution to achieve better personalized recommendations. We have developed a Personalized Context-Aware Hybrid Travel Recommender System (PCAHTRS) by incorporating user’s contextual information. The proposed PCAHTRS is evaluated on the real-time large-scale datasets of Yelp and TripAdvisor. The experimental results depict the improved performance of the proposed approach over traditional approaches. We have concluded the paper with future work guidelines to help researchers to achieve fruitful solutions for recommendation problems.