Personalized Context-Aware Recommender System for Travelers

Nowadays, traveling has become more convenient thanks to many recent technological advancements. However, the main problem is that, with non-customized offers, the risk for the travelers is to waste their time looking for the most appropriate one. Consequently, travelers need a system capable of understanding their contextual preferences for ranking travel offers accordingly. In this work, we propose The Hybrid Offer Ranker (THOR) as a possible solution: on the one hand, we employ various classification algorithms to learn the individuals' contextual preferences; on the other hand, to help new users the system has no information on (cold users), we employ unsupervised algorithms to identify clusters of users with similar preferences and build group preference models accordingly.