An extensive study on the evolution of context-aware personalized travel recommender systems

Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.