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In a recent years Tourism and Travel stores offering a huge quantity of services and traveling information by online. Additionally, this huge volume of information smoothly accessed by electronic devices, like phone, computer with the availability of internet connection. When tourists are visiting any cities, most of them aimed to explore the interesting fact or things about the places and events etc. in spite of the fruitful progress there are still several opportunities remaining to discover. Firstly, we investigate the properties of the travel package and expand TAST (Tourist-Area-Season topic) model. Thereafter, we expand the TouristArea-Season topic (TAST) model for confining the relationship over the every group of the tourist. This research work expressing an outline for the recommender system and describing the latest production of recommendation techniques that are generally divided in the three phase: Collaborative, Hybrid recommendation and Content-based approaches. Finally, we appraise the TouristArea-Season topic (TAST) model with the cocktail recommendation approach on the real-world travel package data.

Recommendation and ranking of travel package to tourist