Recommender Systems in Tourism


Nowadays, many people use online services to plan and book a trip. But! due to huge amount of information available online and plethora of possibilities, firstly they have to invest a lot of time to decide where to visit, when to visit, what to do, Secondly, it is very difficult to discern  the more interesting offers from the rest and thirdly many good offers go unnoticed.
Therefore In order to improve the travelers’ experience, the recommender systems select the more suitable and adequate offers for them and offers activities appropriate to their profile.
On the other side, the travel companies who want to construct a sustainable relationship with their customers to drive growth need to engage with the individual customers in a way that creates a memorable event by moving beyond the trip into travelers’ daily lives by offering personalized services. These companies not only wish to engage with their existing customers but they also want someone to recommend their products to, and personalize them to the needs of, new unknown customers as well so that they can increase sales of their services for better economies of scale and higher profitability.
So, in order to get it right, they go beyond being relevant—they become hyper-relevant and to make their offerings hyper-relevant to existing and unknown customers, the recommender systems with artificial the intelligence of understanding each customer needs and offer them personalized travel itineraries that satisfy those needs are an amazing smart tool for them.
The third element in travel & Tourism is the mobility, customers may not wish to multi-engage in selection and consumption over multiple applications in multiple countries through unknown languages and the service providers on the other side wishes to keep their customers loyally hooked to their services at all times to be able to increase their per customer consumption of services and keep them from looking for alternate service providers.
Hence, dynamic smart tourism information systems need to keep recommending the traveler not just at home but even in the foreign country according to their own preferences and likings and be skillfully responsive to any changes in customers travel anytime, this way the traveling customer can have a dependable, trustworthy seamless personalized experience all the time, anywhere.
Dynamic Smart tourism recommender systems address the location and adaptability needs as well, with the inclusion of self-indulging machine-learning capabilities to help the travelers in an informed decision making process.
To be able to be dynamic, self-learning, intelligent, dependable, adaptable, personalized, hyper-relevant and prolific, the recommender system first needs to gather information about the subject throughout its travel journey from before traveling to in-travel to after travel through various sources such as internet searches, flights, accommodations, cities, bookings, hotels, activities, shopping preferences etc. So that it can understand its subject’s needs and choices and be able to recommend something, according to that information.
It also needs to gather information about other similar subjects, by the rule of generality, it is most likely that the subject would purchase something similar to what its peers are purchasing.
It also needs to gather information about the location of the subject to be able to make recommendations based on the subject’s location.

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