The evolution of the internet over the past years has given destinations, suppliers of tourism services and intermediaries a wide range of new possibilities to get into contact with their customers. Destinations are at the heart of travel decisions but yet it is difficult for travelers to find quality information relating to destinations and even more for Destination Marketing Organizations (DMOs) to distribute their tourism offer. Thus, a wide range of new technologies - so called recommendation technologies - have emerged aligning destination information with booking facilities to convert destination search into online bookings. Recommender systems have already been applied successfully in the private market but yet it is difficult to determine the added value for the service offered. This paper discusses different recommendation technologies, presents current business models applied in e-commerce and provides suggestions on how those could be applied to recommender systems

Recommender systems: Do they have a viable business model in e-tourism?

Rabanser, Ulrich;Ricci, Francesco
2005

Abstract

The evolution of the internet over the past years has given destinations, suppliers of tourism services and intermediaries a wide range of new possibilities to get into contact with their customers. Destinations are at the heart of travel decisions but yet it is difficult for travelers to find quality information relating to destinations and even more for Destination Marketing Organizations (DMOs) to distribute their tourism offer. Thus, a wide range of new technologies - so called recommendation technologies - have emerged aligning destination information with booking facilities to convert destination search into online bookings. Recommender systems have already been applied successfully in the private market but yet it is difficult to determine the added value for the service offered. This paper discusses different recommendation technologies, presents current business models applied in e-commerce and provides suggestions on how those could be applied to recommender systems
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/2343
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