Clustering and route optimization for personalized travel sequence recommendation system

Abstract

Travel route planning is one of the major significant steps, used for a tourist to arrange his/her trip. Earlier to the traveling of an unknown area, the tourists of people have questions regarding the arrangements of their trips. Though users are able to take assistance of the travel guide or raise questions toward web based communities, the procedure is usually not enough and the results might not be customized. Big media is mainly the growth of social media (e.g., Facebook, Flickr, Twitter etc.) offering large opportunities to deal with several challenging issues, for instance, Global Positioning System (GPS) estimation and travel recommendation. Travelogue websites (e.g., www.igougo.com) offer rich information regarding landmarks and traveling knowledge written by users. Travel Recommender Systems (TRSs) help to deal with the dataand#8223;s, tourists might experience, when they search for options, by providing them selected routes with the purpose of matching their personal preferences. The major issue of TRS was the ability to obtain relevant information about preferences and requirements of the users and identify the Points of Interest (POIs) with the purpose of matching with his or her interests. Automatic TRSs are extremely preferred to plan the customized trip related to the usersand#8223; preferences. A number of automatic TRSs methods have been introduced recently to improve TRS system. These methods are classified into collaborative filtering, content-based filtering, and knowledge-based methods. These methods are capable of recommending personalized travel by taking all POIs into consideration. newline