Abstract
Travel and tourism is the leading application field in the era of 21st century. As it is not possible for the tourist to always prefer the guide book, guide or any other sources for the information of any location. Our work aims to address these challenges by proposing the algorithms to recommend personalized travel itineraries for both individuals and group of tourist based on their interest preferences. The main approach of this recommendation system is to improvise the older existing applications by providing various analytical results. In-order to make best plans and knowing multiple details about the unknown places is quite a difficult issue. So our recommendation system helps to solve such problems. It uses Geomatic mapping for location which is stored in cloud, also can be viewed in offline. Experimental evaluation in Flickr dataset of multiple cites forming links between images sharing common metadata from tourist. Weather is predicted using Hadoop, also the way of travelling to communicate across different places is identified by the recommendation system. One other facility in this recommendation system consists of a translator, translates words or sentences into native language. These patterns score found in historical data can be used for predicting the future.
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