A SURVEY BASED ON TRAVEL RECOMMENDATION SYSTEM USING BIG DATA ANALYTICS

now a day, traveling recommendation system is the most important factor for user. There are many existing approaches in the travel recommendation system. This paper explain a travel recommendation system using travelers with large amount of data. It recommends personalized users interest and recommend a sequence of travel interest instead of an individual point of interest. The existing method cannot complete the constraint i.e. personalized and consecutive recommendation together. To solve the problem a topical package model is created which automatically mine the user interest of attributes like time, cost and season to solve the problem of providing personalized and. The proposed system uses the traveler’s user experience of social media which is used to map each user and routes explanation. To recommend personalized POI sequence, first famous routes are stratified as per the similarity between user package and route package. Then high stratified routes are more optimized by using social similar users travel records for more accuracy.