A Study on Personalized Travel Sequence Recommendation on Multi-Source Social Media

Abstract

Now a days, traveling recommendation is important for users who plan for traveling. There are many existing techniques which are used for travel recommendation. This paper explains a personalized travel sequence recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. It recommends personalized users travel interest and recommend a sequence of travel interest instead of an individual point of interest. The existing system cannot complete the requirement i.e. personalized and sequential recommendation together, source and destination points for each tourist. Large amount of data can be collected from the Internet and travel guides, but these resources normally recommend individual Point of Interest (POI) that is considered to be familiar, but they do not provide sufficient information to the interest preference of the users or hold to their trip constraints. To solve the problem of providing personalized and sequential travel package recommendation, a topical package model is created using social media data in which automatically mine user travel interest with another attribute like time, cost, and season of traveling. The proposed system uses the travelogues and photos of social media which map each user and routes description to the topical package area to induce user topical package model and route topical package model. In addition, the huge volume of information makes it a challenge for every tourist to pay attention to a potential set of POIs to make a visit in any unknown city. After the tourist discovers an acceptable set of POIs to go to, it’ll take abundant time and energy for him/her to make a brief outline of the suitable duration of the visit at every POI and the order in which to visit the POIs.To suggest 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.