The Application of Data Mining Algorithms in the Construction of Travel Recommendation System

Abstract

Photos shared by users of online social network often associated with tags, texts, geographic and time information. These data are ideal research resource which can be used by researchers to observe people's interests and behaviors. They can also be used to analyze people's travel behaviors and interests because it can reflect people's travel activities and experiences directly. This project aimed to consolidate people into groups based on their travel preferences of travel season, travel time span, scenery type of travel location and then analyze popular travel places for different people groups. The travel preferences were analyzed from extracted data applying PHP language and Naive Bayes classification approach. According to the analyzed data, people were divided into groups. Then, DBSCAN clustering was used to get popular locations based on the different groups. As a result, 80 thousand photo data of 5 cities have been collected from Flickr by Phython. It has been showed that people's preference of season in most of the mined cities was spring and summer and the preference of time span was short. Moreover, mined people has been divided into 16 groups according to their preference and based on the groups popular locations have been calculated by DBSCAN clustering. Theses locations have been provided in a recommendation website which was built in this project. For the future work, mining Flickr data associated with other travel social network sites could be a good way to build a more effective recommendation system.