Social Network Based Tourist Recommendation System

• Main Authors: Wen-Kuan Chiu, 邱文寬
• Other Authors: 葉耀明
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/43421837965886354843

碩士 === 國立臺灣師範大學 === 資訊工程學系 === 102 === Facebook is the most influential community platform in the world; according to the statistics officially released by Facebook, up to the third quarter of 2013, the number of active users has reached 1,190 millions. With the growth of users, Facebook has become a producer of Big data, and the vast amount of status updates posted by users, which conceal all kinds of useful information, have triggered many research projects focus on Facebook parameters such as the like count, the group count, etc. This research is based on Facebook communities. Through the analyses of the SCAN clustering algorithm, the users would be able to understand the relationships and connections between their mutual friends as stated in the results of grouping. Besides, the results of grouping have combined the Big Five personality model. By means of Facebook parameters, users can easily find out the personality traits of their friends. In this research, FQL was adopted to obtain the information of users themselves and friends around them for further analyses. When the number of friends is increasing, the SCAN clustering algorithm can improve the efficiency when it comes to grouping manually on Facebook. Lastly, this system has developed an integration with tourist information for the purpose of backpacking, so suitable travel companions will be recommended according to users' travel itineraries..