"In today's society, social networks are a popular way to connect with friends and family and share what's going on in your life. With the Internet connecting us all closer than ever before, it is increasingly common to use social networks to meet new friends online that share similar interests instead of only connecting with those you already know. For the problem of attempting to connect people with similar interests, this paper proposes the foundation for a Geo-social network that aims to extract the semantic meaning from users' location history and use this information to find the similarity between users. Once the similarity scores are obtained, the results are examined to extract the groups of similar users for the Geo-social network. Computing similarity for a large number of users and then grouping based on the results is a computationally intensive task, but fortunately Apache Spark can be leveraged to execute the comparison and clustering of users in parallel across multiple computers, increasing the computation speed when compared to a centralized version and working quickly enough to suggest friends in real time for a given user"--Abstract, page iii.
M.S. in Computer Science
Missouri University of Science and Technology
vii, 28 pages
© 2018 Tyler Clark Percy, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Percy, Tyler Clark, "Analyzing large scale trajectory data to identify users with similar behavior" (2018). Masters Theses. 7807.