Recommendation in location based social networks

• Main Author: Chen, Long.
• Other Authors: School of Computer Engineering
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
• Online Access: http://hdl.handle.net/10356/52049
• Institution: Nanyang Technological University

Recommendation in Location Based Social Networks (LBSNs) is an emerging research topic. Compared to conventional recommendation systems, there is much more information could be utilized in a LBSN recommendation system. In this report, the author first proposes a new method of utilizing spatial sensitivity to generate dynamic weightage average score for combining the User Collaborative Filtering and the Geographical Influence in a personalized fashion for each single user. Temporal information in check-ins which interconnects users and point-of-interests have significant value in LBSNs. In this report, the author also discusses how to leverage temporal information in the User Collaborative Filtering and the Geographical Influence. Finally, a unified framework for all proposed methods is defined in this report. A detailed empirical experiment is included in this report which provides a comprehensive comparison of the performance of proposed methods against baseline methods.