Abstract
Location-based social networks (LBSNs) have gained significant popularity nowadays and their location-sharing features promote social interactions and foster community formation. However, friend recommendation on LBSNs remains a challenging research problem. As check-in trajectories indicate user proximity, we propose a deep learning method to represent users and locations by mining user trajectories and generate top-k friend recommendation.
Recommended Citation
Chen, Zhipeng and Zhan, Yongcheng, "Distributed Representations of Users and Locations for Friendship Recommendation on Location-Based Social Network" (2019). AMCIS 2019 Proceedings. 26.
https://aisel.aisnet.org/amcis2019/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/26
Distributed Representations of Users and Locations for Friendship Recommendation on Location-Based Social Network
Location-based social networks (LBSNs) have gained significant popularity nowadays and their location-sharing features promote social interactions and foster community formation. However, friend recommendation on LBSNs remains a challenging research problem. As check-in trajectories indicate user proximity, we propose a deep learning method to represent users and locations by mining user trajectories and generate top-k friend recommendation.