Point-of-interest (POI) recommendation service has drawn growing attention with the
widespread popularity of location-based social networks (LBSNs). Recent research methods …

This paper studies the problem of learning latent representations or embeddings for users in
social networks, by leveraging relationship semantics associated with each link. User …

With the prevalence of smart mobile devices and location-based services, uncovering social
relationships from human mobility data is of great value in real-world spatio-temporal …

We focus on representation learning for social link inference based on user trajectory data.
While traditional methods predict relations between users by considering hand-crafted …

Users of social networks have a variety of social statuses and roles. For example, the users
of Weibo include celebrities, government officials, and social organizations. At the same …

Location-based social networks (LBSNs) have been studied extensively in recent years.
However, utilizing real-world LBSN data sets yields several weaknesses: sparse and small …

Nowadays, a lot of people possess accounts on multiple online social networks, eg,
Facebook and Twitter. These networks are overlapped, but the correspondences between …

Location-based social networks (LBSNs) have recently attracted a lot of attention due to the
number of novel services they can offer. Prior work on analysis of LBSNs has mainly focused …

Learning a vector representation of locations that reflect human mobility patterns is useful for
various tasks, including location recommendation, city planning, urban analysis, and even …

Accurate user representation learning has been proven fundamental for many social media
applications, including community detection, recommendation, etc. A major challenge lies in …