The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …

Urban big data fusion creates huge values for urban computing in solving urban problems.
In recent years, various models and algorithms based on deep learning have been …

Spatial and temporal contextual information plays a key role for analyzing user behaviors,
and is helpful for predicting where he or she will go next. With the growing ability of …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …

With the recent surge of location based social networks (LBSNs), activity data of millions of
users has become attainable. This data contains not only spatial and temporal stamps of …

Point-of-Interest (POI) recommendation has become an important means to help people
discover attractive locations. However, extreme sparsity of user-POI matrices creates a …

In recent years, we have witnessed a rapid surge in assisted living technologies due to a
rapidly aging society. The aging population, the increasing cost of formal health care, the …

In recent years, there has been an increased interest in more user-centered evaluation
metrics for recommender systems such as those mentioned in [49]. It has also been …

Even though human movement and mobility patterns have a high degree of freedom and
variation, they also exhibit structural patterns due to geographic and social constraints …

Urbanization's rapid progress has modernized many people's lives but also engendered big
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …