The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …

Traditional occupant behavior modeling has been studied at the building level, and it has
become an important factor in the investigation of building energy consumption. However …

The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

Next Point-of Interest (POI) recommendation plays an important role in location-based
applications, which aims to recommend the next POIs to users that they are most likely to …

The task of next Point-of-Interest (POI) recommendation aims at recommending a list of POIs
for a user to visit at the next timestamp based on his/her previous interactions, which is …

Learning which Point-of-Interest (POI) a user will visit next is a challenging task for
personalized recommender systems due to the large search space of possible POIs in the …

Abstract Location-based Social Networks (LBSNs) enable users to socialize with friends and
acquaintances by sharing their check-ins, opinions, photos, and reviews. A huge volume of …

Human mobility prediction is a core functionality in many location-based services and
applications. However, due to the sparsity of mobility data, it is not an easy task to predict …

The past flourishing years of sequential location-based services began with the introduction
of the Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of …

Abstract Recently, Next Point-of-Interest (POI) Recommendation which proposes users for
their next visiting locations, has gained increasing attention. A timely and accurate next POI …