As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Next POI recommendation intends to forecast users' immediate future movements given their
current status and historical information, yielding great values for both users and service …

In location-based social networks (LBSNs), extensive user check-in data incorporating user
preferences for location is collected through Internet of Things devices, including cell …

Next POI recommendation has been studied extensively in recent years. The goal is to
recommend next POI for users at specific time given users' historical check-in data …

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 …

Session-based recommendation (SBR), which mainly relies on a user's limited interactions
with items to generate recommendations, is a widely investigated task. Existing methods …

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there's
an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially …

Accurately recommending the next point of interest (POI) has become a fundamental
problem with the rapid growth of location-based social networks. However, sparse …