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 …

With the continuous accumulation of users' check‐in data, we can gradually capture users'
behavior patterns and mine users' preferences. Based on this, the next point‐of‐interest …

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 The Corona Virus Disease 2019 has a great impact on public health and public
psychology. People stay at home for a long time and rarely go out. With the improvement of …

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 …

We present a novel approach for estimating the proportional distributions of function types
(ie functional distributions) in an urban area through learning semantics preserved …

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains
of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural …

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 …

Modeling users' short-term dynamic and long-term static interests to enhance Point-of-
Interests (POI) recommendation performance has shown lots of advantages. Since users' …

Abstract A Recommendation System (RS) is an intelligent computer based system which
provide valuable suggestions to the user and are used in several domains. Social media …