Trajectory data mining: an overview
Y Zheng - ACM Transactions on Intelligent Systems and …, 2015 - dl.acm.org
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 …
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm
With the surging of smartphone sensing, wireless networking, and mobile social networking
techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising …
techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising …
Recommender systems survey
Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …
on demographic, content-based and collaborative filtering. Currently, these systems are …
Deep reinforcement learning for page-wise recommendations
Recommender systems can mitigate the information overload problem by suggesting users'
personalized items. In real-world recommendations such as e-commerce, a typical …
personalized items. In real-world recommendations such as e-commerce, a typical …
Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization
J Huang, Z Tong, Z Feng - International Journal of …, 2022 - Wiley Online Library
With the popularity of Internet of Things (IoT), Point‐of‐Interest (POI) recommendation has
become an important application for location‐based services (LBS). Meanwhile, there is an …
become an important application for location‐based services (LBS). Meanwhile, there is an …
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
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 …
discover attractive locations. However, extreme sparsity of user-POI matrices creates a …
Urban computing: concepts, methodologies, and applications
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 …
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …
Methodologies for cross-domain data fusion: An overview
Y Zheng - IEEE transactions on big data, 2015 - ieeexplore.ieee.org
Traditional data mining usually deals with data from a single domain. In the big data era, we
face a diversity of datasets from different sources in different domains. These datasets …
face a diversity of datasets from different sources in different domains. These datasets …
[PDF][PDF] Where you like to go next: Successive point-of-interest recommendation
Personalized point-of-interest (POI) recommendation is a significant task in location-based
social networks (LBSNs) as it can help provide better user experience as well as enable …
social networks (LBSNs) as it can help provide better user experience as well as enable …
Recommendations in location-based social networks: a survey
Recent advances in localization techniques have fundamentally enhanced social
networking services, allowing users to share their locations and location-related contents …
networking services, allowing users to share their locations and location-related contents …