Results 81 to 90 of about 228 (183)

A Hypergraph Structure-Based Aggregation Network for Next POI Recommendation

IEEE Access
The next Point-of-Interest (POI) recommendation can effectively help users find places they are interested in, which is one of the important applications of location-based social networks (LBSNs).
Zhen Zhang, Jun Chen, Xiangguo Zhao, Yaqi Gong, Jianyu Ren, Jintao Ouyang   +5 more
doaj   +1 more source

Vehicle Trajectory Prediction via Urban Network Modeling. [PDF]

Sensors (Basel), 2023
Qin X, Li Z, Zhang K, Mao F, Jin X.
europepmc   +1 more source

Language-Guided Spatio-Temporal Context Learning for Next POI Recommendation

ISPRS International Journal of Geo-Information
With the proliferation of mobile internet and location-based services, location-based social networks (LBSNs) have accumulated extensive user check-in data, driving the advancement of next Point-of-Interest (POI) recommendation systems. Although existing
Chunyang Liu, Chuxiao Fu
doaj   +1 more source

Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times

ISPRS International Journal of Geo-Information
Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through ...
Mikel Barrena-Herrán, Itziar Modrego-Monforte, Olatz Grijalba   +2 more
doaj   +1 more source

A human mobility dataset collected via LBSLab. [PDF]

Data Brief, 2023
Zhang Y, Gong Q, Chen Y, Xiao Y, Wang X, Hui P, Fu X.   +6 more
europepmc   +1 more source

Library Book Sharing Network (LBSN)

International Journal of Computer Applications, 2019
Maham Nasrullah, Syeda Wajiha, Basit Hassan, Syed Asif   +3 more
openaire   +1 more source

Improving Location Recommendations Based on LBSN Data Through Data Preprocessing

Electronics
The accurate prediction of the next location in a sequence is highly beneficial for users of mobile applications. In this study, we investigate how various data preprocessing techniques affect the performance of location recommendation systems. We utilize datasets from Foursquare and Twitter, incorporating users’ historical check-ins. Key preprocessing
Robert Bembenik, Mateusz Orzoł, Piotr Maciąg   +2 more
openaire   +1 more source

Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

IEEE Access
The growth of the tourism industry has greatly boosted the Point-of-Interest (POI) recom- mendation tasks using Location-based Social Networks (LBSNs). The ever-evolving nature of user preferences poses a major problem. To address this, we propose a Long-
Malika Acharya, Krishna Kumar Mohbey, Dharmendra Singh Rajput   +2 more
doaj   +1 more source

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network's Big Data.

Comput Math Methods Med, 2021
Hou L, Liu Q, Nebhen J, Uddin M, Ullah M, Khan NU.   +5 more
europepmc   +1 more source

Pedestrian Flow Prediction and Route Recommendation with Business Events. [PDF]

Sensors (Basel), 2022
Gu J, Song C, Ren Z, Lu L, Jiang W, Liu M.   +5 more
europepmc   +1 more source