Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation
In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding env...
Main Authors: | Xu Jiao, Yingyuan Xiao, Wenguang Zheng, Lei Xu, Hui Wu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8889741/ |
Similar Items
-
Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
by: Chonghuan Xu, et al.
Published: (2021-10-01) -
Exploring Temporal and Spatial Features for Next POI Recommendation in LBSNs
by: Miao Li, et al.
Published: (2021-01-01) -
A hybrid recommender system using topic modeling and prefixspan algorithm in social media
by: Ali Akbar Noorian Avval, et al.
Published: (2023-01-01) -
Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
by: Jingbo Song, et al.
Published: (2023-05-01) -
Temporal-Spatial Recommendation for Caching at Base Stations via Deep Reinforcement Learning
by: Kaiyang Guo, et al.
Published: (2019-01-01)