Results 21 to 30 of about 1,557 (194)

POI Neural-Rec Model via Graph Embedding Representation

Tsinghua Science and Technology, 2021
With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people’s ability to mine their POIs.
Kang Yang, Jinghua Zhu, Xu Guo
doaj   +1 more source

BERT4Loc: BERT for Location—POI Recommender System

Future Internet, 2023
Recommending points of interest (POI) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it is important to analyze users’ historical behavior and preferences.
Syed Raza Bashir, Shaina Raza, Vojislav B. Misic   +2 more
openaire   +3 more sources

Exploring Temporal and Spatial Features for Next POI Recommendation in LBSNs

IEEE Access, 2021
With the increasing popularity of Location-Based Social Networks (LBSNs), a significant volume of check-in data of users has been generated. Such massive data brings difficulties for the users to efficiently retrieve their desired point-of-interest (POI).
Miao Li, Wenguang Zheng, Yingyuan Xiao, Ke Zhu, Wei Huang   +4 more
doaj   +1 more source

Geographically Insensitive Spatial-Temporal POI Recommendation Based on Heterogeneous Graph Embedding [PDF]

Jisuanji kexue yu tansuo
The increasingly large scale of location-based social networks (LBSN) promotes the rapid development of point-of-interest (POI) recommendation business.
LI Manwen, ZHANG Yueqin, ZHANG Chenwei, ZHANG Zehua
doaj   +1 more source

Relation Embedding for Personalised POI Recommendation

CoRR, 2020
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-
Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz   +3 more
openaire   +2 more sources

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

IEEE Access, 2020
User-POI rating matrix is one of the current research hotspot of POI recommendation algorithms, the goal of which is to obtain the POIs with the highest user satisfaction.
Xiangfu Meng, Jinfeng Fang
doaj   +1 more source

A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

ISPRS International Journal of Geo-Information, 2020
With the growing popularity of location-based social media applications, point-of-interest (POI) recommendation has become important in recent years. Several techniques, especially the collaborative filtering (CF), Markov chain (MC), and recurrent neural
Chunyang Liu, Jiping Liu, Shenghua Xu, Jian Wang, Chao Liu, Tianyang Chen, Tao Jiang   +6 more
doaj   +1 more source

Personalized Geographical Influence Modeling for POI Recommendation [PDF]

IEEE Intelligent Systems, 2020
Point-of-interest (POI) recommendation has great significance in helping users find favorite places from a large number of candidate venues. One challenging in POI recommendation is to effectively exploit geographical information since users usually care about the physical distance to the recommended POIs.
Yanan Zhang, Guanfeng Liu 0001, An Liu 0002, Yifan Zhang, Zhixu Li, Xiangliang Zhang 0001, Qing Li 0001   +6 more
openaire   +2 more sources

A content-location-aware personalized POI recommendation model

Shenzhen Daxue xuebao. Ligong ban, 2022
Aiming at the data sparsity problem of user-POI matrix in point of interest (POI) recommendation, the more and more studies have explored the contextual factors such as geographical location, content information and social relations to deal with the ...
LIANG Bi, LIU Dujin, XIONG Lun, XU Xiaohong   +3 more
doaj   +1 more source

Interpretable Embeddings for Next Point-of-Interest Recommendation via Large Language Model Question–Answering

Mathematics
Next point-of-interest (POI) recommendation provides users with location suggestions that they may be interested in, allowing them to explore their surroundings.
Jiubing Chen, Haoyu Wang, Jianxin Shang, Chaomurilige   +3 more
doaj   +1 more source