Results 31 to 40 of about 443,564 (206)

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   +4 more sources

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

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

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   +3 more sources

Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

, 2023
As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places.
Chen, T, Long, J, Xu, G, Nguyen, QVH, Zheng, K, Yin, H   +5 more
core   +1 more source

KDRank: Knowledge-driven user-aware POI recommendation

, 2023
Accurate user modeling is crucial for point-of-interest (POI) recommendation as it can significantly improve user satisfaction with recommended POIs and enrich user experience.
Bian, Jixin, Deng, Junhui, Shen, Guojiang, Kong, Xiangjie, Liu, Zhi, Jixin Bian, Chenwei Zhang, Zhang, Deju, Deju Zhang, Xiangjie Kong, Zhi Liu, Zhang, Chenwei, Junhui Deng, Guojiang Shen   +13 more
core   +1 more source

Survey on Deep Learning Applications in Point-of-Interest Recommendation [PDF]

Jisuanji kexue yu tansuo
With the proliferation of mobile devices and location-based services, massive user check-in data from location-based social networks have generated widespread attention for point-of-interest (POI) recommendation as an important location service ...
HUANG Ping, WANG Feng, LIU Guangteng, WU Zhongbo, LI Xiaoli, HUANG Jinzhou
doaj   +1 more source

A Privacy-Preserving Time-Aware Method for Next POI Recommendation

, 2023
Compared with traditional point-of-interest (POI) recommendation, next POI recommendation is more difficult and requires comprehensive consideration of users’ behavior patterns, spatial–temporal context, and other information.
Yue Geng, Jianyong Fan, Chenxi Pan, Shuyu Li   +3 more
core   +1 more source

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