Results 171 to 180 of about 30,535 (208)

POI Recommendation Based on Heterogeneous Network

2020
With the development of wireless networks and positioning technologies, location-based social networks (LBSN) have gained popularity. More and more people share experiences about points of interest (POI) through “check-in” behavior. Mining the check-in data can help people discover the POI they are interested in.
Yan Wen, Jiansong Zhang, Geng Chen, Xin Chen, Ming Chen   +4 more
openaire   +1 more source

Current location-based next POI recommendation

Proceedings of the International Conference on Web Intelligence, 2017
Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours.
Shokirkhon Oppokhonov, Seyoung Park, Isaac K. E. Ampomah   +2 more
openaire   +1 more source

Deep Transfer Learning for Successive POI Recommendation

2021
Personalized POI recommendation attracts more and more attention from both industrial and research fields. Due to data collection mechanism, it is common to see data collection with the unbalanced spatial distribution. For example, some cities may release check-ins for multiple years while others only release a few days of data.
Haining Tan, Di Yao, Jingping Bi
openaire   +1 more source

Personalized POI Recommendation Model in LBSNs

2017
The development of location-based social networks (LBSNs) generates large volume of check-in data. Point-of-interest recommendation (POI) is important for users to find some attractive venues, sometimes when users are in some places far away from their living cities.
Zhong Guo, Ma Changyi
openaire   +1 more source

POI Recommendation Based on Heterogeneous Graph Embedding

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019
With the development and popularity of social networks, many human beings prefer to share their experiences on these networks. There are various methods proposed by the researcher which utilized user-generated content in the location-based social networks (LBSN) and recommend locations to users.
Sima Naderi Mighan, Mohsen Kahani, Fateme Pourgholamali   +2 more
openaire   +1 more source

Capturing Geographical Influence in POI Recommendations

2013
Point-of-Interest POI recommendation is a significant service for location-based social networks LBSNs. It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users' interests depends on three factors: user preference, social influence, and geographical influence.
Shenglin Zhao, Irwin King, Michael R. Lyu   +2 more
openaire   +1 more source

Flexible POI Recommendation Based on User Situation

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019
Location-based social networks (LBSNs) become an essential part of our lives as these services can assist users in finding interesting point-of-interest (POI). Many studies have been conducted to perform POI recommendations with various factors, such as user's check-in records, geographic information, and social relationship.
Sein Jang, Jeong-Hun Kim, Aziz Nasridinov   +2 more
openaire   +1 more source

A Context-Aware POI Recommendation

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), 2021
Tipajin Thaipisutikul, Ying-Nong Chen
openaire   +1 more source

Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

IEEE Transactions on Knowledge and Data Engineering, 2022
Pengpeng Zhao, Anjing Luo, Jiajie Xu
exaly  

Trust enhanced POI recommendation with collaborative learning

Intelligent Data Analysis: An International Journal
With the rapid development and popularization of smart mobile devices, users tend to share their visited points-of-interest (POIs) on the network with attached location information, which forms a location-based social network (LBSN). LBSNs contain a wealth of valuable information, including the geographical coordinates of POIs and the ...
Wu, Renhui, Xu, Hui, Rui, Xiaobin, Wang, Zhixiao   +3 more
openaire   +1 more source