Results 61 to 70 of about 687 (209)
A Paradigm of Temporal‐Weather‐Aware Transition Pattern for POI Recommendation
CAAI Transactions on Intelligence Technology, Volume 10, Issue 6, Page 1675-1687, December 2025.ABSTRACT Point of interest (POI) recommendation analyses user preferences through historical check‐in data. However, existing POI recommendation methods often overlook the influence of weather information and face the challenge of sparse historical data for individual users.
Junyang Chen +6 more
wiley +1 more source
Next POI Recommendation via Graph Embedding Representation From H-Deepwalk on Hybrid Network
IEEE Access, 2019 With the rapid development of location-based social networks (LBSNs), point of interest (POI) recommendation has become more and more popular personalized service.
Kang Yang, Jinghua Zhu
doaj +1 more source
Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks
ISPRS International Journal of Geo-Information, 2021 Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult.
Yi-Chung Chen +3 more
doaj +1 more source
Semantic analysis of spatial temporal trajectory in LBSNs [PDF]
SCIENTIA SINICA Informationis, 2017 Spatial temporal data has associated multidimensional features. Deep learning has attracted much attention due to its ability to perform high-level abstraction of complex data. In this paper, we give the definition of the track-data based on its characteristics, and build a spatial temporal semantic trajectory model using Word2vec as its foundation. We
Haoteng YIN, Yang LIU
openaire +1 more source
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach [PDF]
The World Wide Web Conference, 2019 Location Based Social Networks (LBSNs) have been widely used as a primary data source to study the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users' mobility homophily and social proximity, and show that mobility and social features can help friendship and location ...
Dingqi Yang +3 more
openaire +3 more sources
A Potential Friends Recommendation Model for Location-Based Social Network
Dianxin kexue, 2014 To make service convenient and improve user experience degrees, recommendation service has become more and more important to users in the location-based social network（LBSN）.
Xiaochen Sun, Yabin Xu
doaj +2 more sources
ISPRS International Journal of Geo-Information, 2020 The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple ...
Naimat Ullah Khan +5 more
doaj +1 more source
Privacy Preserving POI Recommendation Algorithm Based on LSH [PDF]
Jisuanji gongcheng, 2019 The Location-Based Social Network(LBSN) uses the user’s check-in data to recommend the Point of Interest (POI),but for the consideration of data privacy,various social platforms are unwilling to share data directly.In order to provide a better POI ...
SHEN Xindi,ZHAI Dongjun,ZHANG Detian,LIU An
doaj +1 more source
IEEE Access, 2020 The location-based social network (LBSN) contains a large amount of user check-in data informations, in order to better improve the recommendation performance and avoid the impact of user check-in data sparsity.
Juan Li, Xiaofeng Wang, Wanjing Feng
doaj +1 more source
An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks
ISPRS International Journal of Geo-Information, 2018 Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction.
Elahe Khazaei, Abbas Alimohammadi
doaj +1 more source