Results 61 to 70 of about 3,208 (177)
Location Recommendation System based on LBSNS
Journal of Digital Convergence, 2014 In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc.
Ku-Imm Jung +3 more
openaire +2 more sources
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
HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario [PDF]
, 2018 Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on theWeb such as their tourist experiences.
Andrea Gómez-Oliva +6 more
core +4 more sources
Geo-Social Group Queries with Minimum Acquaintance Constraint
, 2017 The prosperity of location-based social networking services enables geo-social group queries for group-based activity planning and marketing. This paper proposes a new family of geo-social group queries with minimum acquaintance constraint (GSGQs), which
Hu, Haibo +4 more
core +1 more source
LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks [PDF]
, 2017 Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. Asa result of such a sharing of check-in information with each other, users can be influenced to visit (or ...
Bedathur, Srikanta +2 more
core +1 more source
Sentiment Severity on Location-Based Social Network (LBSN) Data of Natural disasters
International journal of recent technology and engineering, 2020 Social media emerged as one of the key components to reach disaster affected people, as they supplement planning and operational coordination. Sentiment analysis was expended to identify, extract or characterize subjective information, such as opinions ...
semanticscholar +1 more source
A Self-Attention Model for Next Location Prediction Based on Semantic Mining
ISPRS International Journal of Geo-Information, 2023 With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular.
Eric Hsueh-Chan Lu, You-Ru Lin
doaj +1 more source
Where could we go? Recommendations for groups in location-based social networks [PDF]
, 2017 | openaire: EC/H2020/654024/EU//SoBigDataLocation-Based Social Networks (LBSNs) enable their users to share with their friends the places they go to and whom they go with. Additionally, they provide users with recommendations for Points of Interest (POI)
Ayala-Gomez, F +4 more
core +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 ...
Yang, Dingqi +3 more
openaire +1 more source
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