Results 51 to 60 of about 687 (209)

Spatio-Temporal Transformer Recommender: Next Location Recommendation with Attention Mechanism by Mining the Spatio-Temporal Relationship between Visited Locations

open access: yesISPRS International Journal of Geo-Information, 2023
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of ...
Shuqiang Xu, Qunying Huang, Zhiqiang Zou
doaj   +1 more source

Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users

open access: yesISPRS International Journal of Geo-Information, 2015
Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel ...
Yeran Sun, Ming Li
doaj   +1 more source

Assessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets [PDF]

open access: yes, 2018
Location-based social networks (LBSN) have provided new possibilities for researchers to gain knowledge about human spatiotemporal behavior, and to make predictions about how people might behave through space and time in the future.
Li, Ming   +3 more
core   +1 more source

Location Recommendation System based on LBSNS

open access: yesJournal 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

A study of neighbour selection strategies for POI recommendation in LBSNs [PDF]

open access: yesJournal of Information Science, 2018
Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction.
Carlos Rios   +2 more
openaire   +2 more sources

A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data

open access: yes, 2023
Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints.
Antoniou, Constantinos   +2 more
core   +2 more sources

LBSN 2010 workshop report

open access: yes, 2011
Social networking services have become very popular in recent years, especially among younger people. While many people still sit behind a desktop computer to upload photos, write blogs and communicate with friends in the virtual world, an increasing ...
Wen-Chih Peng, Xing Xie
core   +1 more source

A Self-Attention Model for Next Location Prediction Based on Semantic Mining

open access: yesISPRS 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

Unfolding the interplay of self-identity and expressions of territoriality in location-based social networks [PDF]

open access: yes, 2017
Self-identity in mobile location-based social networks (LBSN) is a relatively underexplored topic. In this paper, we present our initial understandings on the role that LBSN play in the self-identity of its users and introduce a relationship between self-
Chamberlain, A.   +23 more
core   +1 more source

Complementing Location-Based Social Network Data With Mobility Data: A Pattern-Based Approach [PDF]

open access: yes, 2022
Location-Based Social Networks can be profitably exploited to characterize citizens’ activities in urban environments. However, collecting LBSN is potentially challenging due to privacy concerns, connectivity issues, and potential imbalances in LBSN ...
Daraio, Elena   +3 more
core   +1 more source

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