Results 51 to 60 of about 817 (198)

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

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   +3 more sources

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

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

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

Adaptive Hyperlink‐Induced Topic Search‐Based Deep Learning Model for Topic Refinement and Modeling Using BERT With Residual Generative Network

open access: yesIET Computers &Digital Techniques, Volume 2026, Issue 1, 2026.
Due to the rapid growth of online and technology development, every people have an own mobile to search their doubts and easily find it out within a second. Hypertext induced topic search (HITS) is a link analyzed algorithm utilized to find reliable websites that satisfy broad‐topic queries.
John Vaseekaran S.   +2 more
wiley   +1 more source

Developing a Research Framework of Augmented Reality-Based Location-Based Social Networks in the GIScience Context [PDF]

open access: yes, 2020
Location-based social networks (LBSN) and augmented reality (AR) are closely interconnected concepts which both are important research areas to the GIScience community.
Chengbi Liu
core   +1 more source

Traffic‐Aware Next Point of Interest Recommendation for Urban Mobility Optimization: A Hypergraph Neural Network Approach

open access: yesIET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
This paper proposes a multi‐level aggregated hypergraph network (FDTAN) to address POI recommendation challenges in intelligent transportation systems. By integrating distance‐zone partitioning with spatial‐temporal self‐attention mechanisms, the model captures spatial heterogeneity and dynamic preferences of transportation nodes, effectively resolving
Fang Liu   +3 more
wiley   +1 more source

Customized Tip Summarization Based on Customer Preferences in Online Social Networks

open access: yesApplied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.
There has been a serious issue with overload of data because of the abundance of consumer‐generated suggestions on location‐based social networks (LBSNs). The amount and fragmented nature of all these brief comments make it hard for users to efficiently gather relevant details customized to their interests, even when they provide useful, current data ...
Muhmmad Al-Khiza’ay   +5 more
wiley   +1 more source

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