Mining Location Influence for Location Promotion in Location-Based Social Networks
IEEE Access, 2018 With the extensive application and in-depth study of location-based social networks (LBSNs), more and more businesses are utilizing the new social platform to promote their products and services.
Fei Yu, Shouxu Jiang
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The canary in the city: indicator groups as predictors of local rent increases
EPJ Data Science, 2018 As cities grow, certain neighborhoods experience a particularly high demand for housing, resulting in escalating rents. Despite far-reaching socioeconomic consequences, it remains difficult to predict when and where urban neighborhoods will face such ...
Aike A. Steentoft +3 more
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Personalized Recommendation of Tourist Attractions based on LBSN
DEStech Transactions on Computer Science and Engineering, 2018 Photos metadata in Location-Based Social Networks (LBSN) contain rich time and space information, these metadata provide the basis for the research of personalized recommendation of tourist attractions. The existing methods have many problems such as low accuracy of recommendation and single type of attractions recommendation.
Huifang Lv +4 more
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Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data. [PDF]
Sensors (Basel), 2023 Chang W, Sun D, Du Q.
europepmc +1 more source
A graph neural network framework based on preference-aware graph diffusion for recommendation. [PDF]
Front Psychiatry, 2022 Shu T, Shi L, Zhu C, Liu X.
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Smartphones and Location Awareness in Brazil: Users’ Reactions
Paidéia (Ribeirão Preto), 2014 The general objective of this study was to gain detailed information on how Brazilians are using the many features of their smartphones according to their own accounts. Among these features, of particular interest were the ways in which they react to and
Ana Maria Nicolaci-da-Costa +1 more
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Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks. [PDF]
Sensors (Basel), 2021 Ali K, Li CT, Chen YS.
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Relational POI recommendation model combined with geographic information. [PDF]
PLoS One, 2022 Li K, Wei H, He X, Tian Z.
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International Journal of Distributed Sensor Networks, 2014 In this paper, we investigate how user's online behavior (e.g., making friendships) and their offline activity (e.g., check-ins) affected each other by leveraging the data collected from LBSN.
Zhiwen Yu +4 more
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Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach. [PDF]
Sci Rep, 2023 Betancourt F, Riascos AP, Mateos JL.
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