Location-Based Social Media
Location-Based Social Media

Author: Leighton Evans

Publisher: Springer

Published: 2017-01-23

Total Pages: 119

ISBN-13: 3319494724

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This book extends current understandings of the effects of using locative social media on spatiality, the experience of time and identity. This is a pertinent and timely topic given the increase in opportunities people now have to explicitly and implicitly share their location through digital and mobile technologies. There is a growing body of research on locative media, much of this literature has concentrated on spatial issues. Research here has explored how locative media and location-based social media (LBSN) are used to communicate and coordinate social interactions in public space, affecting how people approach their surroundings, turning ordinary life “into a game”, and altering how mobile media is involved in understanding the world. This book offers a critical analysis of the effect of usage of locative social media on identity through an engagement with the current literature on spatiality, a novel critical investigation of the temporal effects of LBSN use and a view of identity as influenced by the spatio-temporal effects of interacting with place through LBSN. Drawing on phenomenology, post-phenomenology and critical theory on social and locative media, alongside established sociological frameworks for approaching spatiality and the city, it presents a comprehensive account of the effects of LBSN and locative media use.

Computing with Spatial Trajectories
Computing with Spatial Trajectories

Author: Yu Zheng

Publisher: Springer Science & Business Media

Published: 2011-10-02

Total Pages: 328

ISBN-13: 1461416299

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Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals. Computing with Spatial Trajectories introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work. Computing with Spatial Trajectories is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.

Recommender Systems for Location-based Social Networks
Recommender Systems for Location-based Social Networks

Author: Panagiotis Symeonidis

Publisher: Springer Science & Business Media

Published: 2014-02-08

Total Pages: 109

ISBN-13: 1493902865

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Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of these recommender systems. Part 3 provides a step-by-step case study on the technical aspects of deploying and evaluating a real-world LBSN, which provides location, activity and friend recommendations. The material covered in the book is intended for graduate students, teachers, researchers, and practitioners in the areas of web data mining, information retrieval, and machine learning.

Big Data and Innovation in Tourism, Travel, and Hospitality
Big Data and Innovation in Tourism, Travel, and Hospitality

Author: Marianna Sigala

Publisher: Springer

Published: 2019-02-26

Total Pages: 227

ISBN-13: 9811363390

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This book brings together multi-disciplinary research and practical evidence about the role and exploitation of big data in driving and supporting innovation in tourism. It also provides a consolidated framework and roadmap summarising the major issues that both researchers and practitioners have to address for effective big data innovation. The book proposes a process-based model to identify and implement big data innovation strategies in tourism. This process framework consists of four major parts: 1) inputs required for big data innovation; 2) processes required to implement big data innovation; 3) outcomes of big data innovation; and 4) contextual factors influencing big data exploitation and advances in big data exploitation for business innovation.

Mining Human Mobility in Location-Based Social Networks
Mining Human Mobility in Location-Based Social Networks

Author: Huiji Gao

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 99

ISBN-13: 3031019083

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In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to "check in" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., "when and where a user (who) has been to for what," corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.

Point-of-Interest Recommendation in Location-Based Social Networks
Point-of-Interest Recommendation in Location-Based Social Networks

Author: Shenglin Zhao

Publisher: Springer

Published: 2018-07-13

Total Pages: 110

ISBN-13: 9811313490

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This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further, it demonstrates how to build a state-of-the-art POI recommendation system by incorporating the user behavior analysis. Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services. It is assumed that readers have a basic knowledge of mathematics, as well as some background in recommendation systems.

Locative Social Media
Locative Social Media

Author: L. Evans

Publisher: Springer

Published: 2015-05-19

Total Pages: 155

ISBN-13: 1137456116

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This book offers a critical analysis of the effect of usage of locative social media on the perceptions and phenomenal experience of lived in spaces and places. Drawing on users accounts of location-based social networking, a digital post-phenomenology of place is developed to explain how place is mediated in the digital age.

Spatio-Temporal Recommendation in Social Media
Spatio-Temporal Recommendation in Social Media

Author: Hongzhi Yin

Publisher: Springer

Published: 2016-05-19

Total Pages: 122

ISBN-13: 9811007489

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This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users’ behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users’ behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.

Social Computing and Social Media
Social Computing and Social Media

Author: Gabriele Meiselwitz

Publisher: Springer

Published: 2016-07-04

Total Pages: 481

ISBN-13: 3319399101

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This book constitutes the refereed proceedings of the 8th International Conference on Social Computing and Social Media, SCSM 2016, held as part of the 18th International Conference on Human-Computer Interaction, HCII 2016, held in Toronto, ON, Canada, in July 2016. The total of 1287 papers and 186 posters presented at the HCII 2016 conferences were carefully reviewed and selected from 4354 submissions. The papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The 43 contributions included in the SCSM 2016 proceedings were organized in the following topical sections: designing and developing social media; users behaviour in social media; social media, policy, politics and engagement; social network analysis; social media in learning and collaboration; and enterprise social media.