Download or read book Point of Interest Recommendation in Location Based Social Networks written by Shenglin Zhao and published by Springer. This book was released on 2018-07-13 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Personalized POI Recommendation on Location based Social Networks written by Huiji Gao and published by . This book was released on 2014 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
Download or read book Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining written by Inderjit S. Dhillon and published by . This book was released on 2013 with total page 1534 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recommendation in Location based Social Networks written by Bo Hu and published by . This book was released on 2014 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems have become popular tools to select relevant personalized information for users. With the rapid growth of mobile network users, the way users consume Web 2.0 is changing substantially. Mobile networks enable users to post personal status on online social media services from anywhere and at anytime. However, as the volume of user activities is growing rapidly, it is getting impossible that for users to read all posts or blogs to catch up with the trends. Similarly, it is hard for producers and manufactures to monitor consumers and figure out their tastes. These needs inspired the emergence of a new line of research, recommendation in location-based social networks, i.e., building recommender systems to discover and predict the behavior of users and their engagement with location-based social networks. Extracted users' interests and their spatio-temporal patterns clearly provide more detailed information for producers to make decisions to supply their consumers. In this thesis, we address the problem of recommendation in location-based social networks and seek novel methods to improve limitations of existing techniques. We first propose a spatial topic model for top-k POI recommendation problem, and the proposed model discovers users' topic and geographical distributions from user check-ins with posts and location coordinates. Then we focus on mining spatio-temporal patterns of user check-ins and propose a spatio-temporal topic model to identify temporal activity patterns of different topics and POIs. In our next work, we argue that all existing social network-based POI recommendation models cannot capture the nature of location-based social network. Hence, we propose a social topic model to effectively exploit a location-based social network. Finally, we address the problem of determining the optimal location for a new store by considering it as a recommendation problem, i.e., recommending locations to a new store. Latent factor models are proposed and proved to perform better than existing state-of-the-art methods.
Download or read book Web Engineering written by Maxim Bakaev and published by Springer. This book was released on 2019-04-25 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Web Engineering, ICWE 2019, held in Daejeon, South Korea, in June 2019. The 26 full research papers and 9 short papers presented were carefully reviewed and selected from 106 submissions. Additionally, two demonstrations, four posters, and four contributions to the PhD symposium as well as five tutorials are included in this volume. The papers cover research areas such as Web mining and knowledge extraction, Web big data and Web data analytics, social Web applications and crowdsourcing, Web user interfaces, Web security and privacy, Web programming, Web services and computing, Semantic Web and linked open data applications, and Web application modeling and engineering.
Download or read book Computing with Spatial Trajectories written by Yu Zheng and published by Springer Science & Business Media. This book was released on 2011-10-02 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Recommender Systems for Location based Social Networks written by Panagiotis Symeonidis and published by Springer Science & Business Media. This book was released on 2014-02-08 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Web and Big Data written by Xin Wang and published by Springer Nature. This book was released on 2020-10-15 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 11317 and 12318, constitutes the thoroughly refereed proceedings of the 4th International Joint Conference, APWeb-WAIM 2020, held in Tianjin, China, in September 2020. Due to the COVID-19 pandemic the conference was organizedas a fully online conference. The 42 full papers presented together with 17 short papers, and 6 demonstration papers were carefully reviewed and selected from 180 submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender Systems; Information Extraction and Retrieval; Machine Learning; Blockchain; Data Mining; Text Analysis and Mining; Spatial, Temporal and Multimedia Databases; Database Systems; and Demo.
Download or read book Social Network Based Recommender Systems written by Daniel Schall and published by Springer. This book was released on 2015-09-23 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on ‘social brokers’ are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.
Download or read book Encyclopedia of GIS written by Shashi Shekhar and published by Springer Science & Business Media. This book was released on 2007-12-12 with total page 1392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of GIS provides a comprehensive and authoritative guide, contributed by experts and peer-reviewed for accuracy, and alphabetically arranged for convenient access. The entries explain key software and processes used by geographers and computational scientists. Major overviews are provided for nearly 200 topics: Geoinformatics, Spatial Cognition, and Location-Based Services and more. Shorter entries define specific terms and concepts. The reference will be published as a print volume with abundant black and white art, and simultaneously as an XML online reference with hyperlinked citations, cross-references, four-color art, links to web-based maps, and other interactive features.
Download or read book Recommender System with Machine Learning and Artificial Intelligence written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2020-07-08 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
Download or read book Big Data and Innovation in Tourism Travel and Hospitality written by Marianna Sigala and published by Springer. This book was released on 2019-02-26 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Point of interest Recommendations in Location based Social Networks written by 張家棟 and published by . This book was released on 2015 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book CIKM 13 written by CIKM 13 Conference Committee and published by . This book was released on 2013-10-27 with total page 938 pages. Available in PDF, EPUB and Kindle. Book excerpt: CIKM'13: 22nd ACM International Conference on Information and Knowledge Management Oct 27, 2013-Nov 01, 2013 San Francisco, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Download or read book 2021 International Conference on Data Analytics for Business and Industry ICDABI written by IEEE Staff and published by . This book was released on 2021-10-25 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference aims to provide an excellent international platform to researchers, data scientists, business managers, and industry practitioners to meet and exchange their ideas and novel approaches to the technical challenges of a data driven world It provides a premier interdisciplinary forum for sharing knowledge and discussing the most recent innovations, trends, and practical challenges encountered and solutions in the wide fields of big data and data analytics emerging in all business and industrial sectors Integrating sophisticated business analytics in the internal operational processes for marketing, sales, management, designing strategies, etc can play a powerful role in developing any business sector through increasing its efficiency, supporting its decision making under uncertainty, and improving its business operations, in today s competitive market The main goal is to encourage sustained economic growth through data technology
Download or read book Data Driven Mining Learning and Analytics for Secured Smart Cities written by Chinmay Chakraborty and published by Springer Nature. This book was released on 2021-04-28 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides information on data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart cities. This book aims to attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities.
Download or read book Mining Human Mobility in Location Based Social Networks written by Huiji Gao and published by Springer Nature. This book was released on 2022-06-01 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
