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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

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.

Encyclopedia of GIS

Author: Shashi Shekhar

Publisher: Springer Science & Business Media

Published: 2007-12-12

Total Pages: 1392

ISBN-13: 038730858X

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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.

Social Network-Based Recommender Systems

Author: Daniel Schall

Publisher: Springer

Published: 2015-09-23

Total Pages: 139

ISBN-13: 3319227351

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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.

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

Author: Shenglin Zhao

Publisher: Springer

Published: 2018-07-13

Total Pages: 101

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.

Recommendation in Location-based Social Networks

Author: Bo Hu

Publisher:

Published: 2014

Total Pages: 110

ISBN-13:

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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.

Recommender System with Machine Learning and Artificial Intelligence

Author: Sachi Nandan Mohanty

Publisher: John Wiley & Sons

Published: 2020-07-08

Total Pages: 448

ISBN-13: 1119711576

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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.

Advances in Intelligent Web Mastering

Author: Katarzyna M. Wegrzyn-Wolska

Publisher: Springer Science & Business Media

Published: 2007-06-15

Total Pages: 413

ISBN-13: 3540725741

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This book contains papers presented at the 5th Atlantic Web Intelligence Conference, AWIC’2007, held in Fontainbleau, France, in June 2007, and organized by Esigetel, Technical University of Lodz, and Polish Academy of Sciences. It includes reports from the front of diverse fields of the Web, including application of artificial intelligence, design, information retrieval and interpretation, user profiling, security, and engineering.

Data Mining for Social Network Data

Author: Nasrullah Memon

Publisher: Springer Science & Business Media

Published: 2010-06-10

Total Pages: 217

ISBN-13: 1441962875

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Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.

A Community-based Location Recommendation System for Location-based Social Networks

Author: Rifeng Ding

Publisher:

Published: 2015

Total Pages:

ISBN-13:

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"In recent years, location-based social networks (LBSNs) has become more and more popular. As one of the key service in LBSNs, the location recommendation system has drawn much of attention from both industry and academia. According to existing work, link analysis-based methods have been proved to be effective inlocation recommendations for LBSNs. However, most of link analysis-based methods either overlook or overemphasize users' preferences. Recommendation systems that overlook users' preferences can only provide generic recommendation, while systems that overemphasize users' preference cannot recommend local popular locations that do not fit users' historical preferences. To address these issues, in this thesis, I propose a community-based location recommendation system, which takes both users' preferences and locations' popularity into account. Our system groups locations within the user-specified region into communities. Each community represents one location category and will generate a certain number of recommendations. More specifically, communities that represent user-favored categories and communities that contain large number of popular locations have higher priorities to recommend more locations. Besides, the number of recommendations of each community is dynamically calculated for different users at different regions. Thus, our system can cover both user-favored and local popular locations in its recommendations. In the evaluation, we acquire data from Foursquare, which contains 398,819 tips generated by 49,027 users who has visited the New York City. Our recommendation system outperforms the baseline approach with the precision and recall of 52.13%. and 80.01% respectively. The experimental result demonstrates that our system can provide more accurate recommendations with acceptable computation time for various types of users and solve the new-user problem as well." --

Recommender Systems

Author: Charu C. Aggarwal

Publisher: Springer

Published: 2016-03-28

Total Pages: 518

ISBN-13: 3319296590

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This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.