Recommender Systems For The Social Web

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Recommender Systems and the Social Web

Author : Fatih Gedikli
Publisher : Springer Vieweg
ISBN 13 : 9783658019471
Total Pages : 0 pages
Book Rating : 4.0/5 (194 download)

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Book Synopsis Recommender Systems and the Social Web by : Fatih Gedikli

Download or read book Recommender Systems and the Social Web written by Fatih Gedikli and published by Springer Vieweg. This book was released on 2013-04-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere.

Recommender Systems for Location-based Social Networks

Author : Panagiotis Symeonidis
Publisher : Springer Science & Business Media
ISBN 13 : 1493902865
Total Pages : 108 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Recommender Systems for Location-based Social Networks by : Panagiotis Symeonidis

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 108 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.

Recommender Systems for the Social Web

Author : José J. Pazos Arias
Publisher : Springer Science & Business Media
ISBN 13 : 3642256945
Total Pages : 224 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Recommender Systems for the Social Web by : José J. Pazos Arias

Download or read book Recommender Systems for the Social Web written by José J. Pazos Arias and published by Springer Science & Business Media. This book was released on 2012-01-24 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recommendation of products, content and services cannot be considered newly born, although its widespread application is still in full swing. While its growing success in numerous sectors, the progress of the Social Web has revolutionized the architecture of participation and relationship in the Web, making it necessary to restate recommendation and reconciling it with Collaborative Tagging, as the popularization of authoring in the Web, and Social Networking, as the translation of personal relationships to the Web. Precisely, the convergence of recommendation with the above Social Web pillars is what motivates this book, which has collected contributions from well-known experts in the academy and the industry to provide a broader view of the problems that Social Recommenders might face with. If recommender systems have proven their key role in facilitating the user access to resources on the Web, when sharing resources has become social, it is natural for recommendation strategies in the Social Web era take into account the users’ point of view and the relationships among users to calculate their predictions. This book aims to help readers to discover and understand the interplay among legal issues such as privacy; technical aspects such as interoperability and scalability; and social aspects such as the influence of affinity, trust, reputation and likeness, when the goal is to offer recommendations that are truly useful to both the user and the provider.

Social Network-Based Recommender Systems

Author : Daniel Schall
Publisher : Springer
ISBN 13 : 3319227351
Total Pages : 126 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Social Network-Based Recommender Systems by : Daniel Schall

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 126 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.

Recommender Systems

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319296590
Total Pages : 498 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Recommender Systems by : Charu C. Aggarwal

Download or read book Recommender Systems written by Charu C. Aggarwal and published by Springer. This book was released on 2016-03-28 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Recommender Systems for Social Tagging Systems

Author : Leandro Balby Marinho
Publisher : Springer Science & Business Media
ISBN 13 : 1461418941
Total Pages : 111 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Recommender Systems for Social Tagging Systems by : Leandro Balby Marinho

Download or read book Recommender Systems for Social Tagging Systems written by Leandro Balby Marinho and published by Springer Science & Business Media. This book was released on 2012-02-10 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

Recommender Systems and the Social Web

Author : Fatih Gedikli
Publisher : Springer Science & Business Media
ISBN 13 : 3658019484
Total Pages : 112 pages
Book Rating : 4.6/5 (58 download)

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Book Synopsis Recommender Systems and the Social Web by : Fatih Gedikli

Download or read book Recommender Systems and the Social Web written by Fatih Gedikli and published by Springer Science & Business Media. This book was released on 2013-03-29 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere.

Recommender Systems

Author : P. Pavan Kumar
Publisher : CRC Press
ISBN 13 : 1000387372
Total Pages : 182 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Recommender Systems by : P. Pavan Kumar

Download or read book Recommender Systems written by P. Pavan Kumar and published by CRC Press. This book was released on 2021-06-01 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.

Recommender Systems for Technology Enhanced Learning

Author : Nikos Manouselis
Publisher : Springer Science & Business Media
ISBN 13 : 1493905309
Total Pages : 306 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Recommender Systems for Technology Enhanced Learning by : Nikos Manouselis

Download or read book Recommender Systems for Technology Enhanced Learning written by Nikos Manouselis and published by Springer Science & Business Media. This book was released on 2014-04-12 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.

Data Mining for Social Network Data

Author : Nasrullah Memon
Publisher : Springer
ISBN 13 : 9781441962881
Total Pages : 216 pages
Book Rating : 4.9/5 (628 download)

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Book Synopsis Data Mining for Social Network Data by : Nasrullah Memon

Download or read book Data Mining for Social Network Data written by Nasrullah Memon and published by Springer. This book was released on 2010-07-14 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Recommender Systems

Author : Dietmar Jannach
Publisher : Cambridge University Press
ISBN 13 : 9780521493369
Total Pages : 352 pages
Book Rating : 4.4/5 (933 download)

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Book Synopsis Recommender Systems by : Dietmar Jannach

Download or read book Recommender Systems written by Dietmar Jannach and published by Cambridge University Press. This book was released on 2010-09-30 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

Cooperative Information Agents VIII

Author : Matthias Klusch
Publisher : Springer Science & Business Media
ISBN 13 : 3540231706
Total Pages : 314 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Cooperative Information Agents VIII by : Matthias Klusch

Download or read book Cooperative Information Agents VIII written by Matthias Klusch and published by Springer Science & Business Media. This book was released on 2004-09-23 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: These are the proceedings of the 8th International Workshop on Cooperative Information Agents (CIA 2004), held at the Fair and Congress Center in - furt, Germany, September 27–29, 2004. It was part of the multi-conference Net. ObjectDays 2004, and, in particular, was co-located with the 2nd German Conference on Multiagent Systems Technologies (MATES 2004). In today’s networked world of linked heterogeneous, pervasive computer systems, devices, and information landscapes, the intelligent coordination and provision of relevant added-value information at any time, anywhere, by means of cooperative information agents becomes increasingly important for a variety of applications. An information agent is a computational software entity that has access to one or multiple, heterogeneous, and geographically dispersed data and information sources. It proactively searches for and maintains information on behalf of its human users, or other agents, preferably just in time. In other words,itismanagingandovercomingthedi?cultiesassociatedwithinformation overload in open, pervasive information and service landscapes. Cooperative - formation agents may collaborate with each other to accomplish both individual and shared joint goals depending on the actual preferences of their users, b- getary constraints, and resources available. One major challenge of developing agent-based intelligent information systems in open environments is to balance the autonomy of networked data, information, and knowledge sources with the potential payo? of leveraging them using information agents. Interdisciplinaryresearchanddevelopmentofinformationagentsrequires- pertise in relevant domains of information retrieval, arti?cial intelligence, database systems, human-computer interaction, and Internet and Web techn- ogy.

Recommender System with Machine Learning and Artificial Intelligence

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119711592
Total Pages : 448 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Recommender System with Machine Learning and Artificial Intelligence by : Sachi Nandan Mohanty

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 2020-06-09 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.

Recommender Systems and the Social Web

Author : Fatih Gedikli
Publisher :
ISBN 13 : 9783658019495
Total Pages : 126 pages
Book Rating : 4.0/5 (194 download)

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Book Synopsis Recommender Systems and the Social Web by : Fatih Gedikli

Download or read book Recommender Systems and the Social Web written by Fatih Gedikli and published by . This book was released on 2013-04-30 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Recommender Systems Handbook

Author : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1003 pages
Book Rating : 4.4/5 (899 download)

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Book Synopsis Recommender Systems Handbook by : Francesco Ricci

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1003 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Recommendation and Search in Social Networks

Author : Özgür Ulusoy
Publisher : Springer
ISBN 13 : 3319143794
Total Pages : 289 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Recommendation and Search in Social Networks by : Özgür Ulusoy

Download or read book Recommendation and Search in Social Networks written by Özgür Ulusoy and published by Springer. This book was released on 2015-02-12 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume offers a clear in-depth overview of research covering a variety of issues in social search and recommendation systems. Within the broader context of social network analysis it focuses on important and up-coming topics such as real-time event data collection, frequent-sharing pattern mining, improvement of computer-mediated communication, social tagging information, search system personalization, new detection mechanisms for the identification of online user groups, and many more. The twelve contributed chapters are extended versions of conference papers as well as completely new invited chapters in the field of social search and recommendation systems. This first-of-its kind survey of current methods will be of interest to researchers from both academia and industry working in the field of social networks.

Web Recommendations Systems

Author : K. R. Venugopal
Publisher : Springer Nature
ISBN 13 : 9811525137
Total Pages : 178 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Web Recommendations Systems by : K. R. Venugopal

Download or read book Web Recommendations Systems written by K. R. Venugopal and published by Springer Nature. This book was released on 2020-03-02 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse applications, including query log mining, social networking, news recommendations and computational advertising, and with the explosive growth of Web content, Web recommendations have become a critical aspect of all search engines. The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with valuable insights into Web recommender systems.