Recommender Systems For The Social Web

Author : José J. Pazos Arias,Ana Fernández Vilas,Rebeca P. Díaz Redondo
Publisher : Springer Science & Business Media
Page : 224 pages
File Size : 47,7 Mb
Release : 2012-01-24
Category : Technology & Engineering
ISBN : 9783642256943

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Recommender Systems for the Social Web by José J. Pazos Arias,Ana Fernández Vilas,Rebeca P. Díaz Redondo Pdf

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.

Author : Fatih Gedikli
Publisher : Springer Vieweg
Page : 0 pages
File Size : 49,7 Mb
Release : 2013-04-10
Category : Computers
ISBN : 3658019476

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Recommender Systems and the Social Web by Fatih Gedikli Pdf

​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.

Author : Panagiotis Symeonidis,Dimitrios Ntempos,Yannis Manolopoulos
Publisher : Springer Science & Business Media
Page : 108 pages
File Size : 42,9 Mb
Release : 2014-02-08
Category : Computers
ISBN : 9781493902866

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Recommender Systems for Location-based Social Networks by Panagiotis Symeonidis,Dimitrios Ntempos,Yannis Manolopoulos Pdf

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.

Author : Nasrullah Memon,Jennifer Jie Xu,David L. Hicks,Hsinchun Chen
Publisher : Springer Science & Business Media
Page : 216 pages
File Size : 43,6 Mb
Release : 2010-06-10
Category : Business & Economics
ISBN : 1441962875

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Data Mining for Social Network Data by Nasrullah Memon,Jennifer Jie Xu,David L. Hicks,Hsinchun Chen Pdf

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.

Author : Katarzyna M. Wegrzyn-Wolska,Piotr S. Szczepaniak
Publisher : Springer Science & Business Media
Page : 413 pages
File Size : 46,9 Mb
Release : 2007-06-15
Category : Computers
ISBN : 9783540725749

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Advances in Intelligent Web Mastering by Katarzyna M. Wegrzyn-Wolska,Piotr S. Szczepaniak Pdf

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.

Author : Fatih Gedikli
Publisher : Springer Science & Business Media
Page : 112 pages
File Size : 47,7 Mb
Release : 2013-03-29
Category : Computers
ISBN : 9783658019488

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Recommender Systems and the Social Web by Fatih Gedikli Pdf

​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.

Author : Leandro Balby Marinho,Andreas Hotho,Robert Jäschke,Alexandros Nanopoulos,Steffen Rendle,Lars Schmidt-Thieme,Gerd Stumme,Panagiotis Symeonidis
Publisher : Springer Science & Business Media
Page : 111 pages
File Size : 41,5 Mb
Release : 2012-02-10
Category : Computers
ISBN : 9781461418948

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Recommender Systems for Social Tagging Systems by Leandro Balby Marinho,Andreas Hotho,Robert Jäschke,Alexandros Nanopoulos,Steffen Rendle,Lars Schmidt-Thieme,Gerd Stumme,Panagiotis Symeonidis Pdf

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.

Author : Daniel Schall
Publisher : Springer
Page : 126 pages
File Size : 44,9 Mb
Release : 2015-09-23
Category : Computers
ISBN : 9783319227351

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Social Network-Based Recommender Systems by Daniel Schall Pdf

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.

Author : Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Olga C. Santos
Publisher : Springer Science & Business Media
Page : 306 pages
File Size : 53,7 Mb
Release : 2014-04-12
Category : Computers
ISBN : 9781493905300

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Recommender Systems for Technology Enhanced Learning by Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Olga C. Santos Pdf

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.

Author : Matthias Klusch,Sascha Ossowski,Vipul Kashyap,Rainer Unland
Publisher : Springer Science & Business Media
Page : 314 pages
File Size : 51,5 Mb
Release : 2004-09-23
Category : Computers
ISBN : 9783540231707

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Cooperative Information Agents VIII by Matthias Klusch,Sascha Ossowski,Vipul Kashyap,Rainer Unland Pdf

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.

Author : Charu C. Aggarwal
Publisher : Springer
Page : 498 pages
File Size : 50,8 Mb
Release : 2016-03-28
Category : Computers
ISBN : 9783319296593

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Recommender Systems by Charu C. Aggarwal Pdf

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.

Author : Karl Aberer,Andreas Flache,Wander Jager,Ling Liu,Jie Tang,Christophe Gueret
Publisher : Springer
Page : 571 pages
File Size : 51,8 Mb
Release : 2012-11-27
Category : Computers
ISBN : 9783642353864

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Social Informatics by Karl Aberer,Andreas Flache,Wander Jager,Ling Liu,Jie Tang,Christophe Gueret Pdf

This book constitutes the proceedings of the 4th International Conference on Social Informatics, SocInfo 2012, held in Lausanne, Switzerland, in December 2012. The 21 full papers, 18 short papers included in this volume were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections named: social choice mechanisms in the e-society,computational models of social phenomena, social simulation, web mining and its social interpretations, algorithms and protocols inspired by human societies, socio-economic systems and applications, trust, privacy, risk and security in social contexts.

Author : Özgür Ulusoy,Abdullah Uz Tansel,Erol Arkun
Publisher : Springer
Page : 289 pages
File Size : 53,6 Mb
Release : 2015-02-12
Category : Computers
ISBN : 9783319143798

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Recommendation and Search in Social Networks by Özgür Ulusoy,Abdullah Uz Tansel,Erol Arkun Pdf

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.

Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 45,5 Mb
Release : 2020-06-09
Category : Computers
ISBN : 9781119711599

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Recommender System with Machine Learning and Artificial Intelligence by Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta Pdf

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.

Author : Fatih Gedikli
Publisher : Unknown
Page : 126 pages
File Size : 40,5 Mb
Release : 2013-04-30
Category : Electronic
ISBN : 3658019492

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Recommender Systems and the Social Web by Fatih Gedikli Pdf