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Author: Juan Trujillo
Publisher: Springer
ISBN: 9783642163845
Category : Computers
Languages : en
Pages : 0
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Book Description
This book constitutes the refereed proceedings of workshops, held at the 29th International Conference on Conceptual Modeling, ER 2010, in Vancouver, Canada, in November 2010. The 31 revised full papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in sections on the workshops Semantic and Conceptual Issues in GIS (SeCoGIS); Conceptual Modeling of Life Sciences Applications (CMLSA); Conceptual Modelling of Services (CMS); Active Conceptual Modeling of Learning (ACM-L); Web Information Systems Modeling (WISM); Domain Engineering (DE@ER); and Foundations and Practices of UML (FP-UML).
Author: Juan Trujillo
Publisher: Springer
ISBN: 9783642163845
Category : Computers
Languages : en
Pages : 0
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Book Description
This book constitutes the refereed proceedings of workshops, held at the 29th International Conference on Conceptual Modeling, ER 2010, in Vancouver, Canada, in November 2010. The 31 revised full papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in sections on the workshops Semantic and Conceptual Issues in GIS (SeCoGIS); Conceptual Modeling of Life Sciences Applications (CMLSA); Conceptual Modelling of Services (CMS); Active Conceptual Modeling of Learning (ACM-L); Web Information Systems Modeling (WISM); Domain Engineering (DE@ER); and Foundations and Practices of UML (FP-UML).
Author: Bin Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 98
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Book Description
Author: Panagiotis Symeonidis
Publisher: Springer Science & Business Media
ISBN: 1493902865
Category : Computers
Languages : en
Pages : 108
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Book Description
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: Athina A. Lazakidou
Publisher: Springer Science & Business Media
ISBN: 1461436346
Category : Computers
Languages : en
Pages : 249
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Book Description
Online communities are among the most obvious manifestations of social networks based on new media technology. Facilitating ad-hoc communication and leveraging collective intelligence by matching similar or related users have become important success factors in almost every successful business plan. Researchers are just beginning to understand virtual communities and collaborations among participants currently proliferating across the world. Virtual Communities, Social Networks and Collaboration covers cutting edge research topics of utmost real-world importance in the specific domain of social networks. This volume focuses on exploring issues relating to the design, development, and outcomes from electronic groups and online communities, including: - The implications of social networking, - Understanding of how and why knowledge is shared among participants, - What leads to participation, effective collaboration, co-creation and innovation, - How organizations can better utilize the potential benefits of communities in both internal operations, marketing, and new product development.
Author: Brüggemann, Stefan
Publisher: IGI Global
ISBN: 1466608951
Category : Computers
Languages : en
Pages : 387
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Book Description
Collaborative working has been increasingly viewed as a good practice for organizations to achieve efficiency. Organizations that work well in collaboration may have access to new sources of funding, deliver new, improved, and more integrated services, make savings on shared costs, and exchange knowledge, information and expertise. Collaboration and the Semantic Web: Social Networks, Knowledge Networks and Knowledge Resources showcases cutting-edge research on the intersections of Semantic Web, collaborative work, and social media research, exploring how the resources of so-called social networking applications, which bring people together to interact and encourage sharing of personal information and ideas, can be tapped by Semantic Web techniques, making shared Web contents readable and processable for machine and intelligent applications, as well as humans. Semantic technologies have shown their potential for integrating valuable knowledge, and they are being applied to the composition of digital learning and working platforms. Integrated semantic applications, linked data, social networks, and networked digital solutions can now be used in collaborative environments and present participants with the context-aware information that they need.
Author: Özgür Ulusoy
Publisher: Springer
ISBN: 3319143794
Category : Computers
Languages : en
Pages : 289
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Book Description
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: SeyedMohsen Jamali
Publisher:
ISBN:
Category : Online social networks
Languages : en
Pages : 0
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Book Description
Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. However, collaborative filtering based approaches perform poorly for so-called cold start users. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this research we propose novel methods to address the recommendation problem in online social networks. To better understand the underlying mechanisms of user behavior in a social network, we first propose a model to capture the temporal dynamics of user behavior based on different effects influencing the behavior of users in rating items and creating social relations (e.g. social influence, social selection and transitivity of relations). Then we propose a memory based approach based on random walk models to perform recommendation in social networks. Matrix factorization is the most prominent model based approach for collaborative recommendation. We extend matrix factorization and propose a model that takes into account the social network as well as the rating matrix. Finally, we present a mixed membership community based model for recommendation in social networks based on stochastic block models. This model is capable of performing both rating and link prediction. All methods have been experimentally evaluated and compared against state-of-the-art methods on real life data sets from Epinions.com, Flixster.com and Flickr.com. The Flixster data set has been crawled and published as part of the research during this thesis. Experimental results show that our proposed models achieve substantial quality gains compared to the existing methods.
Author: Shenglin Zhao
Publisher: Springer
ISBN: 9811313490
Category : Computers
Languages : en
Pages : 101
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Book Description
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.
Author: Lei Tang
Publisher: Springer Nature
ISBN: 3031019008
Category : Computers
Languages : en
Pages : 126
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Book Description
The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining
Author: M. Narasimha Murty
Publisher: Springer
ISBN: 9783319133645
Category : Computers
Languages : en
Pages : 0
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Book Description
This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014. The 22 revised full papers were carefully reviewed and selected from 44 submissions. The papers feature a wide range of topics covering both theory, methods and tools as well as their diverse applications in numerous domains.
