Recommendation And Search In Social Networks
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| Author | : Panagiotis Symeonidis |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 109 |
| Release | : 2014-02-08 |
| Genre | : Computers |
| ISBN | : 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.
| Author | : Özgür Ulusoy |
| Publisher | : Springer |
| Total Pages | : 294 |
| Release | : 2015-02-12 |
| Genre | : Computers |
| ISBN | : 3319143794 |
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 | : Daniel Schall |
| Publisher | : Springer |
| Total Pages | : 139 |
| Release | : 2015-09-23 |
| Genre | : Computers |
| ISBN | : 3319227351 |
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 | : Edwin Diday |
| Publisher | : John Wiley & Sons |
| Total Pages | : 232 |
| Release | : 2020-01-09 |
| Genre | : Business & Economics |
| ISBN | : 1119694965 |
Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.
| Author | : Sachi Nandan Mohanty |
| Publisher | : John Wiley & Sons |
| Total Pages | : 448 |
| Release | : 2020-07-08 |
| Genre | : Computers |
| ISBN | : 1119711576 |
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 | : Peter Brusilovsky |
| Publisher | : Springer |
| Total Pages | : 662 |
| Release | : 2018-05-02 |
| Genre | : Computers |
| ISBN | : 3319900927 |
Social information access is defined as a stream of research that explores methods for organizing the past interactions of users in a community in order to provide future users with better access to information. Social information access covers a wide range of different technologies and strategies that operate on a different scale, which can range from a small closed corpus site to the whole Web. The 16 chapters included in this book provide a broad overview of modern research on social information access. In order to provide a balanced coverage, these chapters are organized by the main types of information access (i.e., social search, social navigation, and recommendation) and main sources of social information.
| Author | : Nasrullah Memon |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 217 |
| Release | : 2010-06-10 |
| Genre | : Business & Economics |
| ISBN | : 1441962875 |
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 | : Shlomo Berkovsky |
| Publisher | : World Scientific |
| Total Pages | : 736 |
| Release | : 2018-11-30 |
| Genre | : Computers |
| ISBN | : 9813275367 |
Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e-commerce, multimedia and Web content. Collaborative-based methods have been the focus of recommender systems research for more than two decades.The unique feature of the compendium is the technical details of collaborative recommenders. The book chapters include algorithm implementations, elaborate on practical issues faced when deploying these algorithms in large-scale systems, describe various optimizations and decisions made, and list parameters of the algorithms.This must-have title is a useful reference materials for researchers, IT professionals and those keen to incorporate recommendation technologies into their systems and services.
| Author | : Karl Aberer |
| Publisher | : Springer |
| Total Pages | : 571 |
| Release | : 2012-11-27 |
| Genre | : Computers |
| ISBN | : 364235386X |
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 | : Shenglin Zhao |
| Publisher | : Springer |
| Total Pages | : 110 |
| Release | : 2018-07-13 |
| Genre | : Computers |
| ISBN | : 9811313490 |
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.
