Social Network Based Recommender Systems
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Social Network Based Recommender Systems
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Author : Daniel Schall
language : en
Publisher: Springer
Release Date : 2015-09-23
Social Network Based Recommender Systems written by Daniel Schall and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-23 with Computers categories.
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.
Social Network Based Recommender Systems
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Author : Hui Li
language : en
Publisher:
Release Date : 2017-01-26
Social Network Based Recommender Systems written by Hui Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.
This dissertation, "Social Network Based Recommender Systems" by Hui, Li, 李輝, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Recommender systems have become de facto tools for suggesting items that are of potential interest to users and achieving great success in e-commerce. Many famous online vendors such as Amazon and Netix leverage recommender systems to advertise products to customers. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recently, approaches that use data from social networks to improve the accuracy of rating prediction are emerging. However, most of the social network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this thesis, we review important rating prediction approaches in traditional and social based recommender systems. We extend SNRS, a state-of-the-art social recommender system by considering classifying the correlations between pairs of users ratings to enhance accuracy and including more users in the temporal influence links of the target user to improve the coverage. In addition, we boosted the effectiveness of social recommender systems based on matrix factorization, by proposing two models that incorporate the overlapping community regularization into the matrix factorization framework differently. Our empirical studies on real data show that our approaches outperform baselines in both traditional and social network based recommender systems. Subjects: Recommender systems (Information filtering) Online social networks
Recommender Systems For Location Based Social Networks
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Author : Panagiotis Symeonidis
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-02-08
Recommender Systems For Location Based Social Networks written by Panagiotis Symeonidis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-08 with Computers categories.
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.
Social Network Based Recommender Systems
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Author : 李輝
language : en
Publisher:
Release Date : 2015
Social Network Based Recommender Systems written by 李輝 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Online social networks categories.
A Social Network Based Recommender System
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Author : Jianming He
language : en
Publisher:
Release Date : 2010
A Social Network Based Recommender System written by Jianming He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Data Mining For Social Network Data
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Author : Nasrullah Memon
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-10
Data Mining For Social Network Data written by Nasrullah Memon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-10 with Business & Economics categories.
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.
Advances In Intelligent Web Mastering
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Author : Katarzyna M. Wegrzyn-Wolska
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-15
Advances In Intelligent Web Mastering written by Katarzyna M. Wegrzyn-Wolska and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-15 with Computers categories.
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.
Recommender System With Machine Learning And Artificial Intelligence
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Author : Sachi Nandan Mohanty
language : en
Publisher: John Wiley & Sons
Release Date : 2020-07-08
Recommender System With Machine Learning And Artificial Intelligence written by Sachi Nandan Mohanty and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Computers categories.
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.
Point Of Interest Recommendation In Location Based Social Networks
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Author : Shenglin Zhao
language : en
Publisher: Springer
Release Date : 2018-07-13
Point Of Interest Recommendation In Location Based Social Networks written by Shenglin Zhao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-13 with Computers categories.
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.
Probabilistic Models For Recommendation In Social Networks
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Author : SeyedMohsen Jamali
language : en
Publisher:
Release Date : 2013
Probabilistic Models For Recommendation In Social Networks written by SeyedMohsen Jamali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Online social networks categories.
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.
