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Recommendation And Search In Social Networks
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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 294 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.
Author :Panagiotis Symeonidis Publisher :Springer Science & Business Media ISBN 13 :1493902865 Total Pages :109 pages Book Rating :4.4/5 (939 download)
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 109 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.
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 139 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.
Author :Katarzyna M. Wegrzyn-Wolska Publisher :Springer Science & Business Media ISBN 13 :3540725741 Total Pages :413 pages Book Rating :4.5/5 (47 download)
Book Synopsis Advances in Intelligent Web Mastering by : Katarzyna M. Wegrzyn-Wolska
Download or read book Advances in Intelligent Web Mastering written by Katarzyna M. Wegrzyn-Wolska and published by Springer Science & Business Media. This book was released on 2007-06-15 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Web Mining and Social Networking by : Guandong Xu
Download or read book Web Mining and Social Networking written by Guandong Xu and published by Springer Science & Business Media. This book was released on 2010-10-20 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the techniques and applications involved in the Web Mining, Web Personalization and Recommendation and Web Community Analysis domains, including a detailed presentation of the principles, developed algorithms, and systems of the research in these areas. The applications of web mining, and the issue of how to incorporate web mining into web personalization and recommendation systems are also reviewed. Additionally, the volume explores web community mining and analysis to find the structural, organizational and temporal developments of web communities and reveal the societal sense of individuals or communities. The volume will benefit both academic and industry communities interested in the techniques and applications of web search, web data management, web mining and web knowledge discovery, as well as web community and social network analysis.
Book Synopsis Advances in Data Science by : Edwin Diday
Download or read book Advances in Data Science written by Edwin Diday and published by John Wiley & Sons. This book was released on 2020-01-09 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 2020-07-08 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.
Book Synopsis Point-of-Interest Recommendation in Location-Based Social Networks by : Shenglin Zhao
Download or read book Point-of-Interest Recommendation in Location-Based Social Networks written by Shenglin Zhao and published by Springer. This book was released on 2018-07-13 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Collaborative Recommendations: Algorithms, Practical Challenges And Applications by : Shlomo Berkovsky
Download or read book Collaborative Recommendations: Algorithms, Practical Challenges And Applications written by Shlomo Berkovsky and published by World Scientific. This book was released on 2018-11-30 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 . This book was released on 2015 with total page 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.
Book Synopsis Toward a Real-Time Recommendation for Online Social Networks by : Rania Albalawi
Download or read book Toward a Real-Time Recommendation for Online Social Networks written by Rania Albalawi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Internet increases the demand for the development of commercial applications and services that can provide better shopping experiences for customers globally. It is full of information and knowledge sources that might confuse customers. This requires customers to spend additional time and effort when they are trying to find relevant information about specific topics or objects. Recommendation systems are considered to be an important method that solves this issue. Incorporating recommendation systems in online social networks led to a specific kind of recommendation system called social recommendation systems which have become popular with the global explosion in social media and online networks and they apply many prediction algorithms such as data mining techniques to address the problem of information overload and to analyze a vast amount of data. We believe that offering a real-time social recommendation system that can understand the real context of a user's conversation dynamically is essential to defining and recommending interesting objects at the ideal time. In this thesis, we propose an architecture for a real-time social recommendation system that aims to improve word usage and understanding in social media platforms, advance the performance and accuracy of recommendations, and propose a possible solution to the user cold-start problem. Moreover, we aim to find out if the user's social context can be used as an input source to offer personalized and improved recommendations that will help users to find valuable items immediately, without interrupting their conversation flow. The suggested architecture works as a third-party social recommendation system that could be incorporated with other existing social networking sites (e.g. Facebook and Twitter). The novelty of our approach is the dynamic understanding of the user-generated content, achieved by detecting topics from the user's extracted dialogue and then matching them with an appropriate task as a recommendation. Topic extraction is done through a modified Latent Dirichlet Allocation topic modeling method. We also develop a social chat app as a proof of concept to validate our proposed architecture. The results of our proposed architecture offer promising gains in enhancing the real-time social recommendations.
Book Synopsis Job Searching with Social Media For Dummies by : Joshua Waldman
Download or read book Job Searching with Social Media For Dummies written by Joshua Waldman and published by John Wiley & Sons. This book was released on 2013-09-12 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness social media to land your dream job For anyone looking for a first job, exploring a career change, or just setting up for future success, social media sites are proven platforms for facilitating connections, demonstrating passions and interests, and ultimately landing the job. Job Searching with Social Media For Dummies enables you to harness the power of the Internet to research and identify job opportunities, and then create a strategy for securing a position. Job Searching with Social Media For Dummies features in-depth coverage of topics such as: creating effective online profiles and resumes to sell your strengths; maintaining your online reputation and understanding electronic etiquette; using the power of personal branding and building your brand online; avoiding common pitfalls, such as jumping into filling out a social media profile without a strategy; getting to know Twitter, the only real-time job board with literally thousands of jobs posted daily; using social media sites to uncover opportunities in the "hidden job market" ahead of the competition; and much more. Takes the mystery out of Facebook, Twitter, and LinkedIn Offers advice on how to brand yourself online Includes coverage of the latest changes to social platforms and websites If you're a recent graduate, changing careers, or have been away from the job-search scene for a while, turn to the trusted guidance and expert insight of Job Searching with Social Media For Dummies.
Book Synopsis Machine Learning Techniques for Online Social Networks by : Tansel Özyer
Download or read book Machine Learning Techniques for Online Social Networks written by Tansel Özyer and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.
Book Synopsis Recommendation in Location-based Social Networks by : Bo Hu
Download or read book Recommendation in Location-based Social Networks written by Bo Hu and published by . This book was released on 2014 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems have become popular tools to select relevant personalized information for users. With the rapid growth of mobile network users, the way users consume Web 2.0 is changing substantially. Mobile networks enable users to post personal status on online social media services from anywhere and at anytime. However, as the volume of user activities is growing rapidly, it is getting impossible that for users to read all posts or blogs to catch up with the trends. Similarly, it is hard for producers and manufactures to monitor consumers and figure out their tastes. These needs inspired the emergence of a new line of research, recommendation in location-based social networks, i.e., building recommender systems to discover and predict the behavior of users and their engagement with location-based social networks. Extracted users' interests and their spatio-temporal patterns clearly provide more detailed information for producers to make decisions to supply their consumers. In this thesis, we address the problem of recommendation in location-based social networks and seek novel methods to improve limitations of existing techniques. We first propose a spatial topic model for top-k POI recommendation problem, and the proposed model discovers users' topic and geographical distributions from user check-ins with posts and location coordinates. Then we focus on mining spatio-temporal patterns of user check-ins and propose a spatio-temporal topic model to identify temporal activity patterns of different topics and POIs. In our next work, we argue that all existing social network-based POI recommendation models cannot capture the nature of location-based social network. Hence, we propose a social topic model to effectively exploit a location-based social network. Finally, we address the problem of determining the optimal location for a new store by considering it as a recommendation problem, i.e., recommending locations to a new store. Latent factor models are proposed and proved to perform better than existing state-of-the-art methods.
Book Synopsis Producing Timely Recommendations from Social Networks Through Targeted Search by : Anil Gürsel
Download or read book Producing Timely Recommendations from Social Networks Through Targeted Search written by Anil Gürsel and published by . This book was released on 2008 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Probabilistic Models for Recommendation in Social Networks by : SeyedMohsen Jamali
Download or read book Probabilistic Models for Recommendation in Social Networks written by SeyedMohsen Jamali and published by . This book was released on 2013 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Recommender Systems by : Gérald Kembellec
Download or read book Recommender Systems written by Gérald Kembellec and published by John Wiley & Sons. This book was released on 2014-12-15 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.
