Search Results for "statistical-methods-for-recommender-systems"

Statistical Methods for Recommender Systems

Statistical Methods for Recommender Systems

  • Author: Deepak K. Agarwal,Bee-Chung Chen
  • Publisher: Cambridge University Press
  • ISBN: 1316565130
  • Category: Computers
  • Page: N.A
  • View: 5266
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Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Modern Statistical Methods for HCI

Modern Statistical Methods for HCI

  • Author: Judy Robertson,Maurits Kaptein
  • Publisher: Springer
  • ISBN: 3319266330
  • Category: Computers
  • Page: 348
  • View: 1992
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This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Covering many techniques and approaches for exploratory data analysis including effect and power calculations, experimental design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate statistical results fairly, as well as presenting a general set of recommendations for authors and reviewers to improve the quality of statistical analysis in HCI. Each chapter presents [R] code for running analyses on HCI examples and explains how the results can be interpreted. Modern Statistical Methods for HCI is aimed at researchers and graduate students who have some knowledge of “traditional” null hypothesis significance testing, but who wish to improve their practice by using techniques which have recently emerged from statistics and related fields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.

Recommender Systems for Information Providers

Recommender Systems for Information Providers

Designing Customer Centric Paths to Information

  • Author: Andreas W. Neumann
  • Publisher: Springer Science & Business Media
  • ISBN: 3790821349
  • Category: Business & Economics
  • Page: 158
  • View: 3114
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Information providers are a very promising application area of recommender systems due to the general problem of assessing the quality of information products prior to the purchase. Recommender systems automatically generate product recommendations: customers profit from a faster finding of relevant products, stores profit from rising sales. All aspects of recommender systems are covered: the economic background, mechanism design, a survey of systems in the Internet, statistical methods and algorithms, service oriented architectures, user interfaces, as well as experiences and data from real-world applications. Specific solutions for areas with strong privacy concerns, scalability issues for large collections of products, as well as algorithms to lessen the cold-start problem for a faster return on investment of recommender projects are addressed. This book describes all steps it takes to design, implement, and successfully operate a recommender system for a specific information platform.

Recommender Systems

Recommender Systems

  • Author: Sophie Ahrens
  • Publisher: epubli
  • ISBN: 3844208232
  • Category:
  • Page: 364
  • View: 9898
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Recommender Systems for Technology Enhanced Learning

Recommender Systems for Technology Enhanced Learning

Research Trends and Applications

  • Author: Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Olga C. Santos
  • Publisher: Springer Science & Business Media
  • ISBN: 1493905309
  • Category: Computers
  • Page: 306
  • View: 2574
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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.

Recommender Systems Handbook

Recommender Systems Handbook

  • Author: Francesco Ricci,Lior Rokach,Bracha Shapira,Paul B. Kantor
  • Publisher: Springer Science & Business Media
  • ISBN: 9780387858203
  • Category: Computers
  • Page: 842
  • View: 4142
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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, 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. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Social Web Artifacts for Boosting Recommenders

Social Web Artifacts for Boosting Recommenders

Theory and Implementation

  • Author: Cai-Nicolas Ziegler
  • Publisher: Springer
  • ISBN: 3319005278
  • Category: Computers
  • Page: 187
  • View: 5815
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Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the “Web 2.0” or the “Social Web”: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties – when used as proxies for interest similarity – are able to mitigate the recommenders' scalability problem.

Predicting movie ratings and recommender systems

Predicting movie ratings and recommender systems

A 195-page monograph on machine learning, recommender systems, and the Netflix Prize.

  • Author: Arkadiusz Paterek
  • Publisher: Arkadiusz Paterek
  • ISBN: N.A
  • Category: Mathematics
  • Page: 196
  • View: 2162
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A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems. What's inside:introduction to predictive modeling,a comprehensive summary of the Netflix Prize, the most known machine learning competition, with a $1M prize,detailed description of a top-50 Netflix Prize solution predicting movie ratings,summary of the most important methods published - RMSE's from different papers listed and grouped in one place,detailed analysis of matrix factorizations / regularized SVD,how to interpret the factorization results - new, most informative movie genres,how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,dealing with the cold-start: simple content-based augmentation,description of two rating-based recommender systems,commentary on everything: novel and unique insights, know-how from over 9 years of practicing and analysing predictive modeling.

Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods

Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods

Contextual Advancements and New Methods

  • Author: Dehuri, Satchidananda
  • Publisher: IGI Global
  • ISBN: 1466625430
  • Category: Computers
  • Page: 350
  • View: 6003
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Although recommendation systems have become a vital research area in the fields of cognitive science, approximation theory, information retrieval and management sciences, they still require improvements to make recommendation methods more effective and intelligent. Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how this could improve this field of study.

Empfehlungssysteme

Empfehlungssysteme

transparente Visualisierung im mobilen Umfeld

  • Author: Marcus Stolzenberger
  • Publisher: Diplomica Verlag
  • ISBN: 3836678918
  • Category: Business & Economics
  • Page: 186
  • View: 1609
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Der kommerzielle Erfolg des station ren Internets hat f r den Beginn eines neuen Informationszeitalters gesorgt. T glich Emails zu empfangen und zu versenden ist f r den Menschen des 21. Jahrhunderts genauso selbstverst ndlich geworden wie das Telefonieren. Er kn pft im Internet Kontakte, t tigt online berweisungen und nutzt es als Einkaufsm glichkeit oder Informationsmedium. Doch das Auffinden der f r ihn relevanten Informationen oder die Produktauswahl in Online-Shops ger t in Anbetracht der im Vergleich zum realen Leben wesentlich gr eren Produktpalette und der Informations- und Datenflut schnell zur Sisyphusarbeit. Das zentrale Problem des Information Overflows ist somit eines der grundlegenden der Informatik, die Nadel in einem exponentiell wachsenden Heuhaufen zu finden. In realen Gesch ften stehen Fachverk ufer bereit, um individuelle Kaufempfehlungen auszusprechen oder den Informationsbedarf individuell zu befriedigen. Doch wer bernimmt diese Beratung in der Online-Welt? Und was genau sind individuell "relevante" Informationen? Welche Informationen sind "gut" und welche eher weniger und wie finde ich schlie lich die qualitativ "bessere" und f r das einzelne Individuum "n tzlichere" Information? Hierf r bedienen sich Unternehmen und Dienstleister so genannter Empfehlungssysteme, die Suchenden Empfehlungen generieren, welche Informationen, welche Produkte oder welche Dienstleistungen f r ihre Bed rfnisse am besten geeignet sind. Gleichzeitig werden diese Systeme von Unternehmen verwendet, ihre Kunden bei der Produktsuche zu unterst tzen und ihr Angebot durch gezielte Werbung zu individualisieren und personalisieren, den Kunden an sich zu binden und in letzter Konsequenz den Unternehmensgewinn zu maximieren. Dieses Buch stellt die verschiedenen Arten und die zugrunde liegenden Algorithmen von Empfehlungssystemen dar und veranschaulicht diese anhand zahlreicher Praxisbeispiele. Ebenso wird die Geschichte des mobilen Internets beleuchtet und der Einzug von so

Statistik-Workshop für Programmierer

Statistik-Workshop für Programmierer

  • Author: Allen B. Downey
  • Publisher: O'Reilly Germany
  • ISBN: 3868993436
  • Category: Computers
  • Page: 160
  • View: 7722
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Introduction to Data Science

Introduction to Data Science

A Python Approach to Concepts, Techniques and Applications

  • Author: Laura Igual,Santi Seguí
  • Publisher: Springer
  • ISBN: 3319500171
  • Category: Computers
  • Page: 218
  • View: 2076
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This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

User Modeling, Adaptation, and Personalization

User Modeling, Adaptation, and Personalization

17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy, June 22-26, 2009, Proceedings

  • Author: Geert-Jan Houben,Gord McCalla,Fabio Pianesi,Massimo Zancanaro
  • Publisher: Springer Science & Business Media
  • ISBN: 3642022464
  • Category: Computers
  • Page: 488
  • View: 3490
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Research on user modeling (UM) and personalization can be traced back to the early1970s,butitwasnotuntilthemid-1980sthatthecommunityofresearchers working on user modeling and user-adaptive systems started its own series of - ternational meetings on UM. After three international workshops in 1986, 1990, and 1992, User Modeling was transformed into an increasingly prominent bi- nial international conference. Its sustainability was ensured by User Modeling Inc. (http://www. um. org), a professional organization of researchers that has solicited and selectedbids to run the conference,nominated programchairs,and provided ?nancial backing to UM conferences. Between 1986 and 2007, 11 UM conferences were held (including the three workshops just mentioned), bringing together researchers from many areas and stimulating the development of the ?eld. Since the early 1990s, the rapid growth of the World Wide Web and other new platforms has populated the lives of an increasing number of people with a great variety of computing systems. This rampant growth has tended to increase the need for personalization,a topic that more and more researchersand practiti- ers are addressing and that has given rise to several new conferences. Among them,anotherbiennialseriesonAdaptiveHypermediaandAdaptiveWeb-Based Systems (Adaptive Hypermedia or AH for short) quickly established itself as a majorforumandsistereventtoUM,running onalternateyearswithit. Between 2000and 2008,?veAH conferenceswere held. During this period, the increasing complexity and prominence of Web systems prompted the enlargement of the list of topics covered by the AH series.

Modeling Techniques in Predictive Analytics with Python and R

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

  • Author: Thomas W. Miller
  • Publisher: FT Press
  • ISBN: 013389214X
  • Category: Computers
  • Page: 448
  • View: 1430
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Knowledge Engineering and Knowledge Management

Knowledge Engineering and Knowledge Management

19th International Conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014, Proceedings

  • Author: Krzysztof Janowicz,Stefan Schlobach,Patrick Lambrix,Eero Hyvönen
  • Publisher: Springer
  • ISBN: 3319137042
  • Category: Computers
  • Page: 620
  • View: 8576
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This book constitutes the refereed proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2014, held in Linköping, Sweden, in November 2014. The 24 full papers and 21 short papers presented were carefully reviewed and selected from 138 submissions. The papers cover all aspects of eliciting, acquiring, modeling, and managing knowledge, the construction of knowledge-intensive systems and services for the Semantic Web, knowledge management, e-business, natural language processing, intelligent information integration, personal digital assistance systems, and a variety of other related topics.

Neuronale Netze selbst programmieren

Neuronale Netze selbst programmieren

Ein verständlicher Einstieg mit Python

  • Author: Tariq Rashid
  • Publisher: O'Reilly
  • ISBN: 3960101031
  • Category: Computers
  • Page: 232
  • View: 4276
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Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Handbook of Ambient Intelligence and Smart Environments

Handbook of Ambient Intelligence and Smart Environments

  • Author: Hideyuki Nakashima,Hamid Aghajan,Juan Carlos Augusto
  • Publisher: Springer Science & Business Media
  • ISBN: 0387938087
  • Category: Computers
  • Page: 1294
  • View: 6374
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Our homes anticipate when we want to wake up. Our computers predict what music we want to buy. Our cars adapt to the way we drive. In today’s world, even washing machines, rice cookers and toys have the capability of autonomous decision-making. As we grow accustomed to computing power embedded in our surroundings, it becomes clear that these ‘smart environments’, with a number of devices controlled by a coordinating system capable of ‘ambient intelligence’, will play an ever larger role in our lives. This handbook provides readers with comprehensive, up-to-date coverage in what is a key technological field. . Systematically dealing with each aspect of ambient intelligence and smart environments, the text covers everything, from visual information capture and human/computer interaction to multi-agent systems, network use of sensor data, and building more rationality into artificial systems. The book also details a wide range of applications, examines case studies of recent major projects from around the world, and analyzes both the likely impact of the technology on our lives, and its ethical implications. With a wide variety of separate disciplines all conducting research relevant to this field, this handbook encourages collaboration between disparate researchers by setting out the fundamental concepts from each area that are relevant to ambient intelligence and smart environments, providing a fertile soil in which ground-breaking new work candevelop.

Social Network-Based Recommender Systems

Social Network-Based Recommender Systems

  • Author: Daniel Schall
  • Publisher: Springer
  • ISBN: 3319227351
  • Category: Computers
  • Page: 126
  • View: 5894
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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.

Die Physik der Welterkenntnis

Die Physik der Welterkenntnis

Auf dem Weg zum universellen Verstehen

  • Author: David Deutsch
  • Publisher: Springer-Verlag
  • ISBN: 3034860196
  • Category: Science
  • Page: 359
  • View: 8638
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Machine Learning Techniques for Improved Business Analytics

Machine Learning Techniques for Improved Business Analytics

  • Author: G., Dileep Kumar
  • Publisher: IGI Global
  • ISBN: 1522535357
  • Category: Business & Economics
  • Page: 286
  • View: 8934
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Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.