Big Data Recommender Systems
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Big Data Recommender Systems
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Author : Osman Khalid
language : en
Publisher:
Release Date : 2019
Big Data Recommender Systems written by Osman Khalid and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Big data categories.
The increase in the volumes of data creates new challenges that require novel and more efficient solutions to handle big data and scalability issues. Today we are witnessing advances in many areas that are dependent on recommender systems such as social networks, e-commerce websites, search engines, blogs, and sensor networks. Many of these advances are due to the many developments in algorithmics and analytics, wireless networking, Internet of things, high performance computing, and more. Volume 2, Big Data Recommender Systems: Application Paradigms, presents a good overview of the many applications that show the richness of this field and its great potential.
Big Data Recommender Systems
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Author : Osman Khalid
language : en
Publisher: Institution of Engineering and Technology
Release Date : 2019-04
Big Data Recommender Systems written by Osman Khalid and has been published by Institution of Engineering and Technology this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04 with Computers categories.
First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.
Big Data Recommender Systems
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Author : Osman Khalid
language : en
Publisher:
Release Date : 2019
Big Data Recommender Systems written by Osman Khalid and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Machine Learning
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Author : Oliver Theobald
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024
Machine Learning written by Oliver Theobald and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Electronic books categories.
Recommender Systems
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Author : Monideepa Roy
language : en
Publisher: CRC Press
Release Date : 2023-06-19
Recommender Systems written by Monideepa Roy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-19 with Computers categories.
Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
Statistical Methods For Recommender Systems
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Author : Deepak K. Agarwal
language : en
Publisher: Cambridge University Press
Release Date : 2016-02-24
Statistical Methods For Recommender Systems written by Deepak K. Agarwal and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-24 with Computers categories.
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.
Building A Recommendation System With R
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Author : Suresh K. Gorakala
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-29
Building A Recommendation System With R written by Suresh K. Gorakala and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-29 with Computers categories.
Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.
Practical Recommender Systems
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Author : Kim Falk
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-18
Practical Recommender Systems written by Kim Falk and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-18 with Computers categories.
Summary Online recommender systems help users find movies, jobs, restaurants-even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors. About the Book Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows. What's inside How to collect and understand user behavior Collaborative and content-based filtering Machine learning algorithms Real-world examples in Python About the Reader Readers need intermediate programming and database skills. About the Author Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems. Table of Contents PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS What is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate them Non-personalized recommendations The user (and content) who came in from the cold PART 2 - RECOMMENDER ALGORITHMS Finding similarities among users and among content Collaborative filtering in the neighborhood Evaluating and testing your recommender Content-based filtering Finding hidden genres with matrix factorization Taking the best of all algorithms: implementing hybrid recommenders Ranking and learning to rank Future of recommender systems
Collaborative Filtering Using Data Mining And Analysis
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Author : Bhatnagar, Vishal
language : en
Publisher: IGI Global
Release Date : 2016-07-13
Collaborative Filtering Using Data Mining And Analysis written by Bhatnagar, Vishal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-13 with Computers categories.
Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.
Information And Recommender Systems
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Author : Elsa Nègre
language : en
Publisher: John Wiley & Sons
Release Date : 2015-10-02
Information And Recommender Systems written by Elsa Nègre 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 2015-10-02 with Computers categories.
Information is an element of knowledge that can be stored, processed or transmitted. It is linked to concepts of communication, data, knowledge or representation. In a context of steady increase in the mass of information it is difficult to know what information to look for and where to find them. Computer techniques exist to facilitate this research and allow relevant information extraction. Recommendation systems introduced the notions inherent to the recommendation, based, inter alia, information search, filtering, machine learning, collaborative approaches. It also deals with the assessment of such systems and has various applications.