Recommendation Engines

Author : Michael Schrage
Publisher : MIT Press
ISBN 13 : 0262358786
Total Pages : 306 pages
Book Rating : 4.81/5 ( download)

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Download or read book Recommendation Engines written by Michael Schrage and published by MIT Press. This book was released on 2020-09-01 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: How companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like."

Author : Rounak Banik
Publisher : Packt Publishing Ltd
ISBN 13 : 1788992539
Total Pages : 141 pages
Book Rating : 4.34/5 ( download)

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Download or read book Hands-On Recommendation Systems with Python written by Rounak Banik and published by Packt Publishing Ltd. This book was released on 2018-07-31 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319296590
Total Pages : 498 pages
Book Rating : 4.93/5 ( download)

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Download or read book Recommender Systems written by Charu C. Aggarwal and published by Springer. This book was released on 2016-03-28 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Author : Kim Falk
Publisher : Simon and Schuster
ISBN 13 : 1638353980
Total Pages : 743 pages
Book Rating : 4.80/5 ( download)

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Download or read book Practical Recommender Systems written by Kim Falk and published by Simon and Schuster. This book was released on 2019-01-18 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Dietmar Jannach
Publisher : Cambridge University Press
ISBN 13 : 9780521493369
Total Pages : 352 pages
Book Rating : 4.66/5 ( download)

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Download or read book Recommender Systems written by Dietmar Jannach and published by Cambridge University Press. This book was released on 2010-09-30 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

Author : Jie Lu
Publisher : World Scientific
ISBN 13 : 9811224641
Total Pages : 362 pages
Book Rating : 4.45/5 ( download)

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Download or read book Recommender Systems: Advanced Developments written by Jie Lu and published by World Scientific. This book was released on 2020-08-04 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Author : Martin P. Robillard
Publisher : Springer Science & Business
ISBN 13 : 3642451357
Total Pages : 562 pages
Book Rating : 4.55/5 ( download)

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Download or read book Recommendation Systems in Software Engineering written by Martin P. Robillard and published by Springer Science & Business. This book was released on 2014-04-30 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. “Part II – Evaluation” summarizes methods and experimental designs for evaluating recommendations in software engineering. “Part III – Applications” describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.

Author : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1003 pages
Book Rating : 4.76/5 ( download)

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Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1003 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119711592
Total Pages : 448 pages
Book Rating : 4.99/5 ( download)

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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 2020-06-09 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.

Author : Gulden Uchyigit
Publisher : World Scientific
ISBN 13 : 9812797017
Total Pages : 334 pages
Book Rating : 4.18/5 ( download)

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Download or read book Personalization Techniques and Recommender Systems written by Gulden Uchyigit and published by World Scientific. This book was released on 2008 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed.The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems.This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.