Web Recommendations Systems

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Web Recommendations Systems

Author : K. R. Venugopal
Publisher : Springer Nature
ISBN 13 : 9811525137
Total Pages : 178 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Web Recommendations Systems by : K. R. Venugopal

Download or read book Web Recommendations Systems written by K. R. Venugopal and published by Springer Nature. This book was released on 2020-03-02 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse applications, including query log mining, social networking, news recommendations and computational advertising, and with the explosive growth of Web content, Web recommendations have become a critical aspect of all search engines. The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with valuable insights into Web recommender systems.

Recommender Systems Handbook

Author : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1008 pages
Book Rating : 4.4/5 (899 download)

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Book Synopsis Recommender Systems Handbook by : Francesco Ricci

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 1008 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.

Hands-On Recommendation Systems with Python

Author : Rounak Banik
Publisher : Packt Publishing Ltd
ISBN 13 : 1788992539
Total Pages : 141 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Hands-On Recommendation Systems with Python by : Rounak Banik

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.

The Adaptive Web

Author : Peter Brusilovski
Publisher : Springer Science & Business Media
ISBN 13 : 3540720782
Total Pages : 770 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis The Adaptive Web by : Peter Brusilovski

Download or read book The Adaptive Web written by Peter Brusilovski and published by Springer Science & Business Media. This book was released on 2007-04-24 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt: This state-of-the-art survey provides a systematic overview of the ideas and techniques of the adaptive Web and serves as a central source of information for researchers, practitioners, and students. The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field.

Encyclopedia of Data Warehousing and Mining

Author : Wang, John
Publisher : IGI Global
ISBN 13 : 1591405599
Total Pages : 1382 pages
Book Rating : 4.5/5 (914 download)

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Book Synopsis Encyclopedia of Data Warehousing and Mining by : Wang, John

Download or read book Encyclopedia of Data Warehousing and Mining written by Wang, John and published by IGI Global. This book was released on 2005-06-30 with total page 1382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.

2015 Twelve International Conference on Electronics Computer and Computation (ICECCO)

Author :
Publisher :
ISBN 13 : 9781509002009
Total Pages : pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO) by :

Download or read book 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO) written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Recommendation Systems in Software Engineering

Author : Martin P. Robillard
Publisher : Springer Science & Business
ISBN 13 : 3642451357
Total Pages : 560 pages
Book Rating : 4.6/5 (424 download)

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Book Synopsis Recommendation Systems in Software Engineering by : Martin P. Robillard

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 560 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.

Recommender Systems

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319296590
Total Pages : 518 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Recommender Systems by : Charu C. Aggarwal

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 518 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.

Practical Recommender Systems

Author : Kim Falk
Publisher : Simon and Schuster
ISBN 13 : 1638353980
Total Pages : 743 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Practical Recommender Systems by : Kim Falk

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

Recommender Systems

Author : Dietmar Jannach
Publisher : Cambridge University Press
ISBN 13 : 1139492594
Total Pages : pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Recommender Systems by : Dietmar Jannach

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 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.

Recommender System with Machine Learning and Artificial Intelligence

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119711576
Total Pages : 448 pages
Book Rating : 4.1/5 (197 download)

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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.

Mahout in Action

Author : Sean Owen
Publisher : Simon and Schuster
ISBN 13 : 1638355371
Total Pages : 616 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Mahout in Action by : Sean Owen

Download or read book Mahout in Action written by Sean Owen and published by Simon and Schuster. This book was released on 2011-10-04 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook. About the Technology A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others. About this Book This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework. This book is written for developers familiar with Java -- no prior experience with Mahout is assumed. Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book. What's Inside Use group data to make individual recommendations Find logical clusters within your data Filter and refine with on-the-fly classification Free audio and video extras Table of Contents Meet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me

Soft Computing for Problem Solving

Author : Jagdish Chand Bansal
Publisher : Springer
ISBN 13 : 9811315957
Total Pages : 974 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Soft Computing for Problem Solving by : Jagdish Chand Bansal

Download or read book Soft Computing for Problem Solving written by Jagdish Chand Bansal and published by Springer. This book was released on 2018-10-30 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and brings together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in the areas including, but not limited to, algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

Recommender Systems

Author : Gérald Kembellec
Publisher : John Wiley & Sons
ISBN 13 : 1848217684
Total Pages : 245 pages
Book Rating : 4.8/5 (482 download)

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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.

Rising Threats in Expert Applications and Solutions

Author : Vijay Singh Rathore
Publisher : Springer Nature
ISBN 13 : 9811560145
Total Pages : 799 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Rising Threats in Expert Applications and Solutions by : Vijay Singh Rathore

Download or read book Rising Threats in Expert Applications and Solutions written by Vijay Singh Rathore and published by Springer Nature. This book was released on 2020-10-01 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents high-quality, peer-reviewed papers from the FICR International Conference on Rising Threats in Expert Applications and Solutions 2020, held at IIS University Jaipur, Rajasthan, India, on January 17–19, 2020. Featuring innovative ideas from researchers, academics, industry professionals and students, the book covers a variety of topics, including expert applications and artificial intelligence/machine learning; advanced web technologies, like IoT, big data, and cloud computing in expert applications; information and cybersecurity threats and solutions; multimedia applications in forensics, security and intelligence; advances in app development; management practices for expert applications; and social and ethical aspects of expert applications in applied sciences.

Recommender Systems for the Social Web

Author : José J. Pazos Arias
Publisher : Springer Science & Business Media
ISBN 13 : 3642256945
Total Pages : 226 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Recommender Systems for the Social Web by : José J. Pazos Arias

Download or read book Recommender Systems for the Social Web written by José J. Pazos Arias and published by Springer Science & Business Media. This book was released on 2012-01-24 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recommendation of products, content and services cannot be considered newly born, although its widespread application is still in full swing. While its growing success in numerous sectors, the progress of the Social Web has revolutionized the architecture of participation and relationship in the Web, making it necessary to restate recommendation and reconciling it with Collaborative Tagging, as the popularization of authoring in the Web, and Social Networking, as the translation of personal relationships to the Web. Precisely, the convergence of recommendation with the above Social Web pillars is what motivates this book, which has collected contributions from well-known experts in the academy and the industry to provide a broader view of the problems that Social Recommenders might face with. If recommender systems have proven their key role in facilitating the user access to resources on the Web, when sharing resources has become social, it is natural for recommendation strategies in the Social Web era take into account the users’ point of view and the relationships among users to calculate their predictions. This book aims to help readers to discover and understand the interplay among legal issues such as privacy; technical aspects such as interoperability and scalability; and social aspects such as the influence of affinity, trust, reputation and likeness, when the goal is to offer recommendations that are truly useful to both the user and the provider.

Practical Machine Learning

Author : Ted Dunning
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491915722
Total Pages : 55 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Machine Learning by : Ted Dunning

Download or read book Practical Machine Learning written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2014 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommendation system. Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. Youll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time. Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actionsrather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques.