Recommendation Engines
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Recommendation Engines
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Author : Michael Schrage
language : en
Publisher: MIT Press
Release Date : 2020-09-01
Recommendation Engines written by Michael Schrage and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-01 with Technology & Engineering categories.
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."
Recommendation Engines
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Author : Michael Schrage
language : en
Publisher:
Release Date : 2020
Recommendation Engines written by Michael Schrage and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Recommender systems (Information filtering) categories.
"How does Netflix know just what to suggest you watch next? How does Amazon determine what a "customer like you" has also purchased? The answer is recommender systems, the technological concept that lies at the heart of most of the successful companies in the digital economy. Michael Schrage starts with the origins of recommender systems, which go back further than you think (see: the Oracle at Delphi for one of history's earliest recommenders), and a history of the first companies to harness recommendations. He then discusses the technology behind how recommenders work: the AI and machine learning algorithms that power these recommender platforms. Next he discusses the role of user experience, and how recommender systems are designed, and how design choices function as nudges to make certain recommendations more salient than others. He explores three case studies: Spotify, Bytedance, and Stitch Fix, looking at how recommenders can create new business solutions and how algorithms can go beyond curation to content creation. The concluding chapter on the future of recommender systems is perhaps the most enlightening. Moving away from technology and business, Schrage embraces the philosophical, probing the role of free will in a world mediated by recommender systems (a recommendation inherently offers a choice; without the element of choice, any digital manipulation of our preferences cannot truly be called a "recommendation"), and exploring the role of recommender systems as a means of improving the self. In the vein of Free Will, this book presents the essential information while revealing the author's point of view. Schrage wants to push our understanding of recommender systems beyond the technological, to understand what societal role they play and what opportunities they offer now and in the future"--
Hands On Recommendation Systems With Python
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Author : Rounak Banik
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31
Hands On Recommendation Systems With Python written by Rounak Banik 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 2018-07-31 with Computers categories.
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.
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.
Recommender Systems
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Author : Gérald Kembellec
language : en
Publisher: John Wiley & Sons
Release Date : 2014-12-15
Recommender Systems written by Gérald Kembellec 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 2014-12-15 with Computers categories.
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.
Building Recommendation Engines
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Author : Sureshkumar Gorakala
language : en
Publisher:
Release Date : 2016-12-30
Building Recommendation Engines written by Sureshkumar Gorakala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-30 with categories.
Understand your data and preferences to make intelligent, accurate, and profitable decisionsAbout This Book* A step-by-step guide to building recommendation engines that are personalized, scalable, and real time* Get to grips with the best tool available on the market to create recommender systems* This hands-on guide shows you how to implement different tools for recommendation engines, and when to use whichWho This Book Is ForThis book caters to beginners and experienced data scientists looking to understand and build complex predictive decision-making systems, recommendation engines using R, Python, Spark, Neo4j, and Hadoop.What you will learn* Building your first recommendation engine* Discover the tools needed to build recommendation engines* Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations* Familiarize yourself with machine learning algorithms in different frameworks* Build different versions of recommendation engines from practical code examplesIn DetailA recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general.If you want to build efficient decision-making systems that will ease your work, this book is for you. This guide will take you on a unique journey of exploring various recommender systems, building them, and implementing them in popular techniques such as R, Python, Spark, and others. This book will cover all that is required to get you up and running with building recommender systems.The book starts with an introduction to recommendation systems and its applications. Then you will start building recommendation engines. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop with practical examples. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation. During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and so on. You will familiarize yourselves with various techniques of recommender systems such as collaborative, content-based, and cross-recommendations before getting to know the best practices of building a recommender system towards the end of the book.
Building Practical Recommendation Engines
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Author : Suresh K. Gorakala
language : en
Publisher:
Release Date : 2017
Building Practical Recommendation Engines written by Suresh K. Gorakala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
"A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general. This video starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, and more. You will get an insight into the pros and cons of different recommendation engines and when to use which recommendation. With the help of this course, you will quickly get up and running with Recommender systems. You will create recommendation engines of varying complexities, ranging from a simple recommendation engine to real-time recommendation engines."--Resource description page.
Personalization Techniques And Recommender Systems
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Author : Gulden Uchyigit
language : en
Publisher: World Scientific
Release Date : 2008-04-04
Personalization Techniques And Recommender Systems written by Gulden Uchyigit and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-04 with Computers categories.
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.
Recommender Systems Algorithms And Their Applications
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Author : Pushpendu Kar
language : en
Publisher: Springer Nature
Release Date :
Recommender Systems Algorithms And Their Applications written by Pushpendu Kar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Realtime Data Mining
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Author : Alexander Paprotny
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-03
Realtime Data Mining written by Alexander Paprotny and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-03 with Computers categories.
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.