Practical Recommender Systems


Practical Recommender Systems
Author: Kim Falk
Publisher: Pearson Professional
ISBN: 9781617292705
Size: 35.99 MB
Format: PDF
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Practical Recommender Systems

Practical Recommender Systems by Kim Falk, Practical Recommender Systems Books available in PDF, EPUB, Mobi Format. Download Practical Recommender Systems books, 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


Practical Recommender Systems
Language: en
Pages: 432
Authors: Kim Falk
Categories: Computers
Type: BOOK - Published: 2019-02-02 - Publisher: Pearson Professional
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
Recommender Systems
Language: en
Pages:
Authors: Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
Categories: Computers
Type: BOOK - Published: 2010-09-30 - Publisher: Cambridge University Press
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
Recommender System with Machine Learning and Artificial Intelligence
Language: en
Pages: 448
Authors: Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta
Categories: Computers
Type: BOOK - Published: 2020-06-09 - Publisher: John Wiley & Sons
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
Kontinuierliche Verbesserung mittels Prescriptive Analytics
Language: de
Pages:
Authors: Thomas Busam
Categories: Technology & Engineering
Type: BOOK - Published: 2020-12-16 - Publisher: Apprimus Wissenschaftsverlag
Sich häufig ändernde Produktionsbedingungen - wenig Personal für Prozessverbesserung - hoher Druck zur Senkung der Produktionskosten: Für dieses Spanungsfeld wird eine Methodik zur Effizienzsteigerung des kontinuierlichen Verbesserungsprozesses mittels Prescriptive Analytics vorgestellt. Die Leitidee besteht darin, einem Prozessverbesserer aus einer standortübergreifenden Wissensdatenbank gemäß Zielrichtung passende Verbesserungsansätze mit prognostizierten Ergebnissen automatisch vorzuschlagen.
Einführung in SQL
Language: de
Pages: 353
Authors: Alan Beaulieu
Categories: Computers
Type: BOOK - Published: 2009-08-31 - Publisher: O'Reilly Germany
SQL kann Spaß machen! Es ist ein erhebendes Gefühl, eine verworrene Datenmanipulation oder einen komplizierten Report mit einer einzigen Anweisung zu bewältigen und so einen Haufen Arbeit vom Tisch zu bekommen. Einführung in SQL bietet einen frischen Blick auf die Sprache, deren Grundlagen jeder Entwickler beherrschen muss. Die aktualisierte 2.
Spring im Einsatz
Language: de
Pages: 559
Authors: Craig Walls
Categories: Computers
Type: BOOK - Published: 2020-01-20 - Publisher: Carl Hanser Verlag GmbH Co KG
- Erstellen reaktiver Anwendungen - Spring MVC für Webanwendungen und RESTful Web Services - Sicherheit für Anwendungen mit Spring Security - Behandelt Spring 5.0 Diese vollständig aktualisierte Ausgabe des Bestsellers »Spring in Action« enthält alle Spring-5.0-Updates, neue Beispiele für reaktive Programmierung, Spring WebFlux und Microservices. Ebenfalls enthalten sind die neuesten
Neuronale Netze selbst programmieren
Language: de
Pages: 232
Authors: Tariq Rashid
Categories: Computers
Type: BOOK - Published: 2017-05-24 - Publisher: O'Reilly
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
Recommender Systems: Advanced Developments
Language: en
Pages: 352
Authors: Jie Lu, Guang-quan Zhang, Qian Zhang
Categories: Computers
Type: BOOK - Published: 2020-08-04 - Publisher: World Scientific
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
Statistical Methods for Recommender Systems
Language: en
Pages:
Authors: Deepak K. Agarwal, Bee-Chung Chen
Categories: Computers
Type: BOOK - Published: 2016-02-24 - Publisher: Cambridge University Press
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
Persuasive Recommender Systems
Language: en
Pages: 59
Authors: Kyung-Hyan Yoo, Ulrike Gretzel, Markus Zanker
Categories: Computers
Type: BOOK - Published: 2012-08-17 - Publisher: Springer Science & Business Media
Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up.