Building a Recommendation Engine with Scala

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9781785282584: Building a Recommendation Engine with Scala

Key Features

  • Learn the basics of a recommendation engine and its application in e-commerce
  • Discover the tools and machine learning methods required to build a recommendation engine
  • Explore different kinds of recommendation engines using Scala libraries such as MLib and Spark

Book Description

With an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients.

This book provides you with the Scala knowledge you need to build a recommendation engine.

You'll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You'll also discover different machine learning algorithms using MLLib.

As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users' recommendation.

What you will learn

  • Discover the tools in the Scala ecosystem
  • Understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine
  • Familiarise yourself with machine learning algorithms provided by the Apache Spark framework
  • Build different versions of recommendation engines from practical code examples
  • Enhance the user experience by learning from user feedback
  • Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations

About the Author

Saleem Ansari is a full-stack developer with over 8 years of industry experience. He has a special interest in machine learning and information retrieval. Having implemented data ingestion and a processing pipeline in Core Java and Ruby separately, he knows the challenges faced by huge data sets in such systems. He has worked for companies such as Red Hat, Impetus Technologies, Belzabar Software, and Exzeo Software. He is also a passionate member of free and open source software (FOSS) community. He started his journey with FOSS in the year 2004. The very next year, he formed JMILUG―Linux Users Group at Jamia Millia Islamia University, New Delhi. Since then, he has been contributing to FOSS by organizing community activities and contributing code to various projects (for more information, visit http://github.com/tuxdna). He also mentors students about FOSS and its benefits.

In 2015, he reviewed two books related to Apache Mahout, namely Learning Apache Mahout and Apache Mahout Essentials; both the books were produced by Packt Publishing.

He blogs at http://tuxdna.in/ and can be reached at tuxdna@fedoraproject.org via e-mail.

Table of Contents

  1. Introduction to Scala and Machine Learning
  2. Data Processing Pipeline Using Scala
  3. Conceptualizing an E-Commerce Store
  4. Machine Learning Algorithms
  5. Recommendation Engines and Where They Fit in?
  6. Collaborative Filtering versus Content-Based Recommendation Engines
  7. Enhancing the User Experience
  8. Learning from User Feedback

"Sinopsis" puede pertenecer a otra edición de este libro.

About the Author:

Saleem Ansari

Saleem Ansari is a full-stack developer with over 8 years of industry experience. He has a special interest in machine learning and information retrieval. Having implemented data ingestion and a processing pipeline in Core Java and Ruby separately, he knows the challenges faced by huge data sets in such systems. He has worked for companies such as Red Hat, Impetus Technologies, Belzabar Software, and Exzeo Software. He is also a passionate member of free and open source software (FOSS) community. He started his journey with FOSS in the year 2004. The very next year, he formed JMILUG―Linux Users Group at Jamia Millia Islamia University, New Delhi. Since then, he has been contributing to FOSS by organizing community activities and contributing code to various projects (for more information, visit http://github.com/tuxdna). He also mentors students about FOSS and its benefits. In 2015, he reviewed two books related to Apache Mahout, namely Learning Apache Mahout and Apache Mahout Essentials; both the books were produced by Packt Publishing. He blogs at http://tuxdna.in/ and can be reached at tuxdna@fedoraproject.org via e-mail.

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Saleem A. Ansari
Editorial: Packt Publishing Limited, United Kingdom (2016)
ISBN 10: 1785282581 ISBN 13: 9781785282584
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Descripción Packt Publishing Limited, United Kingdom, 2016. Paperback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****.Learn to use Scala to build a recommendation engine from scratch and empower your website users About This Book * Learn the basics of a recommendation engine and its application in e-commerce * Discover the tools and machine learning methods required to build a recommendation engine * Explore different kinds of recommendation engines using Scala libraries such as MLib and Spark Who This Book Is For This book is written for those who want to learn the different tools in the Scala ecosystem to build a recommendation engine. No prior knowledge of Scala or recommendation engines is assumed. What You Will Learn * Discover the tools in the Scala ecosystem * Understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine * Familiarise yourself with machine learning algorithms provided by the Apache Spark framework * Build different versions of recommendation engines from practical code examples * Enhance the user experience by learning from user feedback * Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations In Detail With an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients. This book provides you with the Scala knowledge you need to build a recommendation engine. You ll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You ll also discover different machine learning algorithms using MLLib. As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users recommendation. Style and approach A step-by-step guide full of real-world, hands-on examples of Scala recommendation engines. Each example is placed in context with explanation and visuals. Nº de ref. de la librería AAV9781785282584

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Ansari, Saleem A
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Descripción Packt Publishing Limited, United Kingdom, 2016. Paperback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****. Learn to use Scala to build a recommendation engine from scratch and empower your website users About This Book * Learn the basics of a recommendation engine and its application in e-commerce * Discover the tools and machine learning methods required to build a recommendation engine * Explore different kinds of recommendation engines using Scala libraries such as MLib and Spark Who This Book Is For This book is written for those who want to learn the different tools in the Scala ecosystem to build a recommendation engine. No prior knowledge of Scala or recommendation engines is assumed. What You Will Learn * Discover the tools in the Scala ecosystem * Understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine * Familiarise yourself with machine learning algorithms provided by the Apache Spark framework * Build different versions of recommendation engines from practical code examples * Enhance the user experience by learning from user feedback * Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations In Detail With an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients. This book provides you with the Scala knowledge you need to build a recommendation engine. You ll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You ll also discover different machine learning algorithms using MLLib. As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users recommendation. Style and approach A step-by-step guide full of real-world, hands-on examples of Scala recommendation engines. Each example is placed in context with explanation and visuals. Nº de ref. de la librería AAV9781785282584

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Descripción Packt Publishing 1/4/2016, 2016. Paperback or Softback. Estado de conservación: New. Building a Recommendation Engine with Scala. Book. Nº de ref. de la librería BBS-9781785282584

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Descripción Packt Publishing Limited, United Kingdom, 2016. Paperback. Estado de conservación: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. Learn to use Scala to build a recommendation engine from scratch and empower your website users About This Book * Learn the basics of a recommendation engine and its application in e-commerce * Discover the tools and machine learning methods required to build a recommendation engine * Explore different kinds of recommendation engines using Scala libraries such as MLib and Spark Who This Book Is For This book is written for those who want to learn the different tools in the Scala ecosystem to build a recommendation engine. No prior knowledge of Scala or recommendation engines is assumed. What You Will Learn * Discover the tools in the Scala ecosystem * Understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine * Familiarise yourself with machine learning algorithms provided by the Apache Spark framework * Build different versions of recommendation engines from practical code examples * Enhance the user experience by learning from user feedback * Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations In Detail With an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients. This book provides you with the Scala knowledge you need to build a recommendation engine. You ll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You ll also discover different machine learning algorithms using MLLib. As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users recommendation. Style and approach A step-by-step guide full of real-world, hands-on examples of Scala recommendation engines. Each example is placed in context with explanation and visuals. Nº de ref. de la librería LIE9781785282584

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Descripción Packt Publishing, 2016. Paperback. Estado de conservación: Brand New. 164 pages. 9.25x7.50 inches. In Stock. Nº de ref. de la librería __1785282581

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