The Resource Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions, Suresh Kumar Gorakala

Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions, Suresh Kumar Gorakala

Label
Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions
Title
Building recommendation engines
Title remainder
understand your data and user preferences to make intelligent, accurate, and profitable decisions
Statement of responsibility
Suresh Kumar Gorakala
Creator
Author
Subject
Language
eng
Summary
Annotation
Cataloging source
UMI
Dewey number
  • 004.019
  • 006.3/12
Illustrations
illustrations
Index
index present
LC call number
QA76.9.I58
Literary form
non fiction
Nature of contents
dictionaries
Summary expansion
Understand your data and user 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 Build 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 Create efficient decision-making systems that will ease your work Familiarize yourself with machine learning algorithms in different frameworks Master different versions of recommendation engines from practical code examples Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and othersIn 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.The book 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, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best.During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and more. 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!Style and approachThis book follows a step-by-step practical approach where users will learn to build recommendation engines with increasing complexity in every chapter
Label
Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions, Suresh Kumar Gorakala
Publication
Note
Includes index
http://library.link/vocab/branchCode
  • net
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Control code
ocn970351871
Dimensions
unknown
Extent
1 online resource (vii, 347 pages)
Form of item
online
Isbn
9781785883538
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
http://library.link/vocab/ext/overdrive/overdriveId
  • 9c912807-c805-4bb4-a9f0-9e740037317a
  • cl0500000822
http://library.link/vocab/recordID
.b36496376
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)970351871
  • pebc1785883534

Library Locations

    • Deakin University Library - Geelong Waurn Ponds CampusBorrow it
      75 Pigdons Road, Waurn Ponds, Victoria, 3216, AU
      -38.195656 144.304955
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