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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (20) Chapters Close

Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Data Analysis FREE CHAPTER 2. Data Preprocessing 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java Java Tools Index

Utility matrices


Most recommender systems use input that quantify users' preferences for items. These preferences are typically arranged in a matrix that has one row for each user and one column for each item. Such a matrix is called a utility matrix. For example, Netflix asks its users to rate movies from one to five stars. So, each entry in that utility matrix would be an integer uij in the range 0 to 5, representing the number of stars that user i gave to movie j, with 0 representing no rating.

For example, Table 9.1 shows a utility matrix that represents users' ratings of beers on a scale of 1-5, with 5 representing the greatest approval. Blanks represent no rating by that user for that item. The beers are: BL = Bud Light, G = Guinness, H = Heineken, PU = Pilsner Urquell, SA = Stella Artois, SNPA = Sierra Nevada Pale Ale, and W = Warsteiner.

Note

Most of the entries are blank.

 

BL

G

H

PU

SA

SNPA

W

x1

 

5

 

4

  

2

x2

2

 

3

  

5

3

x3

1

 

4

 

3

  

x4

3

4

 

5

 

4

 

x5

  ...
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