Netflix Prize

Ethics of Big Data: Balancing Risk and Innovation

by Kord Davis and Doug Patterson  · 30 Dec 2011  · 98pp  · 25,753 words

(and increasing number of data breaches) that make cross-correlation and de-anonymization an increasingly trivial task. Let’s not forget the example of the Netflix prize. Finally, there is no mention anywhere, in any policy statement reviewed, no matter what it was called, that addressed the topic of reputation. Reputation might

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

by Foster Provost and Tom Fawcett  · 30 Jun 2013  · 660pp  · 141,595 words

’s Pragmatic Chaos, had seven members. The history of the contest and the team evolution is complicated and fascinating. See this Wikipedia page on the Netflix Prize. [72] Thanks to one of the members of the winning team, Chris Volinsky, for his help here. [73] The debate sometimes can bear fruit. For

Remix: Making Art and Commerce Thrive in the Hybrid Economy

by Lawrence Lessig  · 2 Jan 2009

predictions about how much someone is going to love a movie based on their movie preferences.”27 To achieve this end, Netflix runs a “Netflix Prize”—offering a grand prize of $1 million to anyone who improves Netflix’s own system by more than 10 percent. To enable this competition to happen, Netflix

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v. Google, 234 F.R.D. 674 (N.D. Cal. 2006). 26. Phillip Torrone, “Netflix, Open Up or Die . . . ,” available at link #66. 27. Netflix, Netflix Prize, available at link #67 (last visited July 2, 2007). 28. Tapscott and Williams, Wikinomics, 183. 29. See Tim O’Reilly, “What Is Web 2.0

Machine Learning for Email

by Drew Conway and John Myles White  · 25 Oct 2011  · 163pp  · 42,402 words

matrix multiplications, whether it’s the standard linear regression model or the modern matrix factorization techniques that have become so popular lately thanks to the Netflix prize. Because we’ll treat data rectangles, tables, and matrices interchangeably, we ask for your patience when we switch back and forth between those terms throughout

Machine Learning for Hackers

by Drew Conway and John Myles White  · 10 Feb 2012  · 451pp  · 103,606 words

matrix multiplications, whether it’s the standard linear regression model or the modern matrix factorization techniques that have become so popular lately thanks to the Netflix Prize. Because we’ll treat data rectangles, tables, and matrices interchangeably, we ask for your patience when we switch back and forth between those terms throughout

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very popular measure of performance for assessing machine learning algorithms, including algorithms that are far more sophisticated than linear regression. For just one example, the Netflix Prize was scored using RMSE as the definitive metric of how well the contestants’ algorithms performed. x <- 1:10 y <- x ^ 2 fitted.regression <- lm(y

Avogadro Corp

by William Hertling  · 9 Apr 2014  · 247pp  · 71,698 words

watching. The better Netflix can do this, the more you as a customer enjoy using Netflix’s movie rental service. Several years ago, Netflix offered a million dollar prize to anyone who could beat their own algorithm by ten percent.” “What’s amazing and even counterintuitive about recommendation algorithms is that they

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had accomplished. Their project was the culmination of nearly three years of dedicated research and development. It had started with David’s work on the Netflix Prize before he was hired at Avogadro, although even that work had been built on the shoulders of geniuses that had come before him. Then there

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

by Kevin Kelly  · 6 Jun 2016  · 371pp  · 108,317 words

, February 14, 2015. Netflix announced an award: Preethi Dumpala, “Netflix Reveals Million-Dollar Contest Winner,” Business Insider, September 21, 2009. Forty thousand groups submitted: “Leaderboard,” Netflix Prize, 2009. 150,000 car fanatics: Gary Gastelu, “Local Motors 3-D-Printed Car Could Lead an American Manufacturing Revolution,” Fox News, July 3, 2014. 3

The New Kingmakers

by Stephen O'Grady  · 14 Mar 2013  · 56pp  · 16,788 words

’s own algorithm, Cinematch, attempted to predict what rating a given user would assign to a given film. On October 2, 2006, Netflix announced the Netflix Prize: The first team of non-employees that could best their in-house algorithm by 10% would claim $1,000,000. This prize had two major

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that year, Netflix announced that the team “BellKor’s Pragmatic Chaos”—composed of researchers from AT&T Labs, Pragmatic Theory, and Yahoo!—had won the Netflix Prize, taking home a million dollars for their efforts. A year earlier, meanwhile, Netflix had enabled the recruitment of millions of other developers by providing official

Data Mining: Concepts, Models, Methods, and Algorithms

by Mehmed Kantardzić  · 2 Jan 2003  · 721pp  · 197,134 words

time it could be sensitive to noise and outliers. Ensemble-learning approach showed all advantages in one very famous application, Netflix $1 million competition. The Netflix prize required substantial improvement in the accuracy of predictions on how much someone is going to love a movie based on his or her previous movie

Data Science from Scratch: First Principles with Python

by Joel Grus  · 13 Apr 2015  · 579pp  · 76,657 words

', 0.2886751345948129), ('R', 0.2886751345948129)] For Further Exploration Crab is a framework for building recommender systems in Python. Graphlab also has a recommender toolkit. The Netflix Prize was a somewhat famous competition to build a better system to recommend movies to Netflix users. Chapter 23. Databases and SQL Memory is man’s

The Filter Bubble: What the Internet Is Hiding From You

by Eli Pariser  · 11 May 2011  · 274pp  · 75,846 words

What's Yours Is Mine: Against the Sharing Economy

by Tom Slee  · 18 Nov 2015  · 265pp  · 69,310 words

AIQ: How People and Machines Are Smarter Together

by Nick Polson and James Scott  · 14 May 2018  · 301pp  · 85,126 words

Binge Times: Inside Hollywood's Furious Billion-Dollar Battle to Take Down Netflix

by Dade Hayes and Dawn Chmielewski  · 18 Apr 2022  · 414pp  · 117,581 words

Attention Factory: The Story of TikTok and China's ByteDance

by Matthew Brennan  · 9 Oct 2020  · 282pp  · 63,385 words

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos  · 21 Sep 2015  · 396pp  · 117,149 words

Big Data: A Revolution That Will Transform How We Live, Work, and Think

by Viktor Mayer-Schonberger and Kenneth Cukier  · 5 Mar 2013  · 304pp  · 82,395 words

The Formula: How Algorithms Solve All Our Problems-And Create More

by Luke Dormehl  · 4 Nov 2014  · 268pp  · 75,850 words

It's Not TV: The Spectacular Rise, Revolution, and Future of HBO

by Felix Gillette and John Koblin  · 1 Nov 2022  · 575pp  · 140,384 words

Netflixed: The Epic Battle for America's Eyeballs

by Gina Keating  · 10 Oct 2012  · 347pp  · 91,318 words

What Algorithms Want: Imagination in the Age of Computing

by Ed Finn  · 10 Mar 2017  · 285pp  · 86,853 words

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

by Thomas H. Davenport  · 4 Feb 2014

Mining of Massive Datasets

by Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman  · 13 Nov 2014

The Elements of Data Analytic Style

by Jeff Leek  · 1 Mar 2015  · 50pp  · 13,399 words

The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy

by Matthew Hindman  · 24 Sep 2018

The Data Detective: Ten Easy Rules to Make Sense of Statistics

by Tim Harford  · 2 Feb 2021  · 428pp  · 103,544 words

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

by Michael Kearns and Aaron Roth  · 3 Oct 2019

Think Twice: Harnessing the Power of Counterintuition

by Michael J. Mauboussin  · 6 Nov 2012  · 256pp  · 60,620 words

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

by Eric Siegel  · 19 Feb 2013  · 502pp  · 107,657 words

The Creativity Code: How AI Is Learning to Write, Paint and Think

by Marcus Du Sautoy  · 7 Mar 2019  · 337pp  · 103,522 words

Beautiful Visualization

by Julie Steele  · 20 Apr 2010

Too Big to Know: Rethinking Knowledge Now That the Facts Aren't the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room

by David Weinberger  · 14 Jul 2011  · 369pp  · 80,355 words

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)

by Salim Ismail and Yuri van Geest  · 17 Oct 2014  · 292pp  · 85,151 words

Bold: How to Go Big, Create Wealth and Impact the World

by Peter H. Diamandis and Steven Kotler  · 3 Feb 2015  · 368pp  · 96,825 words

Doing Data Science: Straight Talk From the Frontline

by Cathy O'Neil and Rachel Schutt  · 8 Oct 2013  · 523pp  · 112,185 words

Adapt: Why Success Always Starts With Failure

by Tim Harford  · 1 Jun 2011  · 459pp  · 103,153 words

The Art of Statistics: Learning From Data

by David Spiegelhalter  · 14 Oct 2019  · 442pp  · 94,734 words

The Art of Statistics: How to Learn From Data

by David Spiegelhalter  · 2 Sep 2019  · 404pp  · 92,713 words

Smarter Than You Think: How Technology Is Changing Our Minds for the Better

by Clive Thompson  · 11 Sep 2013  · 397pp  · 110,130 words

The Deep Learning Revolution (The MIT Press)

by Terrence J. Sejnowski  · 27 Sep 2018

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

by Valliappa Lakshmanan, Sara Robinson and Michael Munn  · 31 Oct 2020

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig  · 14 Jul 2019  · 2,466pp  · 668,761 words

Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurelien Geron  · 14 Aug 2019

Data Mining: Concepts and Techniques: Concepts and Techniques

by Jiawei Han, Micheline Kamber and Jian Pei  · 21 Jun 2011

Free Speech: Ten Principles for a Connected World

by Timothy Garton Ash  · 23 May 2016  · 743pp  · 201,651 words

When to Rob a Bank: ...And 131 More Warped Suggestions and Well-Intended Rants

by Steven D. Levitt and Stephen J. Dubner  · 4 May 2015  · 306pp  · 85,836 words

Masters of Management: How the Business Gurus and Their Ideas Have Changed the World—for Better and for Worse

by Adrian Wooldridge  · 29 Nov 2011  · 460pp  · 131,579 words

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron  · 13 Mar 2017  · 1,331pp  · 163,200 words

The Slow Fix: Solve Problems, Work Smarter, and Live Better in a World Addicted to Speed

by Carl Honore  · 29 Jan 2013  · 266pp  · 87,411 words

Bad Data Handbook

by Q. Ethan McCallum  · 14 Nov 2012  · 398pp  · 86,855 words

Superintelligence: Paths, Dangers, Strategies

by Nick Bostrom  · 3 Jun 2014  · 574pp  · 164,509 words

Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media

by Tarleton Gillespie  · 25 Jun 2018  · 390pp  · 109,519 words