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Netflix Generates Big Data for To Amp Up Recommendations — Wine Needs To, Too.

This is #5 in Wine Industry Insight’s in-depth series about the quest for the Netflix of wine

Article #1: How A Netflix-Style Recommender Is Vital to Reversing Wine’s Market Marginalization

Article #2: Why solving the “Paradox of Choice” is a major reason Netflix’s recommender is worth $1 Bil/yr. And why wine fails at that.

Article #3: Reviews and 5-Star ratings are so useless for recommendations that Netflix ditched its prized $1-million algorithm. They’re even worse for wine

Article #4: Wine wreck-commendations: Genes determine that no two people taste the same wine the same way.

In article #3 of this series, (Reviews and 5-Star ratings are so useless for recommendations that Netflix tossed its prized $1-million algorithm. They’re even worse for wine) we took a deep look at why Netflix was so eager to move beyond a system that had previously worked so well for it.


In short, Netflix abandoned ratings and reviews because they recognized that the opinions expressed by those ratings and reviews are highly personal, individual perceptions that are both conscious and subconscious experiences that are also shaped by genetic, environmental, psychological, educational, and other factors.


This is the article that was planned to follow #3, but it got bumped because readers wanted more information on why genetics makes it impossible for current methods to make accurate wine recommendations, especially new versions of profile matching systems that have been in play (badly) for decades.

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Collaborative Filtering: This 25-year-old paradigm frustrates but still beats ratings and reviews

As we explored in our last article, genetic variations play a key role in wine recommendation failures. But also at fault are the unsolved problems in Collaborative Filtering which uses data to make recommendations on previous behavior: “people who bought/liked this also bought/liked this.”


According to a scholarly paper  (free PDF) published in 2015 by two of the key Netflix developers of the recommendation system (Carlos Gomez-Uribe, VP of product innovation, and Chief Product Officer Neil Hunt) the rating and recommendation systems lacked the necessary accuracy because of psychological, semantic, personal, and other intractable reasons.

“Now, we stream the content, and have vast amounts of data that describe what each Netflix member watches, how each member watches (e.g., the device, time of day, day of week, intensity of watching), the place in our product in which each video was discovered, and even the recommendations that were shown but not played in each session. These data and our resulting experiences improving the Netflix product have taught us that there are much better ways to help people find videos to watch than focusing only on those with a high predicted star rating.”

What the Gomez-Uribe/Hunt paper mentioned — but never elaborated on — is that the company moved beyond the fairly basic Collaborative Filtering system they had been using to organize their ratings and reviews, in order to expand its use of the Big Data techniques of hoovering up personal information from all over the Internet. This Big Data move was designed to improve recommendation accuracy, which Netflix has demonstrated, is a key element in customer retention.

Despite failures, Collaborative Filtering dominates recommendations.

In general, Big Data Collaborative Filtering systems spew irrelevant recommendations more often than useful ones.


This is evidenced by the reality that only 7% of consumers think that today’s “Big Data” recommendations are useful or relevant.

Screen Shot 2021-03-21 at 11.06.36 AM

But despite this abysmal performance, the 7% of those who do click on a recommendation amount to 24% of e-commerce revenues in general, and 35% of Amazon’s (McKinsey). This is even more vital to wine, given its marginalization relative to Netflix.

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Despite the disdain for current recommendations, our previous article on the stress of choosing among too many options  — the Paradox of Choice — explains why people will click on recommendations, regardless of how frustrating that they can be. Even a lame recommendation may save time and relieve the stress of decisions. It’s important to realize that Collaborative Filtering (CF) is not an inherently bad paradigm on which to base recommendations.


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In the pre-ecommerce, pre-social media era, products -were bought in stores. No accurate, cost-effective way existed for a merchant or producer to sample or directly measure every consumer’s opinion of a physical product purchased or consumed.


Netflix was no different. It sold digital products — CDs — that were imprisoned in plastic. For that reason, ratings and reviews flourished for music (vinyl, tape, or CDs) and movies (tape or CD) because they were the only methods available– usually in print media. But beginning in the mid-1990s the first digital recommendation system — Collaborative Filtering — was invented. That was an epic epoch of change especially when you consider that e-commerce as we now know it had not yet been invented:


Enter Ringo



In today’s online world, Collaborative Filtering Attempts To Eat All The World’s Data


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Quality data needed. Ratings & reviews = GIGO


The Monster that Ringo Begat


Screen Shot 2020-12-24 at 11.29.31 AMHow Ringo and Collaborative Filtering Led Netflix into Big Data

The chart below is a data disclosure cloud composed of exact quotes excerpted from disclosures and privacy notices posted in later December 2020 at It shows not only the data collected from within the Netflix system, but also offers hints and discloses data purchased or obtained from outside sources.

Unredacted inage available to premium subscribers

Unredacted image available to premium subscribers

Every move you make


Big Data works for Netflix PARTLY because of its market environment


Ratings and reviews fail because wine ages


Big Data’s BIGGEST Fatal Flaw: Lack of causality


Netflix gets it partially right

As Netflix wisely recognized, there’s no way to tease causality from a review or a rating. And as Towards Causal Representation Learning explains, even massive amounts of big data lack the necessary causality for precise inferences.

More on why ratings and reviews fail

All of this means that “like this” data for wine is close to meaningless (see links below) and the value of “bought this” data for wine depreciates rapidly.

The links below were written six years ago, but are still valid. Many of these topics (especially regarding genetics) have been updated in this series about Netflix and wine.

Next in this series:

Why Netflix has a leg up on wine because its product experiences are all about sight and sound, while wine’s perception is mostly smell and taste (seasoned by sight, sound, and haptics).  This final article will explain why no one currently in the market can become the “Netflix of wine” without implementing a radically new structure for measuring perception designed to drive recommendations in an open and digitally streamlined sales system.