What Problems Does Tribes Solve & How?

The Tribes algorithm offers far more accurate recommendations than any other system in use for products and services “of personal taste” including (but not limited to): books, movies, wine, restaurants, cheese, beer, cocktails, and anything “Yelp-able.”

Because wine is probably the most complicated product on the planet to recommend accurately, Tribes has been developed through the hands-on design and prototyping of several wine recommendation systems.

 What Is Tribes?

Tribes creates an intelligent, self-learning recommendation system through the correlation of unequivocal, action-oriented preference expressions in an anonymous social network.

 What Main Problem Does Tribes Solve?

Tribes solves the inaccuracies and other significant flaws in other recommendation systems such as inference engines and collaborative filtering. It is also the only recommendation system without the legal and regulatory hassles of consumer privacy issues.

How Does Tribes Do This?

Tribes solves the problems inherent in existing recommendation systems for “products of taste.” It does this by recognizing that the only relevant information is a single datum: a personal preference expressed in terms of future intentions.

Products of personal taste include wine, books, movies, music, cheese, and restaurants and more. Wine is a good example of why current systems fail.

Wine is a good example of why current systems fail. It’s nearly impossible for a retail consumer to reliable choose a good bottle that they will like enough for a subsequent purchase. Many retail purchases are so disliked that they get poured down the kitchen sink. —  Welcome To The Vino Casino And Wine’s Shaky Core.

 Who Benefits?

  • Consumers benefit because they receive a product, service or benefit they will like without revealing personal data and other privacy issues.
  • Merchants benefit because satisfied buyers become return customers. Plus they avoid privacy concerns.

More About Tribes


Tribes is a massive implementation of “tiny data.”

Other recommendation engines in use today rely upon collaborative filtering which is inherently subjective and inaccurate. This is because it tries to make a guess at recommending products on the basis of “people who bought this also bought this.”

The fact that someone bought something is irrelevant to whether they actually liked it or would buy it again.

And a “rating” of how much they liked that product is not much  better because ratings are biased by genetics, psychology, peer-pressure and other uncontrollable, mistake-inducing factors.


Instead of big data and collaborative filtering, Tribes allows accurate product recommendations by using an action-oriented preference expression.

Significantly, this creates an expressed personal action. This is not a “rating” or an “opinion” subject to psychological, genetic and other biases described in more detail, below.


Tribes correlates a single data point about a single product from thousands … or hundreds of thousands of users, then:

Tribes then clusters people of similar preferences into “trust tribes” that allow recommendations of new products.

Tribes constantly adjusts these cluster relationships as new preferences are expressed and new products added. The Tribes system “learns” and gets smarter with every preference expressed.


Wine offers a good example of why Tribes works better than other product selection systems.

SCALING: There are more products available than there are experts available to review them. Only person-to-person assessments can cover the landscape —  3/4 Of Wine In The US Has NEVER Been Rated By Critics. Tribes scales with every consumer and preference expressed.

GENETICS: There are tens of millions of individual genetic variations in the way people taste and smell — Inherited Taste Chaos Sabotages How Wine Gets Recommended. Tribes connects people who have the same taste preferences.

PSYCHOLOGY: Vino-Anxiety, Stress and Social Pressure. Tribes is anonymous.

MISINTERPRETATION: Words Mean Big Trouble For Tasting Notes. Tribes does not require a description.

INCONSISTENCY:  Rating The Rating Systems. Tribes is not a rating. It is the expression of an action.


People are reluctant to share product recommendations publicly online. And those that are shared are biased and plagued by psychological issues — Vino-Anxiety, Stress and Social Pressure. .

Published academic research indicates that Amazon ratings are biased toward the positive. People are inclined to self-justify their purchases.

In addition, dissatisfaction is usually skewed to extreme negatives. This results when anger takes over and the resulting rants are worse than the actual experience This is especially so in various attempts to punish the product maker with ratings retribution.

Retribution rants are common, especially on sites like Yelp.

Overly positive or negative ratings — rants and raves — are not reflective of the product or service’s actual merits. An anonymous Tribes system running silently, unseen but in parallel with social media can allow public rants and raves while privately connecting people of like preferences. This would offer accurate recommendations without the biases and emotions that afflict public ratings.

Tribes: The Only Recommendation Engine Without  Privacy Concerns & Regulation

Tribes addresses the immense legal and regulatory privacy issues inherent in other recommendation systems.

The “big data collection” used by most significant recommendation engines has exploded into major user privacy concerns. Those concerns make merchants easy targets for legal and regulatory bodies, especially in Europe.

This is because private personal information is increasingly by Google, Amazon, Netflix and others as they attempt to make incremental gains in the accuracy of recommendations by their collaborative and content filtering systems.

Because Tribes is an anonymous social network that clusters numeric metadata rather than user identifications or personal data, it can be implemented in a completely private manner.

Tribes gathers no personal information.

The only information needed from a user is a pseudonym. Tribes can provide accurate recommendations with no other information.

If users desire to opt-in for coupons, special deals on products or other communications from merchants, they may use a pseudonymous email address which does not reveal their identity to the Tribes system.

As regulators, legislators and plaintiff’s attorneys increasingly target privacy violations, a prudently implemented Tribes system will provide a safe haven for merchants and the most accurate recommendations for consumers.


Research has shown that online recommendations are the second most trusted source of product information next to friends and family. This trust persists despite bogus reviews from shills, product manufacturers and others trying to bias a rating.

Tribes relies on the anonymous trust that is gained when a consumer tries and enjoys a recommended product. Their expression (buy again, recommend) strengthens a trust relationship from others. A “not buy or not recommend” weaken or terminate any trust that might exist.

The sole determinate for trust and the formation of “trust tribes” is the positive satisfaction with recommended products.


It’s worth mentioning that profiling has been tried as a  promising solutions to the flaws above.

This involves having an expert profile certain characteristics of a product such as wine. Consumers who try the wine also use the same profile.

Then the system matches the consumer profile with wines they have not tried, but which have similar profiles.

Sadly, that method fails for a combination of most of  the previous factors mentioned — Taste Profiling – Organoleptic Breakdown. Tribes avoids all of these issues,.