Supporting
Independent Restaurants

Why Private Ratings Are Better

The History of Public Ratings

Widely recognized as the world's first influential restaurant critic, Alexandre (Balthazar)-Laurent Grimod de La Reynière (1758-1837) originated the double genre of food critic and restaurant guide by creating eight volumes of the famed "Almanach des Gourmands” (the first public restaurant guides c1803-1812).



"Reynière founded a group of critics that put out a monthly journal that discussed dishes prepared by top Parisian restaurants. Later, he would be accused of accepting bribes in exchange for positive reviews, leading him to cancel his publications and move away from Paris" -KaTom



People often ask why we decided to build an AI restaurant recommendation engine powered by private reviews. The answer is simple. What was true in the 18th century remains true today: Once reviews or endorsements are made public by an influential party or company, they become a form of advertising and thus susceptible to commercialization. 

In other words, publicizing data that directly impacts the profitability of someone else’s business inherently creates an incentive to manipulate (or corrupt) that data.



Existing recommendation engines like Yelp and GoogleMaps, that crowd-source public opinions and then present them as unbiased third-party reporting or search results, are the modern versions of this type of advertising.



Although these review platforms have policies that prohibit (and “systems to detect”) fake and solicited reviews, the data they rely upon is as corrupted as their business model. 

They rank and assign one-size-fits all grades to restaurants based on their level of popularity - a methodology that is now as archaic as elections in Ancient Greece where the loudest voices were given the most weight.



Curated public opinions are relatively useless for a small yet increasingly significant percentage of the population whose preferences fall outside the median.



While the practice of giving the loudest voices the most credibility may have proven incredibly profitable during the height of the “keyboard warrior era”, these types of social recommendation apps have been gradually losing credibility with digitally native generations  who are looking for reliable information and more personalized service.

 Yelp (with a declining market cap of $2.94B) is experiencing domestic attrition that outpaces the total number of active users globally*.



Paire (Personal A.I. Recommendation Engine) avoids the conflicts of interests that plague existing platforms and  erode  integrity.



Maintaining a guest-centered approach to every aspect of platform design, including monetization, ensures the integrity of Paire's software and provides an unprecedented level of security for our members. 

We firmly believe that building trust and maintaining integrity is integral to maximizing relevance across a multitude of verticals.

 Our focus remains on celebrating and accommodating diversify of taste and growing revenue.



Paire's reliance on machine learning means we don't  promote, endorse or sell ancillary B2B services to any restaurant. Instead, Paire uses algorithms (similar to those used by dating apps) in order to calculate the mathematical compatibility between each PersonalTaste Profile and the food & beverage outlets available to them. 

Our technology was developed with foundational security that safeguards data against internal and external manipulation. 

At Paire, the only person able to see or change a rating / recommendation is the person who originally entered that rating.



As an additional layer of security, we’ve built the entire system to be free of any incentive to externally alter or artificially generate data. Simply put - even if an account was artificially created or data was amended by an account holder -  the only impact of that corruption would be contained and confined to the compatibilities calculated for that specific account.



Our methodology and technology are unique assets that put us just ahead of the curve. The zero-party data we collect is far more actionable than: category-level filtering, extrapolations derived from curated public reviews, “cookies” or online history data that drives much of the targeted advertising we see today - and generally consider “spam”.











Sources:

*Yelp’s attrition has been attributed to two main factors:



1. Curating reviews of companies while simultaneously selling B2B services to those very same companies is, at best, a conflict of interest...and one that has become more and more obvious to today’s largest group of consumers: Millennials & Gen Zers. 

“According to a Bloomberg report from last year, these young students and professionals command more than $360 billion in disposable income...If a member of Gen Z doesn't agree with the morals of a company, many of them will boycott the products completely and get their friends to do so as well. What advertisers and brand managers learned in their marketing classes years ago is outdated. The best way to learn what Gen Z is looking for from businesses is to ask them.” 

- Jeff Fromm | Forbes



2. As technology advances, the data curated by these companies (public ratings, “Likes” and online reviews) have, according to experts like those at Harvard Business School, become increasingly less reliable since, online content like this is now bought, sold and artificially generated every day . In fact, the commoditization of ratings has resulted in them determining the system is more hackable than many realize: “1 in every 5 Yelp reviews estimated to be fake”. And, not all algorithm manipulation is limited to the benevolence of members in a facebook group who agree to give each other “Likes” and positive reviews in order to off-set the negative ones. If you have $100 and a Fiverr account, you can buy five hundred 4 star ratings and positive reviews for your own business - or five hundred 1 star ratings and negative reviews for your competitor.