Harass Yelp Users With Good Taste
There are so many good restaurants that you don’t want to waste your time and money eating at a bad one. But how do you make sure you like where you’re going? Use this great restaurant recommendation tip from Reddit user Sauwa , share it on the lifeprotips board, and your chances of a satisfying meal will skyrocket—it’s so simple and yet so ingenious that I’m embarrassed I haven’t done it in years. . Here’s how it works: When you’re trying to find a decent place to eat using Yelp or another review site, don’t rely on the collective rating or star count. Instead, find one positive review about a place you already like, and then read what that user liked and didn’t like. If that suits your taste, dig into their post history and eat at other restaurants the user recommends.
You’re essentially putting a stranger in the role that newspaper restaurant critics used to fill, but without having to rely on the tastes of The Sheboygan Press editors. Recommendations from your online stalkers are more likely to match your particular taste and lead you to a decent meal than algorithmic aggregation of opinions from all users.
Why the crowd isn’t necessarily wise when it comes to restaurants
Collecting multiple user ratings and averaging them is a variation on the “wisdom of the crowd” theory first described by the Marquis de Condorcet in 1785. Here’s a simplified explanation: present an obscure specific question with a true or false answer. You would have no way of knowing if one person’s answer was correct, but if more people answered, you could rely on the wisdom of the crowd, even if only a small percentage of respondents actually knew the correct answer. In theory, everyone who didn’t know would be evenly divided between “true” and “false”, neutralizing each other’s votes and leaving the correct answer obvious.
Relying on the opinion of the crowd works great for some reviews, especially for products that have a specific function. If 90% of people who buy a hammer report that it hammers nails fairly well, then it’s probably a good hammer. But how much you can enjoy a restaurant or a movie or a novel is a different matter, because it depends on personal taste. While there are some things most of us agree on when we eat out – restaurants shouldn’t serve raw chicken, for example – the subtleties differ. My idea of a great burger and yours can be very different, and a fantastic hole-in-the-wall rib will still get terrible reviews from people who love fine dining.
Napoleon Dynamite and the love or hate effect
Back in 2006, Netflix started offering a million dollars to anyone who could improve its movie recommendation system. Improvements were made—most people’s tastes in movies are frighteningly predictable—but algorithm after algorithm got stuck on Napoleon Dynamite . It would seem impossible to predict people’s opinions of the 2004 quirky indie comedy (and a few other films) based on other films they’ve enjoyed. But people have strong opinions about Napoleon Dynamite: they either love him or hate him with a little bit of a sweet spot. The result, in terms of review aggregation, is something like 2 1/2 stars out of 5. Average. This is the least likely reaction to watching a movie.
Similarly, this can work with restaurant recommendations, especially for “non-traditional” dishes or anything experimental. If you like spicy food, then the place where they cook real Korean galbi jim gets 5 stars. However, if you don’t like it, it’s inedible – one star. Average, and we’re right in the middle. It doesn’t help anyone.
Potential dishonesty of review aggregation
I don’t know for sure if the reviews on popular restaurant rating sites accurately reflect the opinions of users, but I’d bet a lot of money on no. Whether or not the sites themselves are honest, individual businesses often live or die on positive ratings, and it’s not hard for a business to either boost its reputation with fake positive reviews or score the competition with negative ones. It’s estimated that 20% of online reviews are fake – enough to affect overall rankings, especially for newer places with few reviews.
It is difficult for large platforms to weed out fake reviews (even though they try ), but it is easy for a person to find another real person. To weed out fakes, be suspicious of reviews written in general language, especially in the same language that refers to more than just a place. If you want to get all the internet detectives, do a reverse image search on your profile and food photos to see if they were taken from somewhere else. Once you do that, you will find your personal food authority and you will understand the most delicious burritos in your city.