It's kinda hard to tell from the article whether "negative" means one-star or, like, three-stars. Because yeah, there are quite a few people who tend to overreact to a less-than-ideal experience where they feel compelled to leave a one-star review because the check took an extra ten minutes to arrive. And surely anyone with a shred of empathy would cut a brand new restaurant some slack if it's not perfect on the first week.
OTOH, people seem to think you're being mean if you leave a 2-3/5 review for a restaurant, and at some point we should acknowledge the fact that some places are just mediocre. And if I go to a thoroughly mediocre Italian restaurant, I don't think it's mean to leave it a mediocre rating, because the average person should know that there are four better Italian restaurants within 15 miles they could go to.
5-star rating distributions are heavily bipolar - maybe like 80% of ratings will be 1 or 5. This is part of why some systems end up switching to thumbs up/down. I don't like it much, but empirically, it's how people seem to use rating scales.
I semi-joke with friends that I wreck the ratings of things just because I try to use the entire scale, so my 4 star "pretty darn good!" rating actually ends up lowering the average.
That's partly the platforms' fault, though: anything less than a perfect rating is assumed to be a sign of a problem that needs to be fixed. There is no room for, "This is as good as it needs to be, but not as good as some other things." The best steak joint in town? 5 stars. McDonald's? 5 stars, unless you're complaining about a specific problem with your order. And many services will send your complaint to the restaurant and the restaurant will respond to you to try to get you to change your mind. Not worth the bother.
When the platform isn't actively hostile to it, I always use the full set of stars with the expectation that it's a normal distribution and 3 stars will be by far the most common rating. It actually works pretty well in some places, e.g., Netflix's DVD rental service, which still uses 5-star ratings, almost always correctly predicts my ratings to within half a star because I've rated a ton of movies.
But I'd never do it to an Uber driver: I may live in a mental world where an uneventful ride ought to be rated 3 stars and someone would have to really go above and beyond to earn 5 stars, but actually doing that would be a cruel act given how the system works in practice.
Back when I looked at the netflix challenge, the baseline above which improvement was barely possible was:
Per person find their average rating, and score movies not by ratings, but by how far the rating deviated from that person's average.
I think that approach got me to 92% accuracy. More advanced math gave me like another 2 percent, with the winner of the challenge having gotten 96% accuracy.
My point being. A very basic recommendation engine system should correct for your 4star is pretty good habit.
Agree that it's very simple to do some basic corrections from individual distributions. I just think it's relatively rare to see any of this in the wild (and when it's there, there's often pushback from people complaining about the process).
OTOH, people seem to think you're being mean if you leave a 2-3/5 review for a restaurant, and at some point we should acknowledge the fact that some places are just mediocre. And if I go to a thoroughly mediocre Italian restaurant, I don't think it's mean to leave it a mediocre rating, because the average person should know that there are four better Italian restaurants within 15 miles they could go to.