Foursquare uses it and I've found their scores to be way more useful than Yelp's.
The biggest problem with star ratings is that it's so arbitrary. What is the difference between 3 and 3.5? What is a 1 vs a 2? 3/5 is 60%, that's almost failing when you think about it on a grading scale, if I scored something as a 3/5 I would never use that product or service again, yet, many of the best restaurants are rated 3/5 on Yelp.
Unless the user has some scoring system in place for different qualities of the product or service, there is no way you can get anything resembling an accurate score.
I would never trust a user to accurately assess a score given 10 different options (.5-5) but I would be way more likely to trust a user to say either "I like this product" or "I do not like this product."
But yes, the Wirecutter approach works great, but it just doesn't scale.
If a place has more 5-star ratings than 4-star ratings, it's generally amazing. If it has more 4-star ratings than 5-star ratings, it's generally fine but not something particularly special.
Just thumbs up/down would eliminate what is, to me, the single most useful aspect of Yelp.
It doesn't matter that star ratings are arbitrary -- when you average enough of them out, a clear signal overrides the noise. You can distrust any given user, while still trusting the aggregate.
(Curiously enough, I don't find any equivalent value on Amazon. On Yelp, you're really evaluating an overall experience along a whole set of dimensions, so there's a lot more to discriminate on. On Amazon, it does seem to be more of a binary evaluation -- does the product work reliably or not?)
It ensures votes hold equal weight and that "extreme polar" voters don't skew things. It also avoids the opposite problem of "everything is neutral" vote unless horrible/incredible.
RT also handles high brow and low brow well. You get less voting of "eh I didn't love it, but it's sophisticated so I'll give it an extra star."
I'm sold on simple up/down.
So RT is good at predicting "should I watch this movie I haven't watched before", but bad at predicting more sophisticated habits or preferences. I wouldn't buy the Blu Ray off a RT prediction, but I would rent.
So it becomes a question of what are you trying to accomplish? For some issues up/down is a good way to solve a problem, for others it isn't.
It feels like you're mixing together two different arguments. Rotten Tomatoes is good at predicting whether someone will like a movie. What is "ranking"? That is a very undefined concept. Ranking of what? It's clearly not ranking of likelyhood of a person liking a movie because rotten tomatoes already does that.
Later you mention likelihood of repeat watchings of a movie. Rotten Tomatoes thumbs up or down based on whether someone liked a movie, as a result it produces a metric on likelihood of someone liking of movie. Instead if rotten tomatoes immediately after watching a movie, asking "Did you like this movie?", asked "Would you watch this movie again?" then it would produce an indicator of re-watchability.
Up/down doesn't matter - it's the question that's being asked.
note the caveat RT obviously doesn't actually ask critics these questions, they read and judge their reviews and interpret them as answering those questions.
Really this comes down to how terrible one dimensional comparisons are: it only measure popularity, which is a terrible filter for quality.
Recently, however, it seems like more (imo undeserving) movies that are "just ok" - like decent, but nothing special, romantic comedies and big blockbusters - are scoring above 90%. I might be being curmudgeonly about it, but I've nearly stopped checking it because it feels like there's no information there. My theory is that this started happening once Roger Ebert died... without such a leader in the field, no one is willing to say they didn't like a film unless it's obviously very bad.
Amassing a bunch of 4- and 5-star ratings is easy, but leaving nothing for even the most habitual of complainers to complain about? That's an monumental achievement.
A humorous take: https://xkcd.com/1098/
I've been really interested in the idea of emotive reviews as an alternative to single dimensional scores. The best idea I have at the moment is something akin to emoji reactions like you see on GitHub issues, finding a way to encode some feelings relevant to product reviews in a mechanism like that seems really intriguing to me.
(thumbs up) I liked this
(heart) I loved this
(thumbs down) I didn’t like this
(smiling face) This made me happy or satisfied
(frowning face) This made me sad or disappointed
(surprised face) This made me surprised or impressed
(angry face) This made me angry or frustrated
If you’re only using icons to make recommendations to an individual user based on their own history, maybe you don’t need to infer the actual meanings; you can add all sorts of icons without any particular meaning and just make recommendations by correlation:
(thinking face) I’m considering this / I’m confused by or dubious of this
(gear) This was useful / this made me think
(fire) This album was great / this sauce was spicy
(heart eyes) I really want this / this is adorable
...
Just brainstorming here. There are obviously many possible approaches in this space.
Or, one could just let users tag the subject and the interface would display the "weights" of the tags.
A simple utilitarian object? It mostly works or it doesn't.
A movie? Just to start with, there's the rating of the movie itself vs. the rating for this particular DVD. And then there are the dimensions on which the movie itself could be rated.
Or you just throw your hands up in the air and either do a thumbs up/down or a 5 star rating system on the grounds that it's better than nothing.
I wouldn't trust an engineering graduate who scored 100% on all their exams to design a bridge at all. Where as someone with 10+yrs relevant experience but who got 60-70% in their exams would be preferable to me.
Mastery of the math isn't that relevant due to all the design standards you have to understand and comply with anyway, while all the little pragmatic solutions to real world constraints (incl how the builders work and what they need to be effective) learnt from experience and mentoring from your senior peers are far more important.
That would be terrible test design. At my (German) university, most Multiple Choice tests give one point for a correct answer, minus half a point for a wrong answer. That way you expect negative points from people who think they know everything but are no better than random guessing, zero points from somebody who knows nothing, some points from someone who can always narrow it down to two choices.
I guess my point is that you can arbitrarily raise the floor with a bad grading scheme, but there's no inherent reason to do that.
Strangely, it gets even harder with the thumbs down—there are vanishingly few things i actively wish didn't exist. Why downvote at all?
Personally I just stop watching the moment I feel regret—the thumbs down button has no role in how I consume.
Two star ratings, though—that is meaningful, at least to me.
Without your thumbs up/down feedback it is hard for Netflix to figure out what is your opinion about the movie.
School grading systems serve a completely different purpose and are a terrible comparison.
If any given option only gets a small handful of votes, then you might see a strong bias (favourable or otherwise) where neutral would be appropriate.
In Likert scale design (where favourability options >2), there's a strong debate over even or odd choices -- should someone be able to give a "meh" rating, or do you want to force a positive or negative, if even slight.
Hence, 3, 4, 5, 6, and 7 point (typically) scales.
For example, Uber seems to think anything but a 5/5 is a failure. I know this so I skew to accommodate, but in my personal ranking system I've only had a couple 5 star rides (someone really going above and beyond).
Up/down with an optional qualifier afterwards (e.g. "why were you unhappy?" after a thumbs down) seems to remove a lot of confusion.
I think that a wording and iconography in 4 steps could be useful. -2 = something really bad happened, -1 = below expectations, 0 = happy customer, 1 = exceeded expectations. Forcing people to write a detail on any rating other than 0 would make most ratings 0. Angry people usually like to write comments anyways.
To use an example I gave elsewhere. I order a cable from Amazon. It works. Therefore it met my expectations of a working cable. Yet, I think most people would interpret a 3-star rating as my being lukewarm on my purchase. I'm not. But what the heck do I expect a cable to do other than being a fair price and to work?
With respect to movies. Some movies get really built up and I go in expecting great things (e.g. Fury Road). I come out thinking they were just OK. So maybe 3 stars. But definitely not -1 or 2 stars. My personal expectations aren't necessarily a good baseline.
For movies I would love to have reviews in style: Somebody writes up their expectations before going to the movie, and then rates the movie according to that. I find most "press and critics" reviews useless as if somebody is not a fan of story-less action movies, then why the hell is he reviewing them.
The way I generally rate things falls in line with this idea:
5: Excellent
4: Pretty Good
3: Average; Unsurprising. Not Impressed, Not Disappointed
2: Kinda Sucks
1: Run Away
This definitely doesn't seem to be how everyone else is using the 5-star scale.
Stack Exchange has this to an extent, where negative ratings cost the rater points -- you have to really want to assign a negative.
The problem with an up/down for me is that it doesn't capture an ambivalent reaction. That means transactions will tend toward the mode, imo. You will do enough to get a thumbs up, that's all.
Overall number of votes matters too.
The ultimate question is is this going to be useful to me, and the answer to that is ... somewhat complicated.
Informative, timely, accurate, significant (which may be none of the above), funny (may be appropriate or inappropriate, based on context and/or volume).
Some information is often (though not always) better than no information. Bad information is almost always worse.
(Aside: troubleshooting a systems issue yesterday I had the problem of someone trying to offer answers to questions where "I don't know" was far more useful than "I think ...". Unfortunately, I was getting the "I think ..." response, though not phrased as such.)
What you describe, the wirecutter treatment, is the case of an expert opinion. Here there remain issues -- particular of the biased expert. But if I could give a hierarchy of opinions from least to most useful:
-2. Biased. -1. Ignorant. 0. None. 1. Lay. 2. Novice. 3. Experienced. 4. Expert. 5. Authority in field and unbiased.
Note that the problem of judging expertise itself recapitulates much of the same problem.
Qualification and reputation of the raters themselves is a critical element missing from virtually all ratings systems.
The other alternative is for users to actually SORT and RANK all products in that category that they have reviewed. Not a tenable solution.
Side comment, the Yelp histograms ARE useful... but that is more of a side effect/emergent from a bad rating scheme than anything else. Because people are using the stars not ideally, the histogram gives you insight into that. So it's not a bad solution, but a better solution would be something other than the stars.
The real win would be empowering the user to choose their own rating style. I don't see this happening because it's much harder to push content at users this way.
[1] https://www.theatlantic.com/technology/archive/2014/01/how-n...
Netflix is interesting because their content is pretty bad (compared to say IMDB they have very few movies); recommendations are incredibly useful to pretend its library is much larger than it actually is.
I wonder why they didn't go the amazon route of actual reviews + semantic analysis.
Mostly I just hate cross referencing netflix with reviews to figure out if it's worth my time.
There's also things to consider, like time, that becomes relevant. Dichotomous ratings are known to be inferior statistically speaking, but they are faster, so there's a convenience angle. Tradeoffs.
These discussions always get frustrating to me because there's so much armchair ad hoc stuff that goes on when there's a huge scientific literature on this already.
People also don't seem to be aware of the assumptions they're making. About ratings being skewed, for example: for a lot of products, people probably do kind of want to know basically "is this meeting my needs?" and then everything is just a decrement away from that. Laundry detergent, for example, is something where I want it to clean my clothes well without damaging them. Why should that be normally distributed?
Also, there's a difference between ratings and how they're used. My guess is that 1-3 star rating variance is meaningful from an experiential point of view, but not from a purchasing point of view. That is, if you had the choice of a 3-star product or a 1-star product, I think people would prefer the 3-star product. When we say "1-3 stars don't matter" we don't actually mean that, we mean that they don't matter because it's below our threshold of what we'd be willing to spend money on.
But, to your overall point, there are a lot of things that I just want a hopefully mostly unbiased expert to tell me what to buy and I'm just fine with that. When I buy a garden hose nozzle, I'm just fine with whatever one of Wirecutter's sister sites tells me to buy. I don't need or want to do a lot of research into the finer points of garden hose nozzle design.
1. How long did it take for the person to vote in the first place? (Might change weight; if we're talking cars, nobody really knows if they "like" it 4 minutes after purchasing and it means something different if the rating appears 3 months later.)
2. Has the vote changed between up/down? Has this happened twice?
3. Has the person voted for other things in similar categories? Might make sense for phones, over a period of years. Doesn't make sense for a person to buy and rate 20 different chairs in a week. Use it to give credibility.
The problem with dichotomous ratings (binary, thumbs up-down) is that they lose a lot of meaningful information without eliminating the problems you're referencing.
That is, the same problems apply to dichotomous ratings, in that people still have tendencies to use the rating scale differently. Some tend to give thumbs up a lot, others down, and people interpret what's good or bad differently. People who are ambivalent split the difference differently.
On top of that, you lose the valid variance in moderate ranges, and actually amplify a lot of these differences in use of the response scale, by forcing dichotomous decisions, because now you've elevated these response style differences to the same level of the "meaningful part" of the response. E.g., maybe one person tends to rate things more negatively than another person, rating 4 and 5 respectively. But when you dichotomize, now that becomes 1 and 2.
The question is whether or not, on balance, the variance associated with irrelevant response scale use is greater than the meaningful variance, and generally speaking studies show the meaningful variance is bigger. In general, you see a small but significant improvement in rating quality going from 2 to 3, and from 3 to 4, and then you get diminishing returns after 4-6 options.
Also, people really don't like being forced to take ambivalence and choose up or down, so in the very least having a middle option is better (unless you want to lose ratings).
It's fairly straightforward to adjust for rating style differences if you have a bunch of ratings of an individual on a bunch of things whose rating properties are fairly well-known. Amazon could do this if they wanted to, and Rotten Tomatoes I think might do something like this already.
RT, in fact, is kind of a bad example, because their situation is so different from typical product ratings, in that you have a small sample of experts who are rating a lot of things. They also are aggregating things that themselves are not standardized-- their use of the tomatometer in part stems from them having to aggregate a wild variety of things, as if everyone on Amazon used a different rating scale, or no rating scale at all. Note too that there's then a "filtering" process involved by RT. Finally I also feel obliged to note they do have ratings and not just the tomatometer, which I've started paying attention to after realizing that things like Citizen Kane show up as having the same tomatometer score as Get Out--a fine movie but not the same.
The game theory angle is interesting to think about. It's something I don't deal with usually because in the situation I'm used to, the raters don't have access to other rater's ratings. That's one solution, but impractical. A sort of meta-rating is one solution--a lot like Amazon's "helpfulness" ratings. It's imperfect but probably does well in adjusting for game theory-type phenomena, like retaliatory rating, etc.
I usually don't want ratings, I want the Wirecutter treatment. Sometimes, I know/care enough to really research the topic, in which case star reviews are relatively unhelpful. The rest of the time, I just want someone trustworthy to say "buy this if you want to pay a lot, buy this if you want something cheap, but this third thing is no good at any price".