Calibrating Reviews and Ratings

NIPS decision are going out soon, and then we're done with submitting and reviewing for a blessed few months. Except for journals, of course.

If you're not interested in paper reviews, but are interested in sentiment analysis, please skip the first two paragraphs :).

One thing that anyone who has ever area chaired, or probably even ever reviewed, has noticed is that different people have different "baseline" ratings. Conferences try to adjust for this, for instance NIPS defines their 1-10 rating scale as something like "8 = Top 50% of papers accepted to NIPS" or something like that. Even so, some people are just harsher than others in scoring, and it seems like the area chair's job to calibrate for this. (For instance, I know I tend to be fairly harsh -- I probably only give one 5 (out of 5) for every ten papers I review, and I probably give two or three 1s in the same size batch. I have friends who never give a one -- except in the case of something just being wrong -- and often give 5s. Perhaps I should be nicer; I know CS tends to be harder on itself than other fiends.) As an aside, this is one reason why I'm generally in favor of fewer reviewers and more reviews per reviewer: it allows easier calibration.

There's also the issue of areas. Some areas simply seem to be harder to get papers into than others (which can lead to some gaming of the system). For instance, if I have a "new machine learning technique applied to parsing," do I want it reviewed by parsing people or machine learning people? How do you calibrate across areas, other than by some form of affirmative action for less-represented areas?

A similar phenomenon occurs in sentiment analysis, as was pointed out to me at ACL this year by Franz Och. The example he gives is very nice. If you go to TripAdvisor and look up The French Laundry, which is definitely one of the best restaurants in the U.S. (some people say the best), you'll see that it got 4.0/5.0 stars, and a 79% recommendation. On the other hand, if you look up In'N'Out Burger, a LA-based burger chain (which, having grown up in LA, was admittedly one of my favorite places to eat in high school, back when I ate stuff like that) you see another 4.0/5.0 stars and a 95% recommendation.

So now, we train a machine learning system to predict that the rating for The French Laundry is 79% and In'N'Out Burger is 95%. And we expect this to work?!

Probably the main issue here is calibrating for expectations. As a teacher, I've figured out quickly that managing student expectations is a big part of getting good teaching reviews. If you go to In'N'Out, and have expectations for a Big Mac, you'll be pleasantly surprised. If you go to The French Laundry with expectations of having a meal worth selling your soul, your children's souls, etc., for, then you'll probably be disappointed (though I can't really say: I've never been).

One way that a similar problem has been dealt with on Hotels.com is that they'll show you ratings for the hotel you're looking at, and statistics of ratings for other hotels within a 10 mile radius (or something). You could do something similar for restaurants, though distance probably isn't the right categorization: maybe price. For "$", In'N'Out is probably near the top, and for "$$$$" The French Laundry probably is.

(Anticipating comments, I don't think this is just an "aspect" issue. I don't care how bad your palate is, even just considering the "quality of food" aspect, Laundry has to trump In'N'Out by a large margin.)

I think the problem is that in all of these cases -- papers, restaurants, hotels -- and others (movies, books, etc.) there simply isn't a total order on the "quality" of the objects you're looking at. (For instance, as soon as a book becomes a best seller, or is advocated by Oprah, I am probably less likely to read it.) There is maybe a situation-depend order, and the distance to hotel, or "$" rating, or area classes are heuristics for describing this "situation." Bit without knowing the situation, or having a way to approximate it, I worry that we might be entering a garbage-in-garbage-out scenario here.