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Comment by unoti | Hacker News Reader

I am so glad for this. Yelp ratings have been horribly un-useful to me. Their questionable business practices aside, it's really hard for me to figure out good places to eat when I'm in an unfamiliar city. And Yelp's ratings are to me useless: highly recommended places which are terrible, and mediocre recommendations on places that are actually quite good for what they're aiming to be. One good thing about Yelp is it tells you if places are open now or not, and what kind of dress code to expect.

Yelp provides a service I really need in a way and from a company that I really don't like, so I'm delighted to hear about competition!

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It's hard to escape the fact that "crowdsourced" == "gameable".

My current local demonstration of the pointlessness of crowdsourced reviews:

http://www.urbanspoon.com/r/71/1709549/restaurant/Melbourne/...

A cafe that's not open yet, has 9 votes including 2 "I didn't like it" votes.

It's interesting that St Ali's other café seems to have a string of recent poor reviews (many written in a suspiciously similar style). Surely it'd be easy for UrbanSpoon to do a quick "sanity check", identify the 9 voters who've voted on a venue they couldn't possibly have been to to form a valid opinion, then remove all their votes and reviews across the board?

That's what _I'd_ do if my objective was a fair and balanced review site. Of course if my objective was a Yelp-like advertising service using poor reviews as a way to blackmail small businesses into paying protection/advertising, I'd obviously _welcome_ poor reviews and vindictive downvotes for businesses that haven't even opened yet… UrbanSpoon, like Yelp, is clearly signalling their intentions to me, and calibrating my expectations of their usefulness...

The problems you're talking about are never going to be solved by ratings companies because you're talking about a completely subjective experience.

There is no such thing as an objectively good restaurant. In my town, there are famous pizza places which people absolutely love and then others who think it's an overrated, pretentious mess.

Nevermind attempting to control for different waiters, chefs, people having a bad day, etc. The problem isn't Yelp-specific.

The problem is entirely human.

Yes, and no. The problems of what movies I might like or what music I like are also entirely human, but Netflix and Pandora have been doing a way better job of figuring that out than Yelp has. And what motivation does Yelp have to do better, anyway-- they don't get their money from me, they get it from venue providers both awesome and terrible.

The motivation is to improve their service or risk losing users like you. They have no business model without happy users.

Netflix and Pandora are largely frictionless experiences. If I spend the money and time to get in a cab in NYC to go to a restaurant that sucks, I'm upset. That's the problem that Yelp is trying to solve. I can't quickly "change the channel" with Yelp, or any sort of ratings for that matter. It isn't a technical or algorithmic problem, it's a human one that doesn't scale well. Quantify != Qualify.

Foursquare can recommend places based on your personal history of check-ins. Our recommendations for you need not be the same as our recommendations for your friends.

(That said, the venue ratings product we announced today is globally consistent: everyone will see the same rating for a given place. Our Explore product, however, does make personalized recommendations.)

Nevertheless personalized recommendations doesn't avoid the issue that the parent was talking about. Say based on my personal history, Foursquare figures out that I love Pizza. Say I am in Denver, CO tomorrow twenty feet away from two Pizza places, both of which have equal ratings but only one "real": Let us assume that both don't pay Foursquare any money but one is a false positive due to differences in personal taste, artificial pumping of results etc. An algorithm is not going to obviate the issue of making a wrong recommendation.

I think you might be selling it a bit short...

Imagine if they could build up a ratings profile of people who rate the same place similarly, and then network that out... for instance:

Person 1 likes A and B, dislikes C, and hasn't been to D Person 2 dislikes A, likes B, and C, hasn't been to D Person 3 likes B, C, and D, and hasn't been to A Person 4 likes A and D, hasn't been to B or C

So, A has 2 likes, 1 dislike , B has 3 likes, C has 2 like, 1 dislike, and D has 1 like.

That's the start of a rating scale.

But what if an algorithm could identify that, say, Person 1 and Person 4 have similar tastes... so it could recommend D to 1, and B to 4. It can also see that 2 and 3 have similarity, and recommend D to 2.

Now, here's where it gets a bit tricky. The algorithmn can tease out that A and C are opposites - maybe one has great food, but with bad atmosphere/service, and the other is the opposite.

Thus with that deduction, it can recommend B, but not C to 4.

In an ideal world, I would totally agree with you. However, most recommendation systems deal with three issues:

1. Sparsity of data. People surprisingly rate much less than you think they would. In fact, negative ratings are way less sparse than positive ratings (This to me is unintuitive because this is not how I would act but it is what it is).

2. Lack of features for similarity computation. Sometimes, the rating matrix is all you have to compute similarities or you have crappy metadata. You may turn out to be lucky and pull down a facebook open graph and have enough coverage to work with, it depends on your model.

3. The problem of high variance due to latent features (which you alluded to in the last part): Your model gets harder to track due to in sufficient information as to why a place is good or bad. Maybe, there is a correlation between seasonal variations and special cuisines, maybe they had a shitty chef that one time Person 4 came there.

I am not saying it is not do-able, I am just saying it is hard and sometimes ML fairy dust is not enough. :)

That's actually pretty easy: "People like you rated this place X".

Yay machine learning! Though to scale that is a nontrivial issue...

A not-quite-as-good but easier statement to make: People who like place X also like places Y, Z... etc.

Also, sparsity of data. The fact that people are precious unique snowflakes. :)

> The problem isn't Yelp-specific. .. The problem is entirely human.

Absolutely. So one part of the solution is to stop putting restaurants on an "objective" 1-5 scale, and averaging every human together.

Instead, cluster restaurants so you can "people who liked the overrated, pretentious mess also liked X..."

Or using a backend weighting system for computing total score for a location.

Low weights: new users, numerous reviews (spamming), low rated reviews

High weights: older users, high rated reviews

Yelp actually does have an algorithm for computing the final star rating. It is not a simple average across all ratings. It takes contributor status on Yelp, age of review and other signals into account before the final score is published. You can, of course, dig into the rating distribution to see the spread between 1 and 5 but I doubt that most users go that far.

Google tried this, but to a first approximation nobody used/uses Hotpot/Google Places/Google+ Local.

In my town, there are famous pizza places which people absolutely love and then others who think it's an overrated, pretentious mess

Let me guess, you live in Phoenix?

http://en.wikipedia.org/wiki/De_gustibus_non_est_disputandum

I'm also glad for this. I've always had a love/hate relationship with Yelp.

Sometimes there are places that I've known forever to be a hidden gem. Now they've become Yelp 5-star and the place gets great business but wait times are always an hour.

Sometimes I'm in a new city and I just Yelp for the latest and greatest. This can lead me to some of the best food I've had, complete with menu suggestions and tips from other users. This is where Yelp really excels. It gives great businesses the business they deserve.

Where Yelp fails me is when restaurants get hurt by harsh and poorly written reviews. A few one stars will even make people avoid a business. I've been to a ton of 3 star restaurants personally recommended to me by a friend and they've been fantastic. When I read the Yelp reviews people will rate 1 star for entirely subjective reasons, even worse for poor service when the explained situation seems completely one-sided. It's one thing to give a highly rated place a second opinion, saying it's overrated. It's another thing to harm an innocent small business and in a way preventing other people from giving the place a chance.

And even with well-reviewed places, for large cities there are hundreds of great places buried in the 4-star <100 reviews list. How many people really scroll past the 5 or 6th page when viewing Most Reviewed and Highest Rated?

The last straw would have to be the extortionist behavior of their sales team. But, that's an entirely different story.

Conversely I have had a ton of success with Yelp in unfamiliar cities (small towns not so much, but there are many small towns that simply don't have good places so I don't blame this on Yelp).

I treat Yelp reviews like I do any online review, say Amazon for an example. Stars don't mean too much but if a place has a ton of reviews and very low average that is probably a good signal. I find a few places that fit the bill and then read the reviews in detail and sometimes the other reviews by people who have outlying reviews. Then I usually cross check at places like OpenTable, Zagat and Chowhound.

I don't recall being disappointed and have found some very excellent spots. I was in Atlanta last week and hit up Kevin Rathbun Steak (http://www.yelp.com/biz/kevin-rathbun-steak-atlanta) and JCT Kitchen & Bar (http://www.yelp.com/biz/jct-kitchen-and-bar-atlanta). Both were great.

Update: I'd say that with Yelp I probably miss out on some great spots that don't have much of a presence there, but I also don't strike out. False positives are worse to me than false negatives. Especially if I'm only in town for a few days.

Honestly, the only reason I go to Yelp these days is to see the restaurants hours of operation.

If Google maps would integrate this then I wouldn't have to deal with Yelp's UI.

You could always spice up your life, add a soupçon of adventure, a frisson of excitement by not bothering with reveiews at all. Just go out and find a place yourself. You'll end up interacting with your host city in a far more intimate way, discover something unusual and probably meet new or interesting people along the way. A far more enriching experience than following the app-wielding crowds.

I think time is the most limiting factor in my life, so what you described is near impossible for me.

I live in NYC, and so when my wife and I want to go out to eat or enjoy some activity, factoring in the time it takes to travel (usually 40+ minutes each way to any place interesting in the city), we truly can't afford to tenuously choose the places we dine because we want to hedge our bets that the place we'll be at will be great. If it's not, we just spent several hours going to/at a subpar place when we know there are actually hundreds of really incredible places in the city that we could have gone to instead given we had the knowledge.

It doesn't help that we're both programmers who love to program in our spare time together so even during non-work periods we're also "working" to some extent, so usually on a daily basis the only free time we have is when we're eating or right before bed.

But I digress, TL;DR - Not everyone has the luxury of time and the social prowess to curate locations themselves.

Stack up an ordered list of fall-back options in close travel distance to each other. Try #1, if it looks dodgy fall back to #2, etc. Have a tolerable, reliable thing like McDonalds as a worst case fall-back.

The problem with Yelp for me is that it lumps every review together, when I'm part of a niche market. I wish there were a review service that matched me with restaurants people like me (similar background, favorite restaurants, etc.) enjoy.

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