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!
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.
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.
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. :)
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.
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.
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.
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.
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.
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.
In the long run, I'm a lot more likely to believe ratings which are generated passively (through one's actions) vs. through conscious effort. Humans are unreliable self-reporters, and in a lot of cases have commercial or other bias. Plus, there are lots of BS Yelp reviews, like "I am giving this place 1 star because the server looks like my ex-boyfriend who cheated on me".
On the other hand, just tracking where I check in on foursquare will be kind of boring. Office, restaurants near the office (which kind of suck, compared to places even a few blocks away), airports, etc. I hope they have some kind of interesting filtering to solve that.
What I'd really like is something built on my actual purchasing history, deeper into the venue than just presence. Knowing that I always get a double double or 4x4 at innout is a pretty valid endorsement. Knowing that whenever I go to Apple stores, I buy Applecare for the products, also useful. It's useful (blinded, statistically) to other people, and presumably could be useful from a loyalty perspective, or just for personal purchase tracking, to me. (I kind of use my Amazon purchasing history like that, now. i.e. "what printer do I have in the office, so I know what toner to buy, when I'm not at the office to check".)
A payment provider (Amex for me, or maybe Square someday) is probably in the best position to do this, actually.
I expect we may reveal what kinds of things we take as good signals, but the exact recipe will probably remain a secret sauce. Even if we don't, most of the components are really quite obvious: what kinds things would people do if they really liked or hated your business? Those are the kinds of things we're measuring.
Yeah, our SoHo office had no power for almost all of last week. We managed to find temporary office space in Midtown to work out of.
Most of the work for venue ratings was done before the storm though. We've been testing the product internally for a few weeks now. That said, a lot of people were getting work done even from our displaced office space.
> What I'd really like is something built on my actual purchasing history, deeper into the venue than just presence
Maybe next Foursquare will launch a Pay with (four)Square competitor. I feel like they're uniquely positioned to get this type of purchasing data/history and do something useful with it. By comparison, Square has 75,000 registered merchants accepting Pay With Square [1]; LevelUp has only 3,000 merchants [2]; Foursquare has 1,000,000 merchants.
"I am giving this place 1 star because the server looks like my ex-boyfriend who cheated on me".
Hardest I've laughed in about a week. +1
I guess the problem is that objective information is hard to standardize in a reviewing system. Plus, I've actually been wondering whether humans would view objective statistics with more faith than human reviews - no matter how ridiculous they are.
I only really care about restaurant reviews, and on Yelp, the only way I've found it to work is to find a single reviewer who is methodical (and basically inhuman) to follow, then rely on that person's reviews.
(http://www.yelp.com/user_details?userid=kSfj8tii1tw8bH1OYmb-... is the finest reviewer ever in the Mountain View/mid-Peninsula level. 75% of all value I get out of Yelp comes from that person; the remaining 25% is knowing about hours, address, and phone number better than crappy restaurant websites.)
Zagat or Michelin with 1000x more reviewers, which is the promise of a site like Yelp or Foursquare. If a business has been around for a few years, I already probably know about it. What I want is great reviews of new businesses, or rapid re-reviews if something changes, which Zagat and Michelin are poor for.
As far as I know, payment providers generally don't have access to the itemized bill for the services or goods purchased, only the total charge, date/time, and the name of the merchant.
It depends a little on the type of purchase. And, with Square, they're providing the POS (or deep POS integration) which makes it easier to get visibility into products.
I believe credit card companies almost all get deep info when purchasing plane tickets. There may be other categories. Even just knowing main bill vs. tip (which you could easily determine from preauth vs. final amount, if restaurants preauth for the billed amount only, which I think some card associations or acquirers mandate) works as a first approximation -- if I tip 25+%, I probably liked the product or service.
There's also the "purchasing card" market, where itemized invoices (level 2 or 3) get sent to the issuer and then to the responsible party.
Local search today is in the state that web search was in, pre-Google. There are lots of Yahoos and Altavistas, but no Google (Yelp is so reminiscent of Yahoo web search).
Foursquare still has a long way to go before it can consistently give better results than Yelp, but this is a great start! Hope some startups get inspired by this and realize that local search is not a dead problem to work on!
Heck, it's in the same state it was post-Google, too, when Citysearch was the disruptor. Yelp started out good, probably very akin to where Foursquare is now (clean-ish data, fresh perspective), but tolerated too much garbage, much like Citysearch did, until the user has to over-engineer their search in order to get relevant results that aren't contained in 400 pages, 5 items per page.
Think of how long it's been since Yelp added any filtering criteria. Take a look at their user-photo gallery functionality. Old, stagnant, and ripe for a new generation. However, since sites have been repeating these mistakes for 15 years, there's no indication that any of these are solvable problems.
Last night at 11pm, I stood a block from penn station trying to find a Subway sandwhich store that was still open. Armed with my iphone, it took me a full 15-20 minutes and a half dozen calls before I found one.
Sometimes I feel like companies like foursquare would be better off working on much, much more basic(and less glamorous) problems than ratings and check-ins stuff.
If only...using our checkin data...there was some sort of way we could figure out when a place was likely to be open. Hrmmmmmmm.
It all fits together my friend. These things just take some time to get done.
-harryh
PS: Imagine you had access to billions of checkins covering 10s of millions of users and places all over the world. What sort of interesting things could you do with that data? Feel like you have some great ideas? http://foursquare.jobs
I wonder if that really adds much information value in the grand scheme of things. Yelp is built for ratings and reviews and I can't see myself making the extra trip to Foursquare to see how its users rate something when Yelp will be far more useful.
But what 4sq has that Yelp doesn't is number of visits (per user) and time-relation of visits between places. Instead of ratings, a metric unique to 4sq would be repeat visits (cleaned in such a way to eliminate superficial checkins) and where people go immediately before and after visiting a given place. I would rather see that kind of behavioral data leveraged more than have one more place where people rate things
> I would rather see that kind of behavioral data leveraged more than have one more place where people rate things
FTFA:
> The data is based on a bunch of data that Foursquare collects, which it says result in more accurate and varied results than the typical ‘other site’, where ratings can all meet in the middle.
You basically just asked for exactly what the article infers that they're doing already.
So I'll rephrase and say that the rating, as it seems to be presented in the app, doesn't give enough context to show how it is different from how 1 to 10 ratings are done elsewhere.
The problem with this is that everyone uses Yelp and nobody but hipsters uses foursquare. I'm fact even hipsters have moved on from foursquare. Without liquidity of use any ratings system will just be so sparse as to be anecdotal.
I don't see a problem wit yelp, reading only a star rating isn't a good way to make it useful, you have to speed read and sample the reviews. At a glance I can tell a reviewer who's fake, or one of the many that just like to hear the sound of their own voice, or they are just an opinionated asshole. But when you speed read a few comments you can pick up general trends. For example if many reviewers all say a hotel was noisy there might be something in it. Ditto for amazon. The "solution" that some have tried is to rate the rating, then use a pagerank-link algorithm to module the effect any one rater on the rating. In other words, if many people who themselves have been rated at rating highly accurately, then my influence on a rating is higher.
The suggestions I have seen here (mainly collaborative filtering) may be of merit, if that's have foursquare plan to do it then with more of a representative sampling of users and maybe it will work well. Currently though they would need a lot more users.
But personally I find yelp very useful, it just requires more effort than glancing at the star rating.
The trouble with these rating systems is that they're usually done by one of two groups: people who wish they were foodies or people who had a bad experience.
It's nice to see Foursquare get into this space, but like another commenter said -- it'd be nice to see them tackle the minor issues like hours of operations and menus (maybe through some sort of reward?).
We definitely agree that menus + hours and all sorts of things like that are important too. We've already got a couple hundred thousand menus in the system and are working on getting more.
Ok, that wasn't fair. A proper foodie is an aficionado.
To compare it to tech, it's the difference between someone who understand technology and has a background in it versus a self-proclaimed 'techno-geek' who slobbers over a best buy flyer.
I think Foursquare has been a Yelp competitor since they launched the Explore feature. I've been using it to find good stuff that my friends have been to. I'm likely to check out a place if a good friend of mine has been there several times than if a few strangers have given it 5/5 stars.
Maybe Foursquare peaked in the Bay Area in 2009, but as a user since 2010 in Toronto, I can safely say that 4sq has been consistently trending upward since I got it. I think the same is true of every city in the world that isn't SF or NY.
I personally prefer http://yask.it 1 to 10 ratings make people think too much, with yaskit is a 5 questions survey, 5 star rating and 1 open question, simple.
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!