
Food Discovery with Uber Eats: Building a Query Understanding Engine - myhrvold
https://ubere.ng/2JgqOvt
======
mlthoughts2018
While it’s good information retrieval work, I’m super skeptical that it
actually adds meaningful value for customers.

Having worked on several different search engines at several companies, my
consensus on this stuff is that customers usually just want more simple
boolean filters or multi-choice filters that they can use to precisely control
thevresult set for their preferences, and they want you to clean up the data
backing the filters so they are highly accurate.

Spending huge money on a fancy system to try to understand their intent is
overwrought. Did you ask them? Did you try just empowering them to communicate
their own intent with more filtering or ranking options they can tune or
manipulate on their own?

This stuff annoys me sometimes because it’s dripping with so much marketing
hype. Look at a sexy new embedding-based recommender, or a reinforcement
learning agent that learns how to rank to gamify placed orders.

Maybe you could have better served your users by not spending big salaries +
compute costs on this crazy query understanding model and instead just cleaned
up your data so that you stop getting the opening hours of restaurants wrong,
or you stop giving back BBQ places when somebody checks “vegetarian.”

In so many cases, just some simple filters + data cleaning is worth way more
to the customer than all this.

~~~
amelius
> Having worked on several different search engines at several companies, my
> consensus on this stuff is that customers usually just want more simple
> boolean filters or multi-choice filters that they can use to precisely
> control thevresult set for their preferences, and they want you to clean up
> the data backing the filters so they are highly accurate.

Were these customers very computer-literate?

~~~
mlthoughts2018
It varied depending on the domain. In one company it was a high traffic online
merchant, and customers were basically a random sample of the middle-class
population at large, no better-than-average-person computer literacy.

In another case it was a multimedia company and the search users were
producers, artists and technical directors, with much more of a power user
mentality to searching for what they wanted, and jobs that required basic
computer literacy and maybe familiarity with special GUI programs.

But I’m telling you, in every focus group or A/B test or user session we ever
looked at, at any of these companies regardless of the user characteristics,
the absolute number one thing every time was user requests for more and more
categorical filters to allow them to efficiently exclude huge classes of
results they _know_ are not relevant to them.

Amazon (I did not work there) actually gets this right, with lots of brand,
size, color, Prime-eligible, etc. filters for everything. That stuff requires
no machine learning, is simple to implement and A/B test and measure usage
efficacy for, and people _really_ want it.

------
imh
Search and recommendation is so interesting to me. The goal of it is fuzzy. If
I search "pho" then after the highly reviewed pho places, how might you decide
between really poorly reviewed pho places and really highly reviewed ramen
places?

If you just return the pho, it's doing what I asked for, but not showing me
things I might be interested in. If the other pho places are really bad, maybe
you aren't showing me things I'd actually rather eat.

If you return the ramen too, then you're kinda not doing what I asked, which
is frustrating, but you might be showing me things I'd rather eat, given the
shitty alternatives. When there's no mechanism for me to say "but really, I
just want pho" it can be even more frustrating.

In this case there's a clear line between bad and matching vs good and
related, but in many real queries there's not such a clear line. The smarter
the algorithms get, paradoxically, the more frustrated I get by real world
examples of this dilemma. I guess I don't have much to add, but it fascinates
the hell out of me.

~~~
openasocket
I think the solution is providing results with more structure than just a list
of options. Being able to return something like "here's the list of pho
places, but they aren't very good. We also have some ramen places here that
are pretty good if you are interested." I think just having some sort of
explanation why I've gotten a bunch of ramen places in my results would go a
long way. At least giving some sort of explanation of the results could help a
lot with that frustration. You could go even further and add some level of
interactivity, allow the user to respond with "sure, show me the ramen
results," or "no, show me the pho results anyway" would be the ideal.

The difficulty is that a lot of these sorts of queries, especially when
machine learning is involved, are pretty opaque. You can find some set of
results that meet the maximization of some metric in your feature space, but
it's hard to explain why a particular result shows up there without resorting
to a bunch of equations.

~~~
jon_richards
What do we want?

Context-aware natural language processing!

When do we want it?

When do we want what?

------
jon_richards
One thing I've been annoyed at Google maps for is the inability to search
delis, restaurants, takeout places, etc. all in one query. I want literally
everything within 2 or 3 blocks of me. I usually search "food", "restaurant",
and maybe "takeout" and different stuff pops up for each.

~~~
xapata
It used to show all food places when searching for restaurants. Maybe they've
discovered that, for most folks, those are different searches.

~~~
cptskippy
It use to let you filter by price and cuisine but that seems to be gone.

------
madrox
20 years ago, the best we could do was boolean filters. Then Google came along
and we've been charging forth down the fuzzy road ever since. At the first
WSDM conference, intent was all anyone was interested in figuring out.

Now the population at large is far more computer literate than ever. I wonder
if we still really need fuzzy search for everything in the same way we did 20
years ago. It seems like many geo-related problems would be better served by
better boolean search.

~~~
CamperBob2
Agreed, the loss of "programmer-friendly" features like wildcard and boolean
matching is one downside of the progress made in the last couple of decades.

This isn't such a massive problem with Google as it is on specialized search
engines like eBay's. A fuzzy search is worse than useless when you're looking
for items with very specific names.

------
gain_sky
Reminds me a lot of the show Silicon Valley, where one of the characters has
something like a Phd in artificial intelligence but ends up writing code for
an app which dynamically turns your face into a smiley face (i.e. snapchat)..

~~~
simsla
If I remember correctly, the product in that episode was applying "snapchat
filters" (deformation/modification/accessories) to animal faces. It was
portrayed like this stupid job, but as an ML researcher myself my first
thought was: "that's a pretty cool/challenging problem!"

It's funny how things that can seem hard can be so simple, and vice versa! I
guess it depends on the person whether they're looking for an interesting
challenge, or to make an impact - - because the two are often perpendicular.

~~~
gain_sky
I'm sure it is an interesting problem, but what happens when the majority of
our technical talent goes towards solving useless but "interesting" problems?

Look at the engineers credited at the bottom of the article: "graduate of
Carnegie Mellon University with a degree in Computer Science", "Ph.D. in
Electrical Engineering and Computer Science from UC Berkeley", "Ph.D. in
statistical machine learning from Duke University" these are some extremely
intelligent and educated people but they are using their skills to help you
choose between Chinese food and Indian?

We've already seen this situation described as the "Internet of Stuff Your Mom
Won’t Do for You Anymore” and stuff like this doesn't really help. I really
think that innovation is dead in silicon valley, its more about catering to
needs of nerdy man-boys with lots of money to blow.

~~~
TeMPOraL
> _I really think that innovation is dead in silicon valley, its more about
> catering to needs of nerdy man-boys with lots of money to blow._

I disagree. I think it's about catering to the needs of _normals_ with lots of
money to blow. The only nerdy aspect here is this post. The product itself?
Totally for smartphone-using normies.

~~~
gain_sky
Its 2018, normal people do not have money to blow. The "normals" are the ones
delivering your food, driving you around, doing your laundry, walking your
dog, cleaning your house etc.

~~~
TeMPOraL
The "normals" are also people working in management, marketing, and other
high-paying professions. SV might have extremely high density of tech people,
but programmers are not the only (or even the majority of) high-earners in the
West.

------
bschwindHN
All this bullshit and you still can't make an app that doesn't recommend me
burgers in the "Mexican" category, or Indian in the "sushi" category.

[https://imgur.com/a/l0BE27Z](https://imgur.com/a/l0BE27Z)

And yeah I get it, McDonald's probably paid you a lot of money. But you can
stop shoving the suggestion down my throat.

Uber Eats, at least in Japan, is a joke and this is nothing but AI/ML wankery.

------
cavisne
I wonder if/how they treat the 0 results cases, there isnt much discussion of
this. I know some of the delivery companies are setting up commercial kitchens
for outposts of popular restaurants or even entirely virtual restaurants.
UberEats has a great visibility into this, ie if a lot of people are looking
for pho and not finding results, encouraging a popular pho restaurant to setup
in that delivery area makes sense.

------
philliphaydon
In Singapore we have Uber Eats, Food Panda, and Deliveroo.

They all suck. Not a single one allows you to search menus.

Developers need to stop trying to make complicated search features and go back
to a good old full text search that works.

While the argument may be valid for Udon and Soba... Making correlations
between Japanese and Chinese food is not. Living in Singapore, if I want to
order Chinese food, I do not want Japanese food, they are not similar at all.

~~~
com2kid
> In Singapore we have Uber Eats, Food Panda, and Deliveroo.

Less of a problem in America, where all restaurants of a given type have very
similar menus. For example, most Thai restaurants are going to have the same
dishes, with (maybe!) a few unique ones at each location.

Chinese is one that has started to differ, more and more unique Chinese places
are opening, but there are still few enough of them that anyone interested in
those specialty dishes probably knows which restaurant to order from.

(I'm in Seattle for what it is worth. New York City might be a very different
story!)

------
edmundhuber
I don't want to detract from the message of the article, but:

> For example, an eater might search for udon, but end up ordering soba. In
> this case, the eater may have been looking for something similar to udon,
> such as soba and ramen, instead of only being interested in udon. As humans,
> it might seem obvious; Udon and soba are somewhat similar, Chinese and
> Japanese are both Asian cuisines.

Aren't udon and soba both Japanese?

~~~
jy1
Yeah, that's the point!

~~~
xapata
The author seems to be drawing a parallel between udon and soba, but instead
of saying they're both Japanese, says that China and Japan are both in Asia.
It's somewhat of a non-sequitur that only makes sense if (1) the author
thought that udon is Chinese and soba is Japanese, or (2) the author didn't
realize that the second phrase would not be read as a separate example but
still in the context of "In this case ..." which set up the udon example.

Edit: Looking at the authors' names -- Ferras Hamad, Isaac Liu and Xian Xing
Zhang -- I'm guessing the 2nd is what happened. Somehow I get the sense that
Xian Xing Zhang and Isaac Liu know where udon comes from.

------
contingencies
Hierarchies or simple category memberships are very simplistic: this is 1970s
level natural language semantic processing, but I'm not sure it's appropriate.
For example, Yoshihiro Murata is the 3 Michelin Star chef of Kyoto restaurant
_Kikunoi_. In a June 2015 interview[0] in _Mad Feed_ magazine he stated:

 _I think 50 years from now, the borders will be gone. We won 't be talking
about Nordic cuisine or Japanese cuisine anymore. It will be about what is
delicious, without any sense of nationality. That will be the new cooking
tradition._

The point being, perhaps rigid categories are the wrong approach and tastes
are blending across traditional heirarchies. Last week at F&A Next[1] in
Holland I met people are working on scientific flavor combination based
recommendation engines already. Their market was chefs, but why not sell this
feature directly to consumers?

There's another issue which is Halal in practice can mean anything from
"doesn't include pork products" to "must be a certified Halal restaurant with
certified Halal ingredients". They fail to differentiate this in the text.
Basically it boils down to obtaining detailed personal preference and
restriction information, which is good practice anyway in food service, in
case of deadly allergies.

On the other hand, if you trust Michelin chefs, delivery is perhaps the wrong
approach going forward. In March another three Michelin Star chef, Alvin Leung
of HK's _Bo Innovation_ was interviewed[1] by _Salt Magazine_ and stated:

 _Maybe robots making fried rice is the future._

That's basically what we're working on at [http://infinite-
food.com/](http://infinite-food.com/) .. we figure 3 minutes prep times mean
10-20x speed improvement vs. delivery plus full personalization ... raising
Series A now to launch in three markets next year!

[0] [https://www.madfeed.co/2015/a-japanese-master-on-
innovation-...](https://www.madfeed.co/2015/a-japanese-master-on-innovation-
through-tradition/)

[1] [http://fanext.com/](http://fanext.com/)

[2] [https://saltmagazine.asia/food/demon-chef-alvin-leung-
collab...](https://saltmagazine.asia/food/demon-chef-alvin-leung-collaborate-
chef-liu-ching-hai-summer-palace/)

