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.
Were these customers very computer-literate?
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.
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.
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.
Context-aware natural language processing!
When do we want it?
When do we want what?
Me: Restaurant
Google: Restaurant Supply Store
Meat Wholesale Warehouse
Place Where a Taco Cart Occasionally Parks
Former Location of a Restaurant
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.
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.
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.
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.
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.
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.
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!)
> 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?
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.
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/ .. 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-...
[2] https://saltmagazine.asia/food/demon-chef-alvin-leung-collab...
