by mlthoughts2018 on 6/6/2018, 6:25:15 PM
by imh on 6/6/2018, 6:06:55 PM
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.
by jon_richards on 6/6/2018, 7:36:09 PM
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.
by madrox on 6/6/2018, 7:46:47 PM
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.
by gain_sky on 6/6/2018, 7:01:26 PM
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)..
by bschwindHN on 6/7/2018, 3:34:47 AM
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.
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.
by cavisne on 6/7/2018, 1:28:50 AM
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.
by philliphaydon on 6/7/2018, 5:57:47 AM
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.
by edmundhuber on 6/6/2018, 7:01:54 PM
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?
by contingencies on 6/7/2018, 4:30:57 AM
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/ .. 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...
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.