You’d be surprised how effective NLP is for use when identifying query intent, and pulling out modifiers that should apply as metadata filters.
Weighted keyword search works a lot, but it fails hard for many long tail queries (especially in e-commerce and other attribute heavy domains).
IMO there really isn’t a good excuse for these firms to fail at queries like this. The query itself isn’t particularly difficult when using a decent NLP stack and following well known practices.
Google is already by far the most widely used search engine, so they don't really need to innovate or improve the search product very much in order to attract and retain users. Presumably capturing more advertising spending from the companies paying for ads is a bigger priority.
Microsoft under Satya Nadella has been all about enterprise and cloud, and I doubt Bing is a strategic priority any more, so it's not surprising that they wouldn't put a lot of resources into making it better.
Amazon is a little surprising. You'd think they'd have a lot to gain from making it easier for people to find what they're looking for. But maybe less than perfect search results are deliberate? Maybe it's like how supermarkets put basic items in the back of the store and high-margin impulse buys in the front - so you have to walk past chocolates and chips if you want to buy a carton of milk.
If Amazon is deliberately nerfing search results then maybe Google would stand to benefit from having better shopping-related results - people would get frustrated trying to find a shirt without stripes on Amazon and just use Google instead, letting Google profit from advertising in the process. But maybe people selling shirts aren't willing to pay much for ads, so there isn't much money for Google to make by getting better at finding specific types of shirts.
I dunno if any of these conjectures are anywhere near accurate, but it's interesting to think about.
You can't assume that customers would type one thing or another - you need to gather lots of query log data and see what you find. You'd be surprised how much variation there is, but once you do have this data you can then find patterns to cover lots of (but not all) cases.
So NLP is totally a thing you want to have in search. Arguably, its the whole point of search as it exists now.
"Polka dot shirts"
"Wikipedia list of clothing patterns"
If you go to Google's homepage and click the microphone at the end of the search input box you can search by speaking. All it does is convert to speech to text, but it implies you might be able to search in a more "natural language" way.
Google have a blog post from October last year with some more complex examples of where more sophisticated NLP helps https://www.blog.google/products/search/search-language-unde...
Lots of focus on a general purpose mono-model, but
I think a collection of specialized subsystems is a better representation and would produce better results, faster.
But Siri is a general domain problem, which is really really hard. Siri set the expectation you can ask it anything, and it works terribly and for most questions just gives up and runs a web search.
If you are an e-commerce company though, that's a narrow enough domain, because you know that for most people, they're looking for products to buy or compare. It's not an unbounded Q&A service.
AND keyword LIKE '%searchStr%'
At some point in the future marketers will learn about AGI, and we'll have to make yet another term, maybe artificial general practical intelligence?
There is nothing on the word "intelligence" to imply it's not specialized.
I know what agi is; I just find the terms backward.
It's perfectly ok to prefer the qualifier to go the other way around, keep intelligence general and change the name of the specialized form. But that's just not how our language evolved.