[DrScientist]

Isn't the problem more fundamental than that?

A vector embedding is choosing a single meaning for my search terms - and if that single meaning is wrong - because I'm not the average person - then I'll struggle to get relevant results.

I guess you can use context to do the mapping - but the rarer the thing I'm trying to find, the less likely this will work?

Note this happens both ends - in parsing both the query and parsing and indexing the original web page.

I suppose if it misinterprets query and page you might get a hit, but then the result you want might be page 700.

There is nothing more annoying than using a search term that you know should be pretty defining and finding the engine deciding to substitute it for a much more common search term.

It's a bit like the Google equivalent of MS clippy - you appear be searching for ......

[jillesvangurp]

I think that's too simplistic. First, you can have more than one vector. More than one model even. The limitations here are cost, performance, memory and bandwidth. Second, search is of course subjective to some extent but that doesn't mean there's no right and wrong at all. A lot of that stuff boils down to context and having enough data in your context to come up with a good query.

And of course getting systematic about evaluating your search ranking performance. A lot of companies don't do that at all. A product manager checking things manually isn't quite good enough here. If you are just winging it like that, vector search isn't magically going to make it better.

And of course a text field is not a great source of any amount of meaning unless the user types a lot. Especially on mobile phones where typing is both tedious and error prone this is actually a huge problem. And the solution isn't better keyboards or training users to type better/more. If your users have to type a full sentence on a mobile phone before you return results, you've already failed them hard.

But what can you show them after they've typed merely two letters? Mostly vector search is more effective in recommendations or more like this style querying. Textual searches tend to be all over the place. The shorter the queries, the less useful vector search becomes. What's the meaning of "ba"? There isn't any other than that it's the beginning of something that may or may not start with those letters or have some of those letters in them and maybe even in that order.

The classic view of search is a text field and a list of a results is not the best mental model for this. Mostly Social Networks, Google (and others) try to pre-empt you having to search for anything by just magically showing you what you didn't know you were looking for. And when you search for stuff, they show you that other stuff anyway. Using Tik Tok is basically using search without using a text field. It just feeds on your behavior. Your behavior is all it needs to know as a context. It's not wrong as long as you keep on looking at stuff. The more you use it, the smarter it gets.

[DrScientist]

I don't care about phone users not being able to type - I'm not searching from a phone.

In a way, that's exactly what I'm talking about - while search might be generally getting better for the average person, it's getting worse for me.

And sure that might be an economic inevitability - though I do wonder about the long tail of search.

We have 3 trends adding together:

* dumbing down the UI - removal of advances ways to tweak the search.

* vectorisation misinterpreting my search.

* ever improving SEO manipulation.

All these mean I see a significant drop in relevance when I'm searching for non-standard stuff - typically of a science/technical nature.

> The more you use it, the smarter it gets.

So if I'm looking for something new, it sends me the same old stuff? Great.

Not saying the companies aren't doing stuff that makes sense for them - it's just not working for me.