4 ms·

It's possible to use an algorithmic approach (i.e. linear regression) to derive the weights for each of these factors.

toto444 4y ago
All you need to do is scrape the whole internet beforehand.
thrwyndx 4y ago
A lot of these factors depend on internal yandex data, serp clicks especially - good luck getting those outside of yandex prod, and those are the strongest signals.
abm53 4y ago
Given some samples of search results we still don't know: - The X matrix (e.g. the page rank score) for each result. - The y vector, i.e the score for each result. Although we can observe the relative ranking in each sample (would be interested to hear about techniques to cope with this).
michaelmior 4y ago
This is assuming the weights are combined via some linear function. That's not necessarily the case. It's likely that the ranking algorithm is more complex than: calculate features, multiply by weights, add to get ranking. Sure you'll probably still be able to learn something more by playing with the different features, but I doubt you'll get any real meaningful "weights." Especially when how many features are calculated is a black box.
- h0l0cube 4y ago
  Why would it be more complicated? A simple dot product would have excellent scaling characteristics
  - toto444 4y ago
    When it comes to ML scalability is a constraint not a goal. The goal is to minimize some loss function and it turns out simple dot product can be outperformed by more complex algorithms. I remember reading a few years ago that most search engines use some tree based model. If that's the case, that means the idea of monotonic linear weights is not relevant.
    - h0l0cube 4y ago
      Can you be more specific? Dot product is about as performant as it gets with linear memory access and SIMD multiply accumulate. Throw random memory access and flow control in there and it’s a struggle to do it faster. Unless the factors are sparse, in which case just elide the zero values.
      - ethbr0 4y ago
        > scalability is a constraint not a goal. The goal is to minimize some loss function
        h0l0cube 4y ago
        My bad. I was under the impression that most search engines are compute bound, but if anything there’s probably a glut of compute for such applications and a market appetite for better results.
        ethbr0 4y ago
        Also, I'd assume it's highly time-agnostic (i.e. content change timespan : compute availability timespan). So you can run your bulk-recomputing whenever you have spare capacity. Stale rankings aren't great, but don't hurt that much. As long as your liveness is more frequently updated, so you don't send people to dead sites.
        h0l0cube 4y ago

All you need to do is scrape the whole internet beforehand.

A lot of these factors depend on internal yandex data, serp clicks especially - good luck getting those outside of yandex prod, and those are the strongest signals.

abm53 4y ago

Given some samples of search results we still don't know: - The X matrix (e.g. the page rank score) for each result. - The y vector, i.e the score for each result. Although we can observe the relative ranking in each sample (would be interested to hear about techniques to cope with this).

michaelmior 4y ago

This is assuming the weights are combined via some linear function. That's not necessarily the case. It's likely that the ranking algorithm is more complex than: calculate features, multiply by weights, add to get ranking. Sure you'll probably still be able to learn something more by playing with the different features, but I doubt you'll get any real meaningful "weights." Especially when how many features are calculated is a black box.

h0l0cube 4y ago
Why would it be more complicated? A simple dot product would have excellent scaling characteristics
- toto444 4y ago
  When it comes to ML scalability is a constraint not a goal. The goal is to minimize some loss function and it turns out simple dot product can be outperformed by more complex algorithms. I remember reading a few years ago that most search engines use some tree based model. If that's the case, that means the idea of monotonic linear weights is not relevant.
  - h0l0cube 4y ago
    Can you be more specific? Dot product is about as performant as it gets with linear memory access and SIMD multiply accumulate. Throw random memory access and flow control in there and it’s a struggle to do it faster. Unless the factors are sparse, in which case just elide the zero values.
    - ethbr0 4y ago
      > scalability is a constraint not a goal. The goal is to minimize some loss function
      - h0l0cube 4y ago
        My bad. I was under the impression that most search engines are compute bound, but if anything there’s probably a glut of compute for such applications and a market appetite for better results.
        ethbr0 4y ago
        Also, I'd assume it's highly time-agnostic (i.e. content change timespan : compute availability timespan). So you can run your bulk-recomputing whenever you have spare capacity. Stale rankings aren't great, but don't hurt that much. As long as your liveness is more frequently updated, so you don't send people to dead sites.
        h0l0cube 4y ago