[tomrod]

Do yall have a technical blog? I would love to both understand the problem and methods, domains yall cross (eg biometrics and fuzzy matching?), and how yall integrate in different industries.

A good search partner is hard to find. PageRank is fun and all, but I believe better methods exist these days.

[marcinzm]

There's two related problems here: finding relevant results and ranking those results. The first is historically done with massive inverted indexes. Page rank is for the second one of ranking those relevant results.

For the first part you can look into "embeddings" and "approximate nearest neighbor lookup" for the modern approaches. That said inverted indexes are still very popular.

The second one is generally called "learning to rank" so you can find a lot of things written on that topic. The biggest issue here imho is what training data you use which gives you examples of good rankings. The best algorithm trained on garbage will give you garbage.

[dustincoates]

Here's a link to our engineering blog posts: https://www.algolia.com/blog/engineering/

And our CTO, Julien, wrote an "Inside the Engine" series on how our search engine works. It doesn't have the new "hybrid search" but it shows you the base of how we do search: https://www.algolia.com/blog/engineering/inside-the-algolia-...