[jackdh]

Another big difference here is that Algolia does not use machine learning in its algorithms.

This according to an old friend who worked there allowed them to really drill down to why which search results are shown and hence the pay per use does actually make sense.

[mlthoughts2018]

This sounds like comedy to me. Either people say machine learning is magic and solves everything or people say not to use machine learning at all and this lets them drill down and understand?

Many search tasks really do need machine learning, especially variations on collaborative filter and matrix factorization. Mixed modality search often truly does need deep learning and wasn’t even really possible at a level of fidelity suitable for real use cases until maybe 10 years ago.

If Algolia was categorically omitting a whole class of possible solutions, that would be a big red flag, certainly not a reason to think they can drill down to understand search results better.

I worked once on a large ecommerce search engine that had been built with Solr, and the sort order involved crazy hand-tuned boosting scores applied to ngrams of different sizes. None of it was reproducible, nobody knew where the magic boost weights came from, and as the quality of results started to plummet, there was no way to fix it. Everyone was too afraid to modify the magic constants because even slight perturbations created stark visual errors. And this was just for a super simple non-normalized term frequency matrix with boosts. “Not using machine learning” is not at all a signal that your solution won’t end up as a black box with no interpretability.