Algolia raises $110M for its search-as-a-service • Hacker News Board

Re: Algolia raises $110M for its search-as-a-service

#81

Post by plnech » Thu, Oct 17, 2019, 8:12 AM UTC

faceshapeapp wrote: post #17

lettergram wrote: post #16

Earlier quoted context omitted.

It all depends on what you’re trying to find when you search. Google knows your background, Algolia doesn’t. I too built a search engine: https://demo.insideropinion.com/meta_profiles?utf8=%E2%9C%93... I find it works 100x better for me, when I weight results by my expertise. Basically, returning results based on what I already am familiar with.

The results aren't necessarily better because of the Google knowledge of you, because they are pretty good results even in Private browsing without being logged in. It just seems to me that HN search does a simple keyword matching and reordering based on points without any ranking (please someone correct if I'm wrong). So freshness doesn't seem to have any value, but the reality is that, at least in tech, very few co…

FWIW, Private browsing doesn't prevent Google to tailor results to what it can guess about you: https://spreadprivacy.com/google-filter-bubble-study/

Re: Algolia raises $110M for its search-as-a-service

#82

Post by lolive » Thu, Oct 17, 2019, 9:06 AM UTC

faceshapeapp wrote: post #2

Congrats to the team! Although, am I the only one who finds that searching HN through Google gives better results than searching it through the Algolia powered HN search? For example, search "ml" in both, Algolia results are years old and don't seem that relevant, whereas Google picks up more recent threads.

You compare a generic solution vs a highly tuned mission critical search engine.

Re: Algolia raises $110M for its search-as-a-service

#83

Post by Sholmesy » Thu, Oct 17, 2019, 9:16 AM UTC

majormajor wrote: post #52

mlthoughts2018 wrote: post #12

I’ve always had a hard time understanding the value proposition in the same way I don’t understand the value proposition of e.g. AWS Rekognition. Paying per use certainly doesn’t make sense, because it has to be qualified by the accuracy you get per use. And there’s no serious way to understand the accuracy you get per use (on your specific unusual distribution of queries) without employing the expensive ML / stats e…

> And there’s no serious way to understand the accuracy you get per use (on your specific unusual distribution of queries) without employing the expensive ML / stats engineers you probably thought you could avoid hiring by outsourcing to Algolia / Rekognition in the first place. You may simply not be able to do this at all. You might not know how to tell good ML/Stats people from bad. You might not be able to pay the…

At our company we have an "Expert in house ML team", but I don't want them wasting there time with search since it isn't our differentiating factor.

Re: Algolia raises $110M for its search-as-a-service

#84

Post by Mougatine » Thu, Oct 17, 2019, 9:31 AM UTC

j0e1 wrote: post #4

faceshapeapp wrote: post #2

Congrats to the team! Although, am I the only one who finds that searching HN through Google gives better results than searching it through the Algolia powered HN search? For example, search "ml" in both, Algolia results are years old and don't seem that relevant, whereas Google picks up more recent threads.

I have found their search interface for HN ( https://hn.algolia.com ) useful. They have a drop-down for selecting how you'd want to sort the results. From how I see it Google is doing more blackbox magic whereas Algolia is being a faithful/predictable search engine.

Probably because Algolia isn't using yet any machine learning, but more old-school (yet still very good) tries-based search.

And it's probably why also Algolia shines as search engine for a website vs searching the internet like Google. The former has a small scope (looking for videogames on twich, polo on lacoste) while the latter must be personalized for the user (the snake python vs the programming language python).

Re: Algolia raises $110M for its search-as-a-service

#85

Post by mlthoughts2018 » Thu, Oct 17, 2019, 10:38 AM UTC

aphroz wrote: post #39

mlthoughts2018 wrote: post #37

Earlier quoted context omitted.

Totally false. This is like massively overfitting a high-order polynomial regression to your data. The fit looks good enough, then the next data point comes in and breaks in a way the existing model cannot be hacked to account for. The search results you believed were implicitly tuned to some feedback mechanism slowly experience creep as the customer cohort changes and data distribution changes until before you knew…

Not all decisions are good decisions, it depends on who makes the call. In most cases, someone ask you to use a solution because it looks/feels better. In this case Algolia showed how fast and how well it could be implemented. Once the person who takes the decision is convinced it will be implemented. It's mostly marketing. Probably less than 1% of all e-commerce websites measures the impact of a decision.

I did say as much in my original post: Algolia and Rekognition are marketed at CTOs and directors of engineering who want to be sold on a magical line item that removes a whole concept area from their concern, especially one associated with the difficulty of hiring and affording good machine learning staff who can work on the problem both pragmatically and theoretically. They want to be sold a story.

I will say though that your 1% claim is way off in my experience (which includes 3 medium and large ecommerce companies). These companies employ armies of product managers and analytics staff that measure the shit out of everything from the color of a button to the size of font in a banner display for a discount promo code. These things aren’t usually measured because they find value, rather just to give the appearance of data driven decision making and justify job perpetuity.

Re: Algolia raises $110M for its search-as-a-service

#86

Post by factsaresacred » Thu, Oct 17, 2019, 10:39 AM UTC

If you've ever tried installing ElasticSearch and then switched to Algolia you'll understand how great of a product it is.

Now if Firebase could only buy them out and add decent search to their suite of products that would be swell. Mind boggling that Firebase - part of Google - still lacks a decent search solution.

Re: Algolia raises $110M for its search-as-a-service

#87

Post by mlthoughts2018 » Thu, Oct 17, 2019, 10:42 AM UTC

rapind wrote: post #43

mlthoughts2018 wrote: post #35

Earlier quoted context omitted.

You’re not even addressing the engineering costs though. The portion of cost of a search engine solution attributable to the set up of Elastic Search is basically zero. The cost is understanding if the search surfaces relevant items for the specific use case, including asymmetric costs for surfacing bad items in many use cases. Not to mention that plug and play third party solutions like Solr / ES are highly inapplic…

Sounds like a good case for Algolia Professional Services! (If this exists?) I remember pushing Google's search appliance for a large media company some 10 years ago or so (no benefit to myself though, which was pretty noob). It made sense at the time, and solved something for them better than they probably would have implemented it themselves in a good enough way. The most complicated part was setting up rules about…

Except this would be prohibitively expensive. The labor cost of those specialized employees as consultants would be huge.

Re: Algolia raises $110M for its search-as-a-service

#88

Post by mlthoughts2018 » Thu, Oct 17, 2019, 10:44 AM UTC

lonelappde wrote: post #66

mlthoughts2018 wrote: post #35

Earlier quoted context omitted.

You’re not even addressing the engineering costs though. The portion of cost of a search engine solution attributable to the set up of Elastic Search is basically zero. The cost is understanding if the search surfaces relevant items for the specific use case, including asymmetric costs for surfacing bad items in many use cases. Not to mention that plug and play third party solutions like Solr / ES are highly inapplic…

Why do you buy clothes when you could just make them yourself?

A better anology would be to consider a person with very special dietary needs, and then say “why buy dinner when you can make it yourself and actually be sure it meets the dietary needs.”

Re: Algolia raises $110M for its search-as-a-service

#89

Post by mlthoughts2018 » Thu, Oct 17, 2019, 10:45 AM UTC

cglace wrote: post #44

mlthoughts2018 wrote: post #36

Earlier quoted context omitted.

This approach typically fails quite bad in practice. The subjective impression of success depends on the people around at the time of the decision and the set of queries they chose to inspect. As corner cases pop up with significant cost in production (e.g. surfacing nudity in an image search for a query where it’s highly inappropriate) you become less and less capable of understanding why or hacking business logic i…

What do you do when you don’t have the budget for a team of in-house experts and you need something that is good enough and you need it now.

Well, what you don’t do is rush to buy a wrong thing because you’re desperate and it has good marketing.

Re: Algolia raises $110M for its search-as-a-service

#90

Post by mlthoughts2018 » Thu, Oct 17, 2019, 10:54 AM UTC

jackdh wrote: post #78

mlthoughts2018 wrote: post #12

I’ve always had a hard time understanding the value proposition in the same way I don’t understand the value proposition of e.g. AWS Rekognition. Paying per use certainly doesn’t make sense, because it has to be qualified by the accuracy you get per use. And there’s no serious way to understand the accuracy you get per use (on your specific unusual distribution of queries) without employing the expensive ML / stats e…

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.

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.