Real World Recommendation System
blog.fennel.ai
Real World Recommendation System
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Re: Real World Recommendation System
#2[deleted]
Re: Real World Recommendation System
#3Gentle reminder to anyone reading this that your problems are probably not FAANG problems. If you architect your system trying to solve problems you don't have, you are gonna have a bad time.
Re: Real World Recommendation System
#4The main issue with deploying these systems right now is the technical overhead to develop them out. Existing solutions are either paid and require you to share your valuable data, or open source but either abandoned (rip Crab) or inextensible (most rely on their own DB or postgres).
I’d love to see a lightweight, flexible recommendation system at a low level, specifically the scoring portion. There are a few flexible ones (Apache has one) but none are lightweight and require massive servers (or often clusters). It also can’t be bundled into frontend applications which makes it difficult for privacy-centric, own-your-data applications to compete with paid, we-own-your-data-and-will-exploit-it applications.
Re: Real World Recommendation System
#5With all of this technology applied, I am still disappointed by Netflix's recommendations – to the point of just giving up and doing something else.
Re: Real World Recommendation System
#6With all of this technology applied, I am still disappointed by Netflix's recommendations – to the point of just giving up and doing something else.
In some ways it seems like a classic case of trying to solve the wrong problem because the wrong problem potentially has a technical solution. The real problem is making lots of interesting content for people to watch. If you can solve that problem then a simple system of categories is perfectly sufficient for people to discover content. But that’s not a technical problem, and all those engineers have to be given something to do.
Re: Real World Recommendation System
#7With all of this technology applied, I am still disappointed by Netflix's recommendations – to the point of just giving up and doing something else.
Rotten Tomatoes works fine as a recommendation system. It lists all of the new content coming out in a given week. I just read that every week, file down to what looks interesting based on the premise, and read a few reviews. I can usually tell pretty easily what I'll like. No need for in-app recommendations from any specific streaming service at all. Good old-fashioned human expert curators.
Re: Real World Recommendation System
#8With all of this technology applied, I am still disappointed by Netflix's recommendations – to the point of just giving up and doing something else.
This indicates that the problem is difficult to solve at scale and customized per person. Maybe the issue is with our expectations - I find other people are pretty bad at recommending things for me as well.
Re: Real World Recommendation System
#9Gentle reminder to anyone reading this that your problems are probably not FAANG problems. If you architect your system trying to solve problems you don't have, you are gonna have a bad time.
"And note that you don’t even have to be at FAANG scale to run into this problem - even if you have a small inventory (say few thousand items) and a few dozen features, you’d still run into this problem. "
-TFA
Re: Real World Recommendation System
#10Isn't this obvious list-building promotion for a company (Fennel) that sells recommendation systems?
"Fennel AI: Building and deploying real world recommendation systems in production Launched 18 hours ago"
Caveat reader.