[priansh]

The 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.

[faizshah]

Yahoo released Vespa as open source: https://vespa.ai/

It has everything you need at a platform level to build a production recommendation system given that it’s the engine that powered a lot of yahoo product’s search and recommendation capabilities. I have been experimenting with it, the number of capabilities are immense. It’s really an untapped resource.

Take a look at the features: https://vespa.ai/features and the ranking syntax: https://docs.vespa.ai/en/ranking.html

Really cool stuff, I haven’t even scratched the surface of what it can do.

[orasis]

I think we've done a pretty good job on the scoring side with a fast and simple to use API that runs in-process: https://improve.ai