[jzombie]

> Likes/dislikes are stored in local storage and compared against all stories using cosine similarity to find the most relevant stories.

You're referring to using the embeddings for cosine similarity?

I am doing something similar with stocks. Taking several decades worth of 10-Q statements for a majority of stocks and weighted ETF holdings and using an autoencoder to generate embeddings that I run cosine and euclidean algorithms on via Rust WASM.

[mahin]

Yes. Your project sounds cool, post it!

[jzombie]

I just responded to an adjacent query with the info.

https://news.ycombinator.com/threads?id=jzombie#42072665

[tiborsaas]

> I am doing something similar with stocks.

How well does it work?

[jzombie]

It seems to do well for a lot of searches, though some are questionable, but I believe that I know why. I'm training some different autoencoders to give it some different perspectives.

The code lives here: https://github.com/jzombie/etf-matcher

The ad-hoc vector DB I've created lives here: https://github.com/jzombie/etf-matcher/blob/main/rust/src/da...