> 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.
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.