Gradually getting the hang of recommendation algorithms. These are what Netflix or Amazon use to recommend products. It’s useful to understand, since I work as an architect in the e-commerce field.
Look at how LLMs help me — specifically, this diagram was created by DeepSeek from a crude textual description — essentially, a list and my rough reflections on how probably the items should be connected, but I asked not to take it as a command. Well yes, after getting the result, I arranged the boxes a bit more aesthetically, but the connections and grouping were done by DeepSeek, and done better than my textual attempts. It gave me an XML which I imported into Draw IO. Well, I did move some blocks around for aesthetic purposes. ChatGPT o3 initially couldn’t handle it.
Then I sent this diagram several times for validation to ChatGPT o1, and it suggested small tweaks. Thus, ChatGPT reliably understands what’s connected with what on the schematic, and didn’t make a mistake even once.
Just so you know, as of today, I have only really gotten to grips with three from this list — in addition to ItemKNN and UserKNN, which are trivial. Today I was digging into ALS from the Latent Factor Models block of Matrix Factorization. Of course, I’m not planning to delve into each one, but it’s useful to at least understand the blocks and what’s what.
Hi, I'm Rauf Aliev. 