My advice from someone who has built recommendation systems: Now comes the hard part! It seems like a lot of the feedback here is that it's operating pretty heavily like a content based system system, which is fine. But this is where you can probably start evaluating on other metrics like serendipity, novelty, etc. One of the best things I did for recommender systems in production is having different ones for different purposes, then aggregating them together into a final. Have a heavy content-based one to keep people in the rabbit hole. Have a heavy graph based to try and traverse and find new stuff. Have one that is heavily tuned on a specific metric for a specific purpose. Hell, throw in a pure TF-IDF/BM25/Splade based one.
The real trick of rec systems is that people want to be recommnded things differently. Having multiple systems that you can weigh differently per user is one way to be able to achieve that, usually one algorithm can't quite do that effectively.
My advice from someone who has built recommendation systems: Now comes the hard part! It seems like a lot of the feedback here is that it's operating pretty heavily like a content based system system, which is fine. But this is where you can probably start evaluating on other metrics like serendipity, novelty, etc. One of the best things I did for recommender systems in production is having different ones for different purposes, then aggregating them together into a final. Have a heavy content-based one to keep people in the rabbit hole. Have a heavy graph based to try and traverse and find new stuff. Have one that is heavily tuned on a specific metric for a specific purpose. Hell, throw in a pure TF-IDF/BM25/Splade based one.
The real trick of rec systems is that people want to be recommnded things differently. Having multiple systems that you can weigh differently per user is one way to be able to achieve that, usually one algorithm can't quite do that effectively.