[polote]

I'm exactly working on that too, and don't have the answer. The problem is we all have our way to classify things and this is never the same way. The same word also never mean the same things for each one of us.

Two aspects I'm trying currently are (that need users browsing history):

- Dont try to recommend similar website, but recommend users that like similar things as you, and you can list the website that this user likes

- Create tags with accuracy. For example you will tag a website "product management" "startup" and "b2b". You can go one step further and ask users to rate how this tag matches the website. Like 90% for "b2b" and 50% for "startup" and 20% "product management". Then you can let users search tags and their accuracy (I want "product management" at average more than 50%)

Like you I feel like something can be done with LLM but I just haven't found it yet, maybe to suggest the tags of a website from a restricted list of tags, and then to suggest tags from an explanation of what the user is searching and then search those tags