The local LLM here is, if I'm not mistaken @nikodindon@lemmy.world , just used as a feature extraction tool. It's not like asking ChatGPT what to watch next but rather asking it to sumarise the movie as an excel file, that you then process to compute which movie(s) is(are) similar.
Exactly! This has been done plenty of times in the past (there's a reason why some movies datasets are used as toy example for data analysis).
For the unfamiliar with the field, the LLM part here is simply that, instead of building a feature space from predefined tags or variables, it makes a "fuzzier" feature space where it embeds the movies based on the text tokens the model sees.
In essence, the way to compute which movie to recommend is the same (a.k.a no LLM) it is just that the data used for the computation is generated differently.
The local LLM here is, if I'm not mistaken @nikodindon@lemmy.world , just used as a feature extraction tool. It's not like asking ChatGPT what to watch next but rather asking it to sumarise the movie as an excel file, that you then process to compute which movie(s) is(are) similar.
Exactly! This has been done plenty of times in the past (there's a reason why some movies datasets are used as toy example for data analysis). For the unfamiliar with the field, the LLM part here is simply that, instead of building a feature space from predefined tags or variables, it makes a "fuzzier" feature space where it embeds the movies based on the text tokens the model sees. In essence, the way to compute which movie to recommend is the same (a.k.a no LLM) it is just that the data used for the computation is generated differently.
Thank you for building, maintaining, and improving this wonderful platform!
This is very useful, thanks for sharing it!
Second !interstellar@kbin.earth ! I mostly use it due to it's support for other protocols beyond Lemmy, like Mbin
E: Added link
Same here, can confirm this worked on my intel 11th FRMRK 13