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I built a local AI movie recommender for Radarr using Ollama (lemmy.world)
submitted 2 days ago by nikodindon@lemmy.world to c/selfhosted@lemmy.world
67 comments fedilink hide all child comments
 

I Built a Python script that uses a local Ollama LLM to automatically find and add movies to Radarr.

It picks random films from your library, asks Ollama for similar suggestions based on theme and atmosphere, validates against OMDb, scores with plot embeddings, then adds the top results to Radarr automatically.

Examples:

  • Whiplash → La La Land, Birdman, All That Jazz
  • The Thing → In the Mouth of Madness, It Follows, The Descent
  • In Bruges → Seven Psychopaths, Dead Man's Shoes

Features:

  • 100% local, no external AI API
  • --auto mode for daily cron/Task Scheduler
  • --genre "Horror" for themed movie nights
  • Persistent blacklist, configurable quality profile
  • Works on Windows, Linux, Mac

GitHub: https://github.com/nikodindon/radarr-movie-recommender

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[–] timestatic@feddit.org 8 points 1 day ago

Sorry OP that you're getting downvote bombed. This is actually really neat. People go nuts when they hear AI but this is fully local so I think this reaction is unjust. This has nothing to do with ram prices since that stems from data centers or corpos pushing AI on you. Thank you for sharing

[–] meldrik@lemmy.wtf 11 points 1 day ago

This is a cool tool. Thanks for sharing. Don’t worry about the downvotes. The Fediverse has a few anti-AI zealots who love to brigade.

[–] pfr@piefed.social 6 points 1 day ago (1 children)

Anti-AI evangelism is at its peak rn.

[–] Andres4NY@social.ridetrans.it 2 points 1 day ago* (last edited 1 day ago) (2 children)

@pfr @nikodindon That assumes it won't get worse, which I hope it does. AI companies have forced me to take down web stuff that I had running for almost 2 decades, because their scrapers are so aggressive.

[–] halloween_spookster@lemmy.world 1 points 1 day ago

20 decades

Found the time traveler!

[–] meldrik@lemmy.wtf 1 points 1 day ago (1 children)

Like what and what have you tried to block it?

[–] Andres4NY@social.ridetrans.it 1 points 1 day ago (1 children)

@meldrik They're impossible to block based on IP ranges alone. It's why all the FOSS git forges and bug trackers have started using stuff like anubis. But yes, I initially tried to block them (this was before anubis existed).

It was a few things that I had to take down; a gitweb instance with some of my own repos, for example. And a personal photo gallery. The scrapers would do pathological things like running repeated search queries for random email addresses or strings.

[–] meldrik@lemmy.wtf 1 points 1 day ago

I’m hosting several things, including Lemmy and PeerTube. I haven’t really been aware of any scrapers, but do you know of any software that can help block it?

[–] circuscritic@lemmy.ca 45 points 2 days ago* (last edited 2 days ago) (19 children)

Since no one is leaving critical comments that might explain all the downvotes, I'm going to assume they're reflexively anti-AI, which frankly, is a position that I'm sympathetic to.

But one of the benign useful things I already use AI for, is giving it criterias for shows and asking it to generate lists.

So I think your project is pretty neat and well within the scope of actually useful things that AI models, especially local ones, can provide the users.

[–] webkitten@piefed.social 18 points 1 day ago

Seriously; local AI use is what everyone should strive for not only for privacy but because it's better than using a large data centre and the power use for Ollama is negligible.

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[–] fubarx@lemmy.world 6 points 1 day ago

The more local inference, the better. Nice work!

[–] eager_eagle@lemmy.world 19 points 2 days ago

that's pretty cool, this is just the wrong crowd, don't worry about the downvotes

[–] Scrath@lemmy.dbzer0.com 8 points 2 days ago (3 children)

I remember building something vaguely related in a university course on AI before ChatGPT was released and the whole LLM thing hadn't taken off.

The user had the option to enter a couple movies (so long as they were present in the weird semantic database thing our professor told us to use) and we calculated a similarity matrix between them and all other movies in the database based on their tags and by putting the description through a natural language processing pipeline.

The result was the user getting a couple surprisingly accurate recommendations.

Considering we had to calculate this similarity score for every movie in the database it was obviously not very efficient but I wonder how it would scale up against current LLM models, both in terms of accuracy and energy efficiency.

One issue, if you want to call it that, is that our approach was deterministic. Enter the same movies, get the same results. I don't think an LLM is as predictable for that

[–] LiveLM@lemmy.zip 4 points 2 days ago

One issue, if you want to call it that, is that our approach was deterministic. Enter the same movies, get the same results. I don't think an LLM is as predictable for that

Maybe lowering the temperature will help with this?
Besides, a tinge of randomness could even be considered a fun feature.

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[–] Overspark@piefed.social 7 points 2 days ago (1 children)

A recommendation for Moonrise Kingdom based on Mickey 17? The genres might match, but those are totally different movies.

[–] Janx@piefed.social 5 points 2 days ago (1 children)

Also, A Bug's Life from Mickey 17!?

[–] borari@lemmy.dbzer0.com 2 points 1 day ago

That made me lol so hard. Like what’s the fucking point of this thing when it comes up with shit like that?

[–] RIotingPacifist@lemmy.world 4 points 2 days ago (1 children)

How does this compare to an ML approach?

are you training or just using an LLM for this?

[–] eager_eagle@lemmy.world 7 points 2 days ago* (last edited 2 days ago) (1 children)

There's no training, the LLM embeddings are used to compare the plots via a cosine similarity, then a simple weighted score with other data sources is used to rank the candidates. There's no training, evaluation, or ground-truth, it's just a simple tool to start using.

[–] FerCR@kbin.earth 1 points 1 day ago

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.

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