this post was submitted on 20 Mar 2026
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I built a local AI movie recommender for Radarr using Ollama (lemmy.world)
submitted 1 day ago by nikodindon@lemmy.world to c/selfhosted@lemmy.world
65 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 4 points 14 hours 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

[–] pfr@piefed.social 3 points 16 hours ago (1 children)

Anti-AI evangelism is at its peak rn.

[–] Andres4NY@social.ridetrans.it 2 points 10 hours ago* (last edited 8 hours ago) (1 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.

[–] meldrik@lemmy.wtf 1 points 10 hours ago (1 children)

Like what and what have you tried to block it?

[–] Andres4NY@social.ridetrans.it 1 points 10 hours 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 10 hours 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?

[–] meldrik@lemmy.wtf 6 points 19 hours 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.

[–] fubarx@lemmy.world 4 points 18 hours ago

The more local inference, the better. Nice work!

[–] rako@tarte.nuage-libre.fr 1 points 15 hours ago

ai;dr

[–] circuscritic@lemmy.ca 23 points 1 day ago* (last edited 1 day ago) (19 children)

Since no one is leaving critical comments that might explain all 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 9 points 15 hours 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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[–] Scrath@lemmy.dbzer0.com 7 points 1 day ago (2 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

[–] four@lemmy.zip 3 points 1 day ago (1 children)

I'm not an expert, but LLMs should still be deterministic. If you run the model with 0 creativity (or whatever the randomness setting is called) and provide exactly the same input, it should provide the same output. That's not how it's usually configured, but it should be possible. Now, if you change the input at all (change order of movies, misspell a title, etc) then the output can change in an unpredictable way

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[–] LiveLM@lemmy.zip 3 points 1 day 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.

[–] eager_eagle@lemmy.world 9 points 1 day ago

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

[–] Overspark@piefed.social 6 points 1 day ago (2 children)

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

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