How to get Netflix-style recommendations on Plex

Short answer: the thing that makes Netflix feel personal isn’t the algorithm — it’s that every profile gets different rows, each with a reason attached, refreshed constantly. Plex can reproduce all three, but none of them out of the box.

This page breaks “make it like Netflix” into the properties people actually mean, and says which are achievable on a Plex server and how.

What people mean by “like Netflix”

Ask for Netflix-style rows and you’re usually asking for four separate things:

1. Different rows for different people. Your profile and your partner’s profile share a library and show completely different things.
2. Rows with a stated reason. “Because you watched Arrival”, “Top picks for Sam”. The label is half the value — it makes the row feel chosen rather than random.
3. Rows that change. Not the same twelve posters every time you open the app.
4. Rows built from things you can actually watch right now. Not a wishlist.

Plex gives you exactly one of these for free: number four, trivially, because it’s your library. The rest is work.

What Plex gives you, honestly

Recommended shelves are library-wide. Genre rows, recently added, unwatched — everyone with access to that library sees the same rows containing the same titles in the same order, whatever they’ve watched. This is the single biggest gap between Plex and Netflix and there is no setting that closes it.

Discover is genuinely personalised to your Plex account, but it recommends across streaming services rather than from your library, and it’s account-level rather than something a server admin configures.

Continue Watching is per-user, and only contains what you’ve already started.

Smart collections get you the shape of Netflix rows cheaply. “Highly rated thrillers”, “90s action”, “under 100 minutes” — saved filters that stay current as the library grows, promotable to the Home shelf. Genuinely worth doing, and where most people should start. But the filter runs against the library, not against the viewer: Unplayed means unplayed by the admin account, so everyone sees the same row including the people who’ve watched all of it.

That’s the ceiling of the built-in features. Rows that look right, contents that aren’t personal.

Building the four properties

Different rows for different people

This is the hard one, and it isn’t a recommendation problem — it’s an access-control problem.

Plex evaluates label restrictions per account. Label a collection, then add label!=<that label> to every other account’s share filter, and you’re left with a collection one person can see. It needs Plex Media Server 1.43.2.10687 or newer; before that the restriction was ignored on the Home, Recommended and Related shelves and the row leaked.

Get the ordering wrong and it leaks anyway: create the row unpromoted, label it, merge the exclusions everywhere, then promote. Full mechanism, manual steps and pitfalls on per-user collections.

Rows with a stated reason

Two parts: having a reason, and being able to say it.

Having one falls out of how you pick. Start from a title the person actually watched and finished, find similar titles in your library through TMDB similarity — shared genres, keywords, cast, crew — and the seed is the reason. Ask a language model for recommendations cold and you get plausible titles with no traceable reason and, frequently, films you don’t own.

Saying it is easier than it looks. A Plex collection has a title and a summary, both of which show in the UI, so a row can be called “Because you watched Arrival” with a summary explaining each pick. Naming rows after their seed is most of the Netflix feel for almost no effort.

Rows that change

A recommendation row that shows the same posters for a fortnight becomes invisible. Rebuilding on a schedule isn’t enough on its own — an unchanged watch history run through a deterministic scorer produces an identical row. You need either fresh input (new watches, new library additions) or deliberate variation: rotate which seed drives the row, sample from a larger candidate pool than the row can hold, or weight recent watches more heavily.

Rows from things you can watch now

Verify every pick exists in your library before it goes in a collection, and drop anything the person has already watched. This sounds obvious and it’s the most common failure of the AI-first approach — a model asked “what should Sam watch?” will confidently return titles you don’t own, titles under alternate release names, and occasionally titles that don’t exist.

The fix is ordering: generate candidates from your library, then let the model rank and explain them. Never let it invent.

Where this leaves you

You can get most of the Netflix experience on a Plex server. Rows per person, labelled with why, refreshed nightly, drawn from what you own. What you can’t get is any of it from Plex’s settings — it’s the label mechanism plus a scheduled job, and the scheduled job has to be careful about write ordering or it publishes people’s private rows to the whole server.

Two limits stay put whatever you build: the server owner sees every row (Plex doesn’t filter the admin account — there’s no share to filter), and films and shows need separate rows because label restrictions are tracked per library.

The automated version

Shortlist is a self-hosted container that builds all four properties for every user on your server. Per-person rows from each person’s own watch history, named after the film that inspired them, refreshed on a schedule, every pick verified to exist in your library — and made private through the label mechanism, in the order that doesn’t leak.

No AI key required: the built-in picker runs entirely in code. An optional provider (Claude, GPT, Gemini, or a local model through Ollama, llama.cpp, LM Studio, vLLM or LocalAI) adds a ranking and explanation pass over candidates already drawn from your library.

docker run -d --name shortlist -p 5959:5959 \
  -v /path/to/config:/config \
  stevezzau/shortlist:latest

Set -e SHORTLIST_DRY_RUN=1 to preview every change without writing one.

Related

• How to improve Plex recommendations — the settings to change first
• AI recommendations for Plex — where a model helps, and where it invents films
• Recommendations from watch history — where picks come from
• A different home screen per user — the surfaces rows land on
• Per-user collections — the privacy mechanism in full
• Plex recommendation tools compared — the other projects in this space