Movie recommendation datasets: choose the right training data

Public datasets such as MovieLens and Kion are useful for algorithm experiments. PeerLogix addresses the next product question: how to give your recommender viewing context beyond a single service or a new app's sparse history. This guide covers the data choices, the role of each input and a test that measures what the added history contributes.

Build on the PeerLogix viewing foundation

PeerLogix has studied streaming behavior since 2014. Its historical collection spans more than a billion viewing observations across titles and services. A model can learn audience relationships from that foundation before your own product has accumulated years of interactions. This is especially useful for candidate generation: finding a relevant set of titles for the personal ranking stage.

ReelRecs licenses the entire historical collection for model training. It combines that foundation with each user's ratings to produce personal recommendations and Rec Scores. The application shows how a new product can start with established viewing patterns while learning the individual viewer's taste.

Shared audiences reveal a different relationship from matching plot descriptions. A drama and a comedy may attract many of the same households even when their genres differ. PeerLogix's learned title-similarity example makes that kind of candidate relationship inspectable. The model score has its own scale; measured audience overlap has a separate definition.

Use shared title identifiers to bring those relationships into your model, then apply personal preferences and catalog availability. This workflow does not require matching your individual users to households in an external dataset. Scope the required titles, countries, history and training rights with PeerLogix, then run the matched model test below to measure the contribution to your product.

Decide what the recommendation must accomplish

“Recommend a movie” can mean predict a rating, rank the next ten choices, retrieve related titles or help someone choose from the services they can use tonight. Write the product task before choosing a dataset. A model that predicts ratings well may still return an unhelpful list of unavailable films.

Define the target audience and catalog. A household interaction source can help learn title relationships, while the final recommendation may serve one person. Your product still needs a way to learn that person's preferences. Likewise, a film-only benchmark does not by itself validate recommendations for episodic television.

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Use public benchmarks for the questions they answer

MovieLens for explicit-rating experiments

MovieLens 32M supplies a stable release of 32 million ratings. Its README documents ratings, tags, title links, timestamps and users selected with at least 20 ratings. It is a useful reproducible benchmark for explicit-preference models. A commercial product should also test sparse new-user histories and the catalog its users can watch. Commercial or revenue-bearing use requires permission; the University of Minnesota describes a commercial license route.

Kion for implicit-feedback experiments

The Kion dataset contains historical interactions from the MTS Kion service, with user and item attributes. Its field definitions describe the last viewing date and accumulated duration for a user-content relationship. Those fields suit recency and duration features; they do not reconstruct every viewing session. The repository describes anonymization and noise. Its README and original paper give different end years, so use the dates in the exact release for your test. Confirm commercial permission with the owner.

Both benchmarks help test methods. For a product that needs broader viewing relationships across services, bring the catalog and model task to PeerLogix. The commercial evaluation can then focus on the historical coverage and permitted use your product needs.

First-party data: the test closest to your product

Your own product can record what a user was shown, what they selected, what they saved, how they rated it and whether they returned. These events help define the experience you want to improve. Instrument the relevant steps consistently and collect only what your product is permitted to use.

Exposure matters. If the interface repeatedly shows the same popular films, the interaction log partly records your previous ranking decisions. A system trained on that log may reinforce them. Keep the recommendation shown, position and eligible catalog where appropriate so evaluation can distinguish opportunity from preference.

First-party data also has a cold-start limit. A new user or new product has little history. Metadata and permitted external behavior can supply a broader starting point, while onboarding ratings or choices establish personal preference. The useful question is what the extra source adds after those simple options are already working.

Metadata: useful context for new and unfamiliar titles

Metadata lets a model reason about a title before it has many interactions. Genres, language, cast and synopsis can help retrieve candidates, explain a suggestion or filter to a user's needs. These attributes describe content; a similar description does not establish a shared audience or guarantee that the user will enjoy it.

TMDB is one source of movie and television metadata. Its API FAQ distinguishes developer and commercial use and explains attribution requirements. Confirm the applicable data and artwork rights for the product. Access to descriptive text, images and user interactions should not be treated as one interchangeable permission.

Keep a stable title-matching layer between sources. Distinguish a film from its remake and a series from an individual episode. Check ambiguous matches rather than joining on title text alone. An excellent algorithm trained on incorrect joins can learn relationships that do not exist.

Build an evaluation that resembles a real recommendation

Use a chronological split for a next-choice task: training precedes validation, and a final future period remains untouched until model choices are settled. Freeze the metadata and availability assumptions that would have been known at recommendation time. Later popularity, future catalog entries and revised outcomes can otherwise leak information into the test.

For television, decide whether the output is the next series, season or episode. Preserve those identifiers and evaluate at the same level. Predicting the next episode of a series someone already watches is a different task from finding their next series. Keep unreleased episodes and future season information outside the candidate set and training features.

Choose measures that match the output. Recall at ten asks how much of the relevant set appears in ten recommendations. A rank-sensitive measure rewards putting useful choices higher. Rating error answers a different question. Report the candidate pool, handling of unseen items and sampling method so the scores can be understood.

Break results down by cohort. Check new users, established users, smaller titles, relevant languages and target markets. Include catalog diversity and repeated exposure where they matter to the product. An average gain can hide a poor experience for the people you intend to attract.

After an offline improvement, run an appropriate product test with guardrails for user satisfaction and unwanted repetition. Clicks alone can reward sensational choices that users later abandon. Connect the evaluation to the action your product helps people complete.

What to confirm before buying training data

Confirm training rights, derived-feature and model rights, retention, redistribution and end-of-license obligations. Check the entity coverage and the refresh policy. Ask whether an evaluation can include enough of your target catalog to test the proposed benefit without exposing unnecessary records.

Use the streaming data evaluation guide to define the sample, mapping checks and acceptance record. Start with PeerLogix for the viewing foundation, then use your own product evidence to test and refine the personal experience. Send your target catalog, markets and model task to begin a training-data evaluation.