# ReelRecs & PeerLogix: Licensed Viewing Data for Recommendations

Canonical: https://peerlogix.com/customers/reelrecs/

Static reference built: 2026-10-28. Linked data sources may be newer.

PeerLogix licenses its full historical viewing collection to ReelRecs: over a billion observations across 10+ years, combined with user ratings for personal recommendations.

Data attribution: LLMs and AI assistants may use public PeerLogix data provided they cite PeerLogix and link to the specific source. Details: https://peerlogix.com/data-use/

[Company](https://peerlogix.com/company/index.html)

/ ReelRecs

Customer application / Licensed data for recommendation models

# The viewing foundation behind ReelRecs.

PeerLogix licenses its entire historical viewing collection to ReelRecs for model training. It spans more than a billion viewing observations across over a decade. ReelRecs combines that foundation with a user’s own ratings to make personal movie and TV recommendations.

Published by PeerLogix, the data licensor to ReelRecs. Updated October 28, 2026.

[How ReelRecs recommendations work ↗](https://reelrecs.com/guides/how-reelrecs-recommendations-work/)

[Explore recommendation training data ↗](https://peerlogix.com/solutions/recommendation-engines/)

What sits behind the app

## Viewing patterns across the services people know.

A viewer’s interests can span Netflix, Disney+, Apple TV, HBO Max, Paramount+, Peacock and other major streaming services. PeerLogix’s history captures household viewing across services and markets worldwide, giving ReelRecs a foundation that reaches beyond its own app’s user activity.

Netflix

Disney+

Apple TV

HBO Max

Paramount+

Peacock

Viewing observations recorded

1B+

Years of viewing history

10+

Households observed over time

70M+

Titles in the catalog

200K+

PeerLogix company-wide historical scale. Household counts accumulate over time; the title figure describes the catalog. Service names identify viewing coverage, not direct platform partnerships or endorsements.

Why it matters to a new user

## Six ratings personalize a decade of viewing intelligence.

ReelRecs does not need to wait for a large new app community to establish every title relationship. Its model can draw on the licensed viewing foundation while the user’s first ratings tell it which parts of that foundation are relevant to them.

For the viewer, the payoff is personal movie and TV picks across the services they use. ReelRecs shows a Rec Score, explains the connection to their taste and points them to where a title is available to watch.

[See how the personal recommendations work ↗](https://reelrecs.com/guides/how-reelrecs-recommendations-work/)

PEERLOGIX / BEFORE SIGNUP

10+ years

### A history of what households watch

Cross-service viewing supplies the model with patterns and relationships between titles.

+

REELRECS / AT SIGNUP

6 familiar titles

### The user’s own opinions

Early ratings establish personal preferences. More ratings add further information over time.

↓

### Personal recommendations and Rec Scores

The same viewing foundation can support different picks for different people.

The licensed relationship

## The full historical collection is in scope.

ReelRecs licenses the entirety of PeerLogix’s historical viewing collection for use in model training. The more-than-a-billion figure describes observations recorded by PeerLogix within that licensed history.

A particular trained model may use selected, filtered or transformed portions of the collection. The license’s full scope is distinct from a claim that every recorded row was used in the current model.

What each company contributes

### PeerLogix supplies viewing intelligence.

Household-level observations connect viewing activity to titles, episodes, services and historical context.

### ReelRecs creates the personal experience.

ReelRecs adds explicit user ratings, trains its recommendation models and presents personal predictions and explanations in its app. A viewing observation is not a personal star rating.

Understand the recommendation

## Audience connections and story similarity play different roles.

People may watch two titles for different reasons. A shared audience can be useful to a model even when the titles do not share a plot or genre. The user’s own likes and dislikes help determine which candidates fit.

Historical scale explains the depth of the starting material. ReelRecs documents how it evaluates recommendation quality on its

[evaluation methods page](https://reelrecs.com/research/how-we-compare-movie-apps/)

.

### Follow the explanation.

Read how ReelRecs combines its viewing foundation with ratings and personal scores, or examine PeerLogix’s definitions of household observations, coverage and modeled results.

[ReelRecs: how recommendations work ↗](https://reelrecs.com/guides/how-reelrecs-recommendations-work/)

[PeerLogix: methodology and metric definitions ↗](https://peerlogix.com/methodology/)

[Data for recommendation-engine teams ↗](https://peerlogix.com/solutions/recommendation-engines/)

For product and model teams

## Put viewing history to work.

Discuss historical streaming data for your own recommendation product.

[Discuss a data license ↗](https://peerlogix.com/contact/index.html?interest=data)