# Viewing Data for Recommendation Engines | PeerLogix Canonical: https://peerlogix.com/solutions/recommendation-engines/ Static reference built: 2026-10-28. Linked data sources may be newer. Train movie and TV recommendation models with PeerLogix cross-service viewing history. More than a billion observations across 10+ years, licensed for your workflow. 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/ [Solutions](https://peerlogix.com/solutions/index.html) / Recommendation engines For recommendation, discovery and machine-learning teams # Give your model a wider view of what people watch. A new recommendation product can begin with years of viewing context. PeerLogix licenses household-level streaming history to help models learn relationships between titles across services, then combine that foundation with your own users’ preferences. [Discuss training data ↗](https://peerlogix.com/contact/index.html?interest=data) [See how ReelRecs uses it ↗](https://peerlogix.com/customers/reelrecs/) A historical foundation for discovery ## More than a billion viewing observations. PeerLogix has recorded streaming viewing over more than a decade, with coverage spanning major services and markets worldwide. That history connects titles through actual household viewing activity, beyond the behavior a new app has collected itself. Viewing observations recorded 1B+ Years of viewing history 10+ Households observed over time 70M+ Titles in the catalog 200K+ Netflix Disney+ Apple TV HBO Max Paramount+ Peacock Coverage includes Netflix, Disney+, Apple TV, HBO Max, Paramount+, Peacock and other major streaming services. These names describe viewing coverage; they do not imply direct platform partnerships or endorsements. Figures describe PeerLogix’s historical collection, not a current daily panel. What this adds to your model ## Learn which titles connect through their audiences. Two shows can attract overlapping households even when their plots and genres differ. Those patterns give a recommendation model another way to find promising candidates. Your product then uses the individual’s ratings, context and catalog to decide what to recommend. 01 / OBSERVE ### Viewing across services Title and episode activity, household context, and viewing over time. A broader starting point 02 / LEARN ### Relationships between titles Shared audiences and viewing patterns help a model connect films and shows. Candidates worth considering 03 / PERSONALIZE ### A ranking for your user Add explicit ratings, available titles and your product’s recommendation goals. Personal relevance Conceptual data-to-recommendation workflow. Shared viewing describes an audience relationship; story or genre similarity is a separate signal. The product opportunity ## Make the first ratings count. Your first users bring their own tastes. A model trained with historical viewing patterns can use those early preferences to explore a much larger set of title relationships. For an established product, cross-service history adds context beyond its own catalog. It can help surface candidates from another service or connect a familiar favorite with a less obvious choice. Customer application / ReelRecs ### A viewing foundation before the first signup. PeerLogix licenses its entire historical viewing collection to ReelRecs for model training. ReelRecs combines that foundation with each user’s explicit ratings to generate personal movie and TV recommendations and Rec Scores. [Read the licensed relationship ↗](https://peerlogix.com/customers/reelrecs/) Build and evaluate ## Fit the history to your recommendation task. Start with your catalog, target markets and the decisions your model must make. We’ll work through the relevant history, title matching, fields, permitted training use and delivery. ### Keep the evidence precise. Viewing observations record activity. They do not say how much a particular person liked a title. Individual ratings add that explicit preference signal. The licensed collection and the data selected for a particular training run can differ. Evaluate the finished model on the audiences and tasks it serves; collection size alone does not establish recommendation accuracy. [Read the data methodology ↗](https://peerlogix.com/methodology/) Bring your recommendation problem ## Build on viewing history. Tell us about your catalog, audience and model. We’ll scope the data that supports your work. [Talk about a data license ↗](https://peerlogix.com/contact/index.html?interest=data)