Why a Multi-Tiered Approach Solves the Biggest Problems with Entertainment Metadata

Streaming content changes daily. Libraries move, new shows are released, licensing arrangements change.

Ensuring that video content is accurately matched across platforms is essential for providing users with a seamless viewing experience. 

For video streaming platforms, content providers and consumer tech companies, the quality and currency of entertainment (i.e., TV shows & episodes and movies) metadata and content availability are mission-critical.

Your service is only as good as its ability to accurately tell users what to watch and where to find it.

In a world of ever-expanding content and shifting availability, the basic matching algorithms of yesterday simply don’t cut it.

At Reelgood, we understand that achieving a flawless, real-time connection between a platform’s raw data and a global content master list requires more than a single, one-size-fits-all approach. It demands sophistication, speed, and precision. 

That’s why we’ve engineered a comprehensive, multi-tiered content matching system—a system designed to solve the toughest challenges in metadata aggregation.

The Problem with Legacy and Competitive Matching

Imagine the complexity of tracking millions of movies, shows, and episodes across hundreds of global streaming services, all refreshing their catalogs daily.

When a new piece of content appears on a particular service, how does your system definitively know which specific “unique” title it is?

Relying on simple or legacy matching techniques—like exact title and year, or even basic fuzzy logic—leads to two critical failures:

1. Slowness and Scale Issues: Manually matching a large catalog, even a single streaming service like Amazon’s with millions of data points, would take weeks. This bottleneck is simply unsustainable when your goal is to provide daily, near real-time availability updates to your users.Time is money, and slow matching equals outdated data and poor user experience – let alone the number of errors generated from manual human processes.
2. Inaccurate, Incomplete Matches (False Positives/Negatives): Simple matching fails when content titles have minor variations, different localizations, or missing information.

   For example, matching “Harry Potter and the Sorcerer’s Stone” to its U.K. title, “Harry Potter and the Philosopher’s Stone,” is impossible with exact matching. When simple algorithms try to force a match, they either fail (a false negative) or, worse, connect content incorrectly (a false positive), delivering bad information to the end user viewer.

Many solutions often lean too heavily on manual work (which is slow and costly) or less-sophisticated rules-based systems that can’t handle the global complexity of content metadata.

Add in different languages and regional differences, and it’s easy to understand why most legacy systems fail in delivering the accuracy needed for any system

Reelgood’s Four-Tiered Matching Funnel

That’s why any single approach is insufficient, from a speed and accuracy perspective.  And why your legacy provider’s approach is likely failing you – and your customers!

Reelgood’s solution isn’t a single algorithm; it’s a strategically designed four-tiered matching funnel that processes millions of daily data points with unparalleled speed and accuracy. 

This system ensures that the vast majority of content matches are handled automatically and efficiently, reserving human expertise for the truly unique anomalies – ensuring an unparalleled level of accuracy, all day, every day.

Video content data flows through Reelgood’s system in the following strategic order:

1. Automatic Match (The Speed Filter)

This tier handles the most straightforward cases—those instances where a piece of content identified from a streaming service has a near-perfect match with one of our uniquely identified items based on core metadata like title, year, and key industry standard IDs.

• Benefit: Extremely fast and efficient, clearing the vast majority of new content and allowing us to process millions of data points across over 300 global and regional streaming services in a matter of hours, a process that would take weeks manually. This speed is only possible due to the quality and coverage our content being continually curated and refined, ensuring the most accurate dataset in the industry. 

2. Machine Learning (ML) Match (The Precision Engine)

This is where our proprietary expertise truly shines. Content that doesn’t meet the high bar of the Automatic Match is routed to our proprietary NLP (natural language processing) encoder model.

This model, which we’ve rigorously fine-tuned over years on millions of data points, uses advanced tokenization to build embeddings.

• How it works: Instead of just comparing character strings, the model analyzes entire content descriptions and other metadata to understand the context and meaning behind the words.
  
  This allows it to identify matches even when data is sparse, metadata differ slightly, titles are localized (e.g., Sorcerer’s Stone vs. Philosopher’s Stone), or the content is multi-language. For instance, our proprietary ML algorithms can find a match with over 95% confidence where basic fuzzy logic might only score around 70% due to minor variations.This tier is crucial for ensuring that nuanced differences in content are accurately captured.
  
• Benefit: Unmatched precision and sophistication. It allows us to match complex, high-variability global content that would stump less advanced algorithms, all while maintaining a highly conservative match similarity threshold to minimize false positives.
  

  Is this the same content?

   

3. Bulk Match (Rules-Based Sophistication)

This on-demand tier uses a massive and detailed set of rules, taking into account an even broader spectrum of metadata to resolve difficult matches that remain after the ML tier. It’s typically run periodically to process accumulated complex cases.

4. Data Entry Match (The Human Touch)

Only content that defeats all three automated and semi-automated layers is routed to a human team in a “holding” queue.

This typically occurs in edge cases like when key content data is missing—for example, if a content title is only “Harry Potter” with no other identifying information.

• Benefit: Even the most perfect algorithm can’t match content with critically missing information. This final manual step guarantees maximum coverage and accuracy, and utilizes human intuition only where absolutely necessary.
  
  And even better – as we identify and handle edge cases like this that the first three steps don’t handle, we then add the logic into our algorithms so we can handle them automatically in the future.This way, we’re continually using less and less human data review, and by using our human review to train our systems and models, the automated approaches become more and more accurate – and faster.

The Reelgood Advantage: Speed, Precision, and Completeness

What does this multi-tiered approach mean for streaming services and consumer tech companies?

⏱️ Timeliness: From Weeks to Hours

Reelgood’s technology can process a catalog of over a million data points in just a few hours using the power of our multi-tiered approach driven by our own ML models.

This efficiency allows us to provide a near real-time refresh rate of content availability across hundreds of services. If Ted Lasso moves from Apple TV to Netflix, your users will know instantly.

Precision: Trust in Your Data

We solve the hard problems—like varying international titles or limited metadata—with a highly-accurate ML model, significantly reducing the false positives that plague simple matching systems found in most other systems.

We offer an unparalleled level of accuracy, which is non-negotiable for maintaining a positive user experience.

Completeness: Solving the Global Metadata Challenge

Our system leverages the completeness of the metadata we collect and store. Because we have extensive data fields (genre, cast, crew, director, language, etc.) for every master item (available and even those in production), we can match raw data inputs, even if they only contain a few identifying elements.

In addition, our exhaustive dataset and rich fields can complement any missing data from your own data to ensure no gaps or errors.

Our superior data quality and vast training data set are the foundational components that make our ML model so highly effective.

Furthermore, our constant focus on international content and filtering out “extraneous” content (like local news broadcasts) ensures that our master item dataset is cleaner and more relevant than large, undifferentiated databases.

In the world of entertainment metadata, efficiency, coverage, and accuracy are paramount. Reelgood’s multi-tiered matching system delivers all three, giving you the solid data foundation you need to power your consumer applications and drive engagement.

Take The Next Step

If the challenges of content matching, metadata accuracy, and real-time availability resonate with your platform’s needs, it’s time to experience the Reelgood difference. Don’t let outdated systems compromise your user experience or operational efficiency.

Contact us today to learn more about how Reelgood’s multi-tiered content matching system can transform your entertainment metadata strategy.