Introducing AI-Powered Related Content for Admiral Extend Journey Extension
Publishers have always looked for better ways to keep readers engaged after they finish an article.
That moment—when a visitor reaches the end of a story, pauses, becomes idle, or prepares to leave—is one of the most valuable opportunities in the reader journey.
Why?
Because every successful journey extension creates the potential for additional pageviews, additional ad impressions, and additional revenue.
For media publishers, the relationship is straightforward: more pageviews create more opportunities to monetize the audience you’ve already earned.
Today, Admiral Extend is getting smarter with the introduction of AI-powered Related Content recommendations.
Using Content Intelligence and Semantic Content Discovery, Admiral can identify contextually relevant content from across a publisher’s site and recommend it through Extend journeys. This puts AI and machine learning to work in a positive direction for publishers.
The result is a more intelligent way to help readers discover their next article while creating additional opportunities to extend engagement, increase pageviews, and grow revenue from existing traffic.
For publishers already using Admiral, adoption is intentionally simple.
If Admiral is already on page, enabling Related Content is as easy as approving the feature. Admiral handles the content analysis, recommendation engine, and ongoing updates automatically, while publishers retain control over how recommendations are configured and delivered.
What’s New in Extend Journey Extension
Extend now supports three complementary recommendation strategies designed to help publishers identify the best next article for every reader journey.
Related Content
Related Content uses semantic understanding and topic relationships to identify content that is contextually relevant to the article a visitor is currently reading.
Best for:
- Matching reader intent
- Extending article journeys
- Deepening engagement
- Discovering relevant archive content
Surging Content
Surging Content identifies content gaining momentum through recent audience activity.
Best for:
- Emerging trends
- Breaking news
- Fresh stories
Popular Content
Popular Content highlights content with proven audience appeal.
Best for:
- Evergreen performers
- Broad-interest content
- Sitewide discovery
These recommendation strategies work together.
Each answers a different version of the same question:
What is the best next article to show this reader right now?
By default, Extend evaluates recommendations in this order:
- Related Content
- Surging Content
- Popular Content
Related Content becomes the preferred first choice because it is designed to provide the most contextually relevant recommendation when a strong semantic match exists.
If not, Extend can automatically fall back to Surging Content and then Popular Content, ensuring visitors always have a valuable next step available.
Why Journey Extension Matters
Every reader session eventually reaches a decision point.
A visitor finishes reading.
They stop scrolling.
They become idle.
They consider leaving.
At that moment, publishers have already done the hard work. They’ve earned the visit, captured attention, and delivered value.
The next challenge is keeping the journey going.
Every additional page viewed can create another opportunity to:
- Increase engagement
- Serve additional ad impressions
- Generate additional revenue
The goal isn’t simply showing another link.
The goal is to present the right link at the right moment. Admiral's Extend module, Interaction Targeting, and Related Content engine work in tandem to grow additional pageviews.
Why Related Content Means Something Different Today
Related content has evolved significantly over the past two decades.
The earliest recommendation systems relied on manual editorial curation. Later systems used categories, tags, and keyword matching to automate the process. Each approach had value, but they often struggled to understand the actual meaning of content.
Modern AI changes that.
Instead of asking whether two articles share similar words, AI can evaluate whether they share similar concepts, topics, and intent.
This is where content intelligence, semantic content discovery, and contextual recommendations come together.
When Related Content is enabled, Admiral analyzes content across a publisher's site, identifies topics and concepts, and transforms articles into semantic representations that can be compared for similarity.
This process is commonly called content vectorization. In simple terms, vectorization turns article content into a mathematical representation of meaning. Those representations can then be stored in a vector database and compared quickly across a publisher's content library.
“Content vectorization turns article content into a mathematical representation of meaning, allowing AI systems to compare content based on concepts, topics, and context rather than simple keyword overlap.”
That matters because two articles may be highly related even when they do not share the same keywords, tags, or categories.
For example, an article about World Cup predictions may be conceptually related to articles about player profiles, team strategy, tournament analysis, or qualification scenarios—even if the headlines and keywords are different.
As new content is published, Admiral can continue updating the content map automatically. The result is a recommendation engine that understands content based on meaning rather than simple text matching.
For publishers, that means visitors are more likely to discover content that aligns with what they are interested in right now—not simply content that happens to share keywords.
How Related Content Works Inside Extend
When a journey extension opportunity occurs, Extend evaluates available recommendation options and looks for the strongest available match.
The system can consider:
- Semantic similarity between articles
- Relevancy thresholds
- Publisher targeting rules
- Include and exclude criteria
- Previously visited content
- Previously surfaced recommendations
If a qualifying Related Content recommendation exists, it can be shown first.
If no strong semantic match is available, Extend can automatically move to Surging Content and then Popular Content.
This approach allows publishers to prioritize contextual relevance while still benefiting from momentum and popularity-based discovery when appropriate.
Importantly, Extend is designed to avoid wasting recommendation opportunities.
If a visitor has already viewed a page, Admiral can exclude that page from consideration and continue looking for the next best recommendation. Likewise, recently surfaced recommendations can be filtered out to help keep content discovery fresh and relevant.
Publishers Stay In Control
One concern publishers often have with AI-powered recommendations is whether they’ll lose control over what gets surfaced to visitors.
Related Content was designed to automate the heavy lifting while still giving publishers meaningful control over how recommendations are selected and displayed.
Include or Exclude Specific Content Areas
Publishers can determine which sections of their site are eligible for Related Content recommendations.
For example:
- Include editorial content
- Exclude sponsored content
- Exclude subscription pages
- Focus recommendations on specific content sections
- Control recommendations through URL path rules
This helps ensure recommendations align with each publisher’s content strategy.
Adjust Recommendation Precision
Not every publisher wants the same level of recommendation precision.
Related Content includes configurable relevancy thresholds that allow publishers to tighten or broaden semantic matching.
A tighter threshold prioritizes highly similar content.
A broader threshold encourages wider content discovery.
Test and Compare Performance
Publishers can configure Related Content alongside Popular Content and Surging Content and compare performance through A/B testing.
This makes it possible to evaluate how contextual recommendations perform against other journey extension strategies.
Measure Results
Performance can be monitored through Extend analytics and reporting, allowing publishers to evaluate engagement, click-through rates, downstream pageviews, and overall journey extension performance.
The Best Part: Publishers Don’t Have To Build Any of This
Many recommendation systems require significant editorial effort, ongoing maintenance, or complex integrations.
Related Content was designed differently.
What publishers do:
- Have Admiral on page
- Enable the feature
- Optionally configure preferences (Admiral's Customer Love team is always available to help recommend and implement preferences)
What Admiral handles:
- Content analysis
- Semantic mapping
- Recommendation generation
- Ongoing updates
- Content exclusions
- Analytics and reporting
In practice, the experience is much closer to turning on a feature than building a recommendation engine.
Benefits for Publishers
Grow Revenue From Existing Traffic
One of the most efficient ways to grow revenue is to create more value from visitors already on site.
When readers continue their journey through relevant recommendations, publishers generate additional pageviews, additional ad impressions, and additional revenue opportunities without acquiring a new visitor.
More Value From Existing Traffic
Traffic acquisition is increasingly competitive.
Related Content helps publishers maximize the value of existing traffic by creating additional opportunities for readers to continue exploring content.
Increased Pages Per Session
Relevant recommendations help readers discover more content aligned with their interests.
For ad-supported publishers, increased pages per session often translates directly into additional ad inventory and revenue opportunities.
Better Reader Journey Extension
Recommendations align more closely with the reader’s current interests and intent, helping create more natural content journeys.
Better Archive Discovery
Strong evergreen content can continue generating value when surfaced in the right context.
Reduced Manual Work
No hand-curated recommendations.
No large-scale tagging projects.
No ongoing recommendation maintenance.
Smarter Use of AI
AI is applied to a practical publisher challenge: helping readers discover their next article while helping publishers grow engagement, pageviews, and revenue.
The Future of Related Content
The future of content recommendations is not simply about showing more links.
It is about understanding reader context and helping visitors discover the best next piece of content at the moment it matters most.
With AI-powered Related Content now available in Admiral Extend, publishers can bring Content Intelligence, Semantic Content Discovery, and Contextual Recommendations into one of the most valuable moments in the reader journey—with minimal setup and no manual curation required.
Interested in enabling Related Content? Reach out to your Admiral representative to learn more about bringing AI-powered journey extension to your site.
