20-minute walkthrough with 2–3 personalized examples on your real pages.
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A publisher’s guide to AI-driven content feeds — set up recommendations, track reader engagement, and deploy algorithms that boost recirculation and time on site.
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From the Dashboard, select the "Content Recommendation" wizard. The 4 main steps are: Catalog → Tracking → Algorithms → Design & Targeting.
Upload your content (XML/RSS/API) and map attributes (Title, Image, Date, Author). Tip: Use the "AI Auto-Interest" feature to scan your site for keywords if you lack tags.
Track engagement to build user profiles. Choose one method:
Use the "Visual Selector" to click elements on your page (e.g., Author Name, Category Tag) to grab data without coding.
<script>(self.personyze=self.personyze||[]).push(['Article Viewed', "ARTICLE_ID"]);</script>
<script>(self.personyze=self.personyze||[]).push(['Article Liked', "ARTICLE_ID"]);</script>
<script>(self.personyze=self.personyze||[]).push(['Article Commented', "ARTICLE_ID", 'Quantity', QUANTITY]);</script>
Choose the logic that drives the recommendations (e.g., "Most Popular", "Collaborative Filtering"). See the recipes below for examples.
Refine what content is shown using the "Final Touches" step.
Strictly exclude content types, such as "Exclude all Commented" or "Exclude showing on current page" to avoid redundancy.
Control where and how the recommendations appear.
Select a template (Grid, List, Slider) or customize the HTML/CSS. If you select JSON Feed, this step is skipped as you only configure the data output.
Use the Simulator to click a "Placeholder" on your live site. Alternatively, set the widget to appear as a Popup or slide-in. Note: You can add Multiple Widgets to a single page (e.g., "Trending" at top, "Personalized" at bottom).
Add overlays like "Most Read", "New", or "Popular this Week" to increase CTR.
Engage readers immediately with relevant content.
The gold standard. Displays articles matching the visitor's reading history and interests (e.g., "Politics" + "Europe").
Show what's trending across the site. Good for new visitors with no history.
A powerful retention tool. "Welcome back! Here are 5 new articles posted since you were here yesterday." Highly effective for news sites.
Keep readers on site after they finish an article.
Collaborative Filtering: "People who read this article also read X." Great for discovering related deep-dives.
Recommend articles from the Current Category or with the same Tags. Keeps the user in their current flow/topic.
Allows editors to manually curate specific recommendations for high-traffic articles.
Surface content that drives community interaction.
Show articles that are generating the most discussion. Encourages users to click and join the debate.
Highlights "Crowd Favorites" based on explicit user feedback (Likes/Hearts).
Personalize the experience to bring readers back.
A "History" widget allowing users to find articles they started reading but didn't finish.
If a user reads 3 articles about "Space", populate this widget ONLY with Space news, even if they land on the Sports page.
Optimize your circulation strategy.
Personyze automatically tracks CTR and Time on Site to determine the winner.
Measure editorial success.
Common questions about setting up content recommendations. Anything else, our team is one message away.
No. Personyze can automatically extract tags, categories, and keywords from your page's meta tags or HTML structure. We also have an "AI Auto-Tagging" feature that analyzes the text content to generate interests dynamically.
Every time a user views an article, Personyze records the associated tags (e.g., "Politics," "Technology") into their profile. Over time, we build an "Interest Graph" (e.g., User is 70% interested in Sports, 30% in Tech) to weight future recommendations.
Yes. You can use "Managed Recommendations" to pin sponsored posts to specific slots (e.g., Position 1) or boost articles with a specific tag (e.g., "Partner Content") to appear more frequently.
Yes. As long as the video page has a unique URL and metadata (Title, Thumbnail), Personyze treats it just like an article. You can recommend "Videos You Might Like" based on viewing history.
Yes. You can add a filter to exclude content published more than X days ago (e.g., Date > Today - 30 days). This ensures recommendations remain fresh and relevant.
Date > Today - 30 days
Yes. Personyze tracks session behavior immediately. If an anonymous user reads three articles about "Bitcoin," the system will instantly start recommending more crypto news within the same session.
Yes. If you manage multiple sites (Cross-Domain), you can aggregate all content into a single feed and recommend articles from Site A to users on Site B to drive traffic across your network.
Enable the "Exclude Confirmed View" filter. Personyze tracks read history and will automatically remove articles the user has already visited to ensure they always discover something new.
Absolutely. You can use our visual CSS editor to match fonts, colors, and layout. Alternatively, you can use the JSON Output mode to send raw data to your own front-end template for pixel-perfect control.
It's the "People who read X also read Y" logic. It doesn't look at the content topic itself, but rather at user patterns. It helps users discover content that is popular among people with similar reading habits.
Yes. You can set rules like "If user is reading an article by Author A, recommend more from Author A" or "Show latest posts from Author B" if the user follows them.
No. The recommendation engine runs asynchronously. The rest of your page loads first, and the recommendation widget populates instantly afterwards, ensuring no delay in Core Web Vitals.
Yes. You can split traffic to test "Trending Now" vs "Personalized for You" to see which logic generates higher Click-Through Rates (CTR) and deeper engagement.
Standard sync is daily, but we support real-time updates via RSS feeds or API pushes. This is crucial for news sites where breaking news needs to appear in recommendations immediately.
For publishers, the key metrics are CTR (are people clicking?), Recirculation Rate (percentage of users who view another page), and Time on Site (did personalization increase session duration?).
You have the full playbook — now deploy AI-driven recommendations and watch recirculation and time-on-site climb.