Content Recommendations Playbook
A comprehensive guide for publishers and media sites. Learn how to set up AI-driven content feeds, track reader engagement, and deploy algorithms that boost recirculation and time on site.
Start NowThe Wizard Process
From the Dashboard, select the "Content Recommendation" wizard. The 4 main steps are: Catalog → Tracking → Algorithms → Design & Targeting.
Step 1: Content Feed
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.
Step 2: Interaction Tracking
Track engagement to build user profiles. Choose one method:
Option A: Site Containers (Visual)
Use the "Visual Selector" to click elements on your page (e.g., Author Name, Category Tag) to grab data without coding.
Option B: Programmatic JS (Developer)
<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>
Step 3: Algorithm Selection
Choose the logic that drives the recommendations (e.g., "Most Popular", "Collaborative Filtering"). See the recipes below for examples.
Advanced Filtering
Refine what content is shown using the "Final Touches" step.
Smart Logic Filters
- Exclude Read Content: "Exclude all Confirmed view" ensures users don't see articles they've already finished.
- Topic Match: "Only from Topic" -> "User read in past". Shows articles related to topics the user historically engages with.
- Author Match: "Only from Author" -> "Currently Reading". Shows more articles by the same writer.
Exclusion Rules
Strictly exclude content types, such as "Exclude all Commented" or "Exclude showing on current page" to avoid redundancy.
Design, Placement & Targeting
Control where and how the recommendations appear.
Widget Design
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.
Placement
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).
Dynamic Badges
Add overlays like "Most Read", "New", or "Popular this Week" to increase CTR.
Recipe 1: Homepage & Discovery
Engage readers immediately with relevant content.
1. Content Recommended for You
The gold standard. Displays articles matching the visitor's reading history and interests (e.g., "Politics" + "Europe").
2. Most Popular
Show what's trending across the site. Good for new visitors with no history.
3. Published Since Your Last Visit
A powerful retention tool. "Welcome back! Here are 5 new articles posted since you were here yesterday." Highly effective for news sites.
Recipe 2: Article Page Recirculation
Keep readers on site after they finish an article.
1. Visitors Who Read This Also Read
Collaborative Filtering: "People who read this article also read X." Great for discovering related deep-dives.
2. Read Next (Contextual)
Recommend articles from the Current Category or with the same Tags. Keeps the user in their current flow/topic.
3. Managed Related Articles
Allows editors to manually curate specific recommendations for high-traffic articles.
Recipe 3: High Engagement Boosters
Surface content that drives community interaction.
Most Commented
Show articles that are generating the most discussion. Encourages users to click and join the debate.
Most Liked / Favorited
Highlights "Crowd Favorites" based on explicit user feedback (Likes/Hearts).
Recipe 4: Reader Retention
Personalize the experience to bring readers back.
Recently Viewed
A "History" widget allowing users to find articles they started reading but didn't finish.
Interest-Based Recommendations
If a user reads 3 articles about "Space", populate this widget ONLY with Space news, even if they land on the Sports page.
A/B Testing Content Strategy
Optimize your circulation strategy.
Test Ideas
- Algorithm: Test "Collaborative Filtering" vs. "Content Similarity" (Tags).
- Time Frame: Test "Most Popular Last 24 Hours" vs. "Most Popular Last 7 Days".
- Design: Test a "Grid" layout vs. a "List" layout.
Personyze automatically tracks CTR and Time on Site to determine the winner.
Performance & Analytics
Measure editorial success.
Key Metrics
- CTR (Click-Through Rate): The primary metric for content recommendations.
- Article Views: Total reads generated by the widget.
- Engagement Uplift: Compare "Time on Site" for users who clicked a recommendation vs. those who didn't.
Frequently Asked Questions
Do I need to tag all my articles manually?
How does it know what a user is interested in?
Can I prioritize sponsored content?
Does it work with video content?
Can I exclude old news?
Date > Today - 30 days). This ensures recommendations remain fresh and relevant.
Does this work for anonymous visitors?
Can I recommend content from different domains?
How do I prevent the same article from showing twice?
Can I design the widget to look like my site?
What is "Collaborative Filtering" for content?
Can I filter by Author?
Does it impact page load speed?
Can I A/B test different algorithms?
How often is the content feed updated?
What metrics should I track?
Turn Visitor Intent into Revenue with
Smart Recommendations
A comprehensive platform that delivers tailored product and content recommendations at every stage of the customer journey—from first visit to loyal customer.
Step-by-Step Onboarding
Guided setup with a campaign wizard or a fully managed implementation by your dedicated success manager.
Customizable Widgets
Responsive templates that blend into your site and emails with a live visual editor and full CSS control.
API and Integrations
Works on any platform with server-side and client-side APIs, plus easy plug-and-play integrations.
Revenue & ROI Tracking
Prove your value. Advanced analytics track exactly how much revenue is generated by recommendations.
Set and Forget
Live learning. The engine continuously optimizes based on daily feedback to find the perfect match for every shopper.
Flexible Billing
A true pay-as-you-go model. Scale your personalization efforts without heavy upfront commitments.
Suggest the perfect product, every time
Stop guessing. Use Personyze’s intelligent recommendation engine to show the right products to the right shoppers. Boost AOV with "Frequently Bought Together" and AI-driven cross-selling strategies.
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