Content recommendations put the right article, guide or video in front of each reader — a “continue reading” rail under a post, related guides in a sidebar, trending pieces on the homepage. This wizard walks you from catalog to live widget.
In the panel: New campaign › Web Browser › Content Recommendations.
It is the same engine and the same flow as product recommendations, with a content-shaped catalog, its own interaction types, and its own algorithms.
Your content catalog: articles, guides, videos. The step shows how many items Personyze holds and whether a feed keeps them current.
Content interactions are viewed, commented, liked and reached goal. Each shows how many events arrived in the last 30 days.
Two action types for content: the Content Recommendation Widget, and Content Recommendations via JSON Feed if you would rather render it yourself.
Templates are grouped into Articles, Continue Reading, Navigation, Paging, Slide-In, Grid, Slider and Vertical — layouts built for content rather than products.
Ten algorithms across Personalized & Popular, Visitor History, Catalog Events and Custom. Filter by page first: Home, Category, Article, Search or Not Found.
Output count and fallbacks, articles to skip, catalog restrictions and custom filter rules — the same controls as product recommendations.
Audience rules combined with AND / OR / XOR, each group switchable between Include and Exclude, with campaign readiness and an audience forecast on the right.
Your content catalog: articles, guides, videos. The step shows how many items Personyze holds and whether a feed keeps them current.
Content interactions are viewed, commented, liked and reached goal. Each shows how many events arrived in the last 30 days.
Two action types for content: the Content Recommendation Widget, and Content Recommendations via JSON Feed if you would rather render it yourself.
Templates are grouped into Articles, Continue Reading, Navigation, Paging, Slide-In, Grid, Slider and Vertical — layouts built for content rather than products.
Ten algorithms across Personalized & Popular, Visitor History, Catalog Events and Custom. Filter by page first: Home, Category, Article, Search or Not Found.
Output count and fallbacks, articles to skip, catalog restrictions and custom filter rules — the same controls as product recommendations.
Audience rules combined with AND / OR / XOR, each group switchable between Include and Exclude, with campaign readiness and an audience forecast on the right.
The steps
Seven steps: Catalog, Interactions, Content, Target, QA, Automation and Performance.
Automation and Performance only appear once the campaign has been saved. On a new unsaved campaign you will see five steps. Note also that Content comes before Target — you design the widget first, then decide who sees it.
1. Catalog — the articles to recommend from
Each item needs at least an internal id, a title and a URL, plus an image if your layout shows one. Ingest works the same four ways as the product catalog — file upload, URL feed, SFTP or the REST API.
Categories and tags earn their keep here. They are what allows a recommendation to follow a topic rather than fall back on raw popularity, and they power the “from current category” and “from visitor’s interests” scopes on almost every algorithm.
Use is_published to withdraw a piece without deleting it.
2. Interactions — what the engine learns from
Content interactions are a different set from products:
- Viewed — required. An article was read.
- Commented — the engagement signal for editorial content.
- Liked — saved or favourited.
- Reached goal — whatever counts as success for that piece: a signup, a download, a click through to a product.
These feed interest inference, which is what makes recommendations follow a reader across your site and, if you use the SDK, into your app.
The empty state matters. Nothing warns you at publish time. With no interactions the widget still renders — it just serves the same generic list to everyone, because there is no reading history to personalize against. Confirm at least Viewed is receiving events before you publish.
3. Content — the widget itself
Click Add action. Two types are offered: the Content Recommendation Widget, and Content Recommendations via JSON Feed — see JSON API — Content Recommendations for the second.
Templates are grouped into Articles, Continue Reading, Navigation, Paging, Slide-In, Grid, Slider and Vertical. The paging and continue-reading patterns are specific to content — for example Article Paging shows the two newest side by side, or steps through up to eight one at a time with arrows and a counter.
Choosing an algorithm
Pages available: All pages, Home Page, Category Page, Article Page, Search Page, Not Found. As with products, the page is not merely where the widget appears — it changes the anchor a behavioural algorithm works from.
The algorithms
Ten algorithms in four families. Expand a family to see what is in it.
Personalized & Popular — 5 algorithms
Best when the visitor’s intent is not yet clear. Good defaults for homepage and category pages.
| Content Recommended for You | Articles the visitor is likely to view or comment on, based on similar visitors’ behaviour plus demographics. |
| … Published Since Last Visit | The same, restricted to articles published after the visitor’s last session. |
| Most Popular | The most shown articles. |
| Most Liked Content | Articles most often liked or saved. |
| Most Commented | The most commented articles. |
Visitor History — 3 algorithms
Surfaces things this specific visitor has touched before. Best for returning visitors.
| Reached Goal | Articles the current visitor marked as reaching their goal. |
| Recently Viewed | Articles this visitor viewed recently — all of them, or only the one being viewed now. |
| You Commented | Articles the visitor commented on. |
Catalog Events — 1 algorithms
Triggered by changes in your article catalog — newly published items. A strong hook for return-visitor homepages.
| Recently Published | Articles added after the visitor’s last visit. |
Custom — 1 algorithms
When you want full control — you define the filter, Personyze ranks within it.
| Any Articles (custom filter) | Your own filter rules; Personyze ranks within the result set. |
Most take modifiers: a period (recently, today, last 2 / 4 days, last week, all time), a source scope (any, mix categories, mix interests, current category, last or all viewed categories, commented categories, visitor interests), and a sort (reached goal, commented, liked, viewed, recently published).
Inside the action editor
The editor has the same three sub-steps as product recommendations — Select Recommendation Algorithm, Customize Look and Feel, Placement, Trigger & Frequency — with the same output and fallback controls, skip rules, catalog restrictions and custom filter rules. Those are covered in detail on the Product Recommendations Wizard page and behave identically here.
4. Target — who sees it
With no rules the widget reaches every visitor, which is often right for a content rail. Narrow it by new versus returning readers, by the category they are reading, or by an imported audience.
5. QA — before publishing
Save the campaign as a draft and the QA panels open: campaign summary, shareable preview links, the Simulator, overlaps and conflicts with your other live campaigns, and preview results from the last 90 minutes of preview traffic.
The content-specific check: does the widget come back populated, and with sensible items? An empty widget points at the catalog or interactions rather than your targeting.
6. Automation — start and stop by rule
Rules that start or stop the campaign on a date, or on measured campaign or site performance. Nothing is on unless you add it, and measured rules are checked once a day against completed days.
7. Performance — once it is live
Impressions, engagement and attributed goals. Reporting can run from the campaign start or from the last edit.
Two clocks apply, as with every campaign: Live Visits shows matching sessions within seconds and is where you confirm a new campaign is working, while the Performance step only fills in after sessions end — about an hour after the first one closes, and 1–3 hours to settle.
What Performance looks like with data
For content, the Performance step trades transactions for engagement: goal completions, engaged readers (comments / likes), the goal rate, and the Shown → Viewed funnel over the whole site’s sessions.
Related
- Product Recommendations Wizard — the same flow for products, with the action editor documented in full.
- JSON API — Content Recommendations — raw JSON instead of a rendered widget.
- Content Recommendations for Apps
- Articles object in the REST API.