Category Recommender for Content recommends content categories — the topics your articles are filed under — instead of single articles. It is the content twin of the product Category Recommender: a reader who does not yet know which article they want can still be shown the topic their kind of reader goes to next.
Adding it
In a Content Recommendations campaign: Add action → Recommendations → Category Recommender for Content, next to Recommend interests. Pick one of the three designs:
- Category Tiles — a photo grid with the name on the picture.
- Category Strip — round pictures in a scrolling row.
- Category List — names and counts, for a sidebar.
Then the three steps every recommendation has: Select Recommendation Algorithm, Customize Look & Feel, and Placement, Trigger & Frequency.
The algorithms
A category is chosen from the categories of the articles visitors read — their views, the goals they reached, what they commented on or liked.
| Algorithm | Recommends |
|---|---|
| Personalized Recommendations · You May Like | Categories fitted to this reader’s own reading. |
| Recently or Most Read Category | Categories the visitor read recently, or the most-read categories. |
| Category of Recently Read Article · Currently Reading | Categories from which the visitor read articles recently, or is reading now. |
| Others Who Read Also Read | Categories that readers of the same articles went on to. |
| Most Popular Category · Trending Categories | What everybody reads most, or more than usual right now. |
| Most Converting Category · What Converting Readers Read | Categories whose readers reach your goals. |
| Categories Commented On or Liked | Categories of articles the visitor engaged with. |
| Past Goals Recommendations · Reached Goal Again | Categories tied to goals the visitor reached before. |
What it needs
- A content catalogue whose articles carry categories — see Categories tracking.
- Reading history for the personal algorithms. For a first-time visitor, a popular or trending category is the better first question.
Related
- Category & Interest Recommenders — the product categories and article interests versions
- Content recommendations — recommending single articles