Item Ranking

Like Personalization, even item ranking algorithms are Editorial Products because they demonstrate Editorial Judgment.

Why it matters: One might not have the resources to implement Personalization or might want to start with something simpler. In this situation, use data science or basic machine learning to implement user-agnostic item ranking.

How: You can train different item-ranking models for different use cases (recirculation, conversion), retention and cohorts (logged out, logged in, subscribers). For this, you might want to use datapoints like:
– Pageviews
– Reading Time = Avg. Time On Page x Page-views
– Topic rank
– Time decay
– Subscriptions|Subscription Conversion|conversions and renewals
– Age of the article

Future potential: Currently, most item ranking models run at a Posts level. A better approach would be to have models that run at phrase or sentence level, allowing for inline linking.