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
  • Subscription 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.