Made with Humane Club
Block Pattern: Slim Fit
- 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.
- 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:
- 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.
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