Item Ranking - Ritvvij Parrikh Item Ranking | Ritvvij Parrikh Humane ClubMade with Humane Club
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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.
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