How do content recommendations work?

 

Overview

Simpplr uses a machine learning algorithm that adjusts content recommendations in Feed, Tile, and Popular Content Emails based on user behavior. This means that each user will see different content personalized to them.

  • All content types are supported (page, album, blog, and event).
  • There are up to five content recommendations for the daily email, 10 content recommendations for the weekly email, and 15 content recommendations in feed and tile.

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What are recommendations based on?

Our model bases recommendations on: 

  • The content users have engaged with in the past.
  • The content viewed by the users with similar profiles.
  • Recency of content. (latest news are more relevant than old news)
  • Do not recommend the content users have seen before since they are less likely to engage with them again.
  • Sites and people users are following.
  • If the user has engaged with less than 5 content (cold start), the model will provide 10 most popular and recent content to collect more signals.
  • Must read content and content on home and site carousel will be prioritized higher. (customer curated content)

Other factors:

  • Content older than a month is not recommended.
  • The model doesn't recommend more than two pieces of content from the same category (diversity).
  • The same content will not be recommended to the same users more than twice in daily/weekly email to avoid spamming (feedback loop).
  • If there is no content to recommend to users, we won't send an email and we will hide "Recommended" tab in feed and tile. 

 

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