Diversity Recommendation Systems in Machine Learning and AI

Diversity Recommendation Systems in Machine Learning and AI

Diversity Recommendation Systems in Machine Learning and AI. Personalization & Discovery that Sparks Social Change and Increases Customer Lifetime Value.

Every day you are being influenced by machine learning and AI recommendation algorithms.

What you consume on social media through Facebook, Twitter, Instagram, the personalization you experience when you search, listen, and watch through Google, Spotify, Youtube, what you discover using Airbnb and UberEats, all of these products are powered by machine learning recommender systems.

80% of all content consumed on Netflix and $98 billion of annual revenue on Amazon is driven by recommendation systems and these companies continue investing millions in building better versions of these algorithms.

There are two main types of recommender systems:

  1. Collaborative filtering: finding similar users to you and recommending you something based on what that similar user liked.
  2. Content-based filtering: taking your past history and behavior to make recommendations.

There is also a hybrid based recommender system, which mixes collaborative and content-based filtering. These machine learning and AI algorithms are what power the consumer products we use every day.

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