What are the types of recommender systems?

The main types are collaborative filtering, content-based filtering, hybrid, context-aware and knowledge-based systems, deep learning models and, most recently, generative recommendation.

  • Collaborative filtering: recommends what similar people liked or bought.
  • Content-based filtering: recommends items that resemble what someone already likes, using product attributes and descriptions.
  • Hybrid: combines the two, which is what most production systems do.
  • Context-aware: adjusts for location, season, device or time of day.
  • Knowledge-based: uses rules and constraints, such as compatibility or a customer’s contract catalogue.
  • Deep learning: neural models, such as two-tower and sequential models, that learn from very large amounts of behavior.
  • Generative: AI that produces the recommendation itself, such as a summary, a plan or a basket.