Items & Users

  • Webshop
    • Items: Products
    • Users: Customers
  • Library
    • Items: Books
    • Users: Library visitors
  • News site
    • Items: News articles
    • Users: Readers

Recommender Systems

Suggest personalised and relevant items

  • Cover the entire spectrum of the user’s interests
  • Take the user’s context into account
  • Avoid suggestions from what is already known
  • Expand the user’s range of interests

Scalability of Recommender Systems

Recommending Movies

Aproaches for Recommender Systems

  • Popularity-based
  • Association rules mining
  • Content-based
  • Collaborative filtering

Popularity-Based recommendations

Example: The New York Times Online

Pros:

  • Small complexity
  • Explainable

Cons:

  • No personalisation
  • Item properties are not taken into account