Introduction to Recommender Systems

Introduces the basics of recommender systems, their significance in various industries, and the fundamental algorithms and metrics used.

Mar 16, 2:00 – 3:00 PM


Key Themes

Build with AIMachine Learning

About this event

Introduction to Recommender Systems

  • Definition and significance
  • Historical context and evolution
  • Applications in different industries (e-commerce, streaming services, social media)

Types of Recommender Systems

  • Content-based filtering: Recommending items based on the properties of the items and a user profile.
  • Collaborative filtering: Recommending items by identifying patterns of preferences among users.
  • User-based
  • Item-based
  • Hybrid systems: Combining both methods to leverage their strengths.

Basic Algorithms and Metrics

  • Overview of key algorithms (nearest neighbors, matrix factorization)
  • Evaluation metrics (precision, recall, RMSE, MAE)

Challenges and Limitations

  • Cold start problem
  • Data sparsity
  • Scalability

Case Study Discussion


  • Gregory McGann

    GDG Oxford

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