Advanced Techniques in Recommender Systems

Dive deeper into the algorithms, particularly matrix factorization techniques, and explore model evaluation and optimization strategies.

Mar 23, 2:00 – 3:00 PM


Key Themes

Build with AIMachine Learning

About this event

Advanced Algorithms

  • Deep dive into matrix factorization techniques (SVD, ALS)
  • Introduction to neural network-based recommenders (autoencoders, CNNs for recommendation)

Evaluation and Optimization

  • Cross-validation techniques for recommender systems
  • Overfitting prevention strategies
  • Parameter tuning (grid search, random search)

Context-Aware Recommender Systems

  • Incorporating context into recommendations (time, location, device)
  • Algorithmic adjustments for context-awareness

Ensemble Techniques

  • Combining multiple models for improved performance
  • Stacking, blending, and boosting applied to recommender systems

Case Study Discussion


  • Gregory McGann

    GDG Oxford

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