Basic Recommender Systems

Instructor: Paolo Cremonesi

What you'll learn

  •   You'll be able to build a basic recommender system.
  •   You'll be able to choose the family of recommender systems that best suits the kind of input data, goals and needs.
  •   You'll learn how to identify the correct evaluation activities to measure the quality of a recommender system, based on goals and needs.
  •   You'll be able to point out benefits and limits of different techniques for recommender systems in different scenarios.
  • Skills you'll gain

  •   Data-Driven Decision-Making
  •   Performance Tuning
  •   Innovation
  •   Data Mining
  •   Algorithms
  •   Statistical Methods
  •   Predictive Modeling
  •   Usability
  •   Applied Machine Learning
  •   System Requirements
  •   Data Ethics
  •   Machine Learning Algorithms
  • There are 4 modules in this course

    After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains. You'll know how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You’ll have the tools to measure the quality of a recommender system and incrementally improve it with the design of new algorithms. You'll learn as well how to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues such as identity, privacy, and manipulation. Providing affordable, personalised and high-quality recommendations is always a challenge! The course also leverages two important EIT Overarching Learning Outcomes (OLOs), related to creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the predictions. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and strategies in different and innovative scenarios, for a better quality of life.

    EVALUATION OF RECOMMENDER SYSTEMS

    CONTENT-BASED FILTERING

    COLLABORATIVE FILTERING

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