NSWI166: Introduction to recommender systems and user preferences

Course Materials

Content of the course is available online: lecture slides, labs assignments, semestral work. Last year's slides and labs are also available.

Course Language

This year, the lectures (Wednesday from 10:40) and labs (Wednesday from 9:00 every even week) will be taught in Czech.

If you want to pass the course in English, contact the lecturer.

Course Organization

The preliminary course roadmap is as follows (note this may slightly change over the semmester):

Lectures

• Basics
  • 18.02. - Introduction; What are recommender systems (RS)
  • 25.02. - Collaborative filtering RS
  • 04.03. - Collaborative filtering RS
  • 11.03. - Content-based RS
  • 18.03. - Evaluating recommender systems
  • 25.03. - Evaluating recommender systems
• User Feedback
  • 01.04. - Probably void (habilitation lecture; to be confirmed)
  • 08.04. - Explicit and implicit user feedback
• (Slightly) Advanced RS Concepts
  • 15.04. - Hybrid RS, part2, part3
  • 22.04. - Hybrid RS II.
  • 29.04. - Visualize user preferences, linear monotone preference model, explanations
  • 06.05. - Deep Learning in RS, new challenges & recent trends (context, explanations, biases, fairness,...)
  • 20.05. - Invited lecture, Seznam.cz To be confirmed

Labs

• Labs 1: Code your first recommender system (assignment). Have a working Python 3.x instance with Jupyter notebook ready. If you never heared about NumPy, SciPy, or Pandas, have a look, e.g., here.
• Labs 2: Using simple RS frameworks: LensKit (assignment).

Grading & Requirements

Exams

Oral exams with written preparation: 4-5 questions from the theory covered during lectures; being able to apply theoretical findings in practical settings.

Credits

The following is required to obtain the credits:

• Active reading: four tasks (each time you can select one of 2-3 papers); write a short report on selected papers (strict deadlines). At least two accepted reports needed to pass, additional/exceptional reports = bonification for exams
• Participation during labs: completion of the labs assignments to gain "active participation" points. At least three points needed to pass, additional/exceptional results = bonification for exams. Expect that 2-3 assignments might have to be finalized at home.
• Semestral work (TBD): . Create a user study comparing several recommendation variants and evaluate its results.

Note that in case you cannot attend a lab, you can still complete the assignment at home. There will be options to upload your completed assignments via Grupik module of SIS.
Alternative pass: as a (probably more difficult) alternative to the requirements above, we can discuss an individual, more extensive, research project (contact me if interested).