PV254 Recommender Systems
Autumn 2014 is the first run of this course. This page is still "in progress".
About the course
Recommender systems are very active area of both research and application
(well known applications include Amazon, Netflix). This course covers basic
principles of recommender systems, particularly with focus on collaborative
filtering (recommendations based on people behaviour) and on educational
applications (including discussion of projects developed at our faculty).
The focus is also on practical experience (a project).
- week 1-6: lectures (with discussions)
- 15. 9. Introduction, overview
- 22. 9. Collaborative filtering
- 29. 9. Other recommendation techniques
- 6. 10. Evaluation
- 13. 10. Educational recommender systems
- 20. 10. Other aspects of recommender systems, Case studies
- week 7-12: work on project, consultations
- week 13-14 (10. 12., 17.12.): presentation of projects
There are two options - an "applied" project and a "research" project.
Development of a simple recommender system
Project for teams of 1-4 students.
Goal: To build a simple recommender system.
The focus should be on functionality (not on user interface). The system should
include enough content and functionality to be "interesting".
Suggestions (will be discussed in more detail during lectures):
- "short text" recommendations: jokes, quotes, poetry, baby names, recipes, ...
- "local" recommenations (Brno): restaurants, cultural events, places, ...
- educational recommenations: courses (MU, MOOC), foreign language
vocabulary, learning materials, ...
- product recommendation (specialized for a particular domain): board
games, books for children, ...
Model development and evalution
Goal: For a given dataset develop a model for predicting user ratings / student
performance. Evaluate the model (compare to previous models). Provide
visualizations of the domain (similarities between "items").
Specific data sets will be provided, together with some basic guidelines for
model development and evaluation. Two types of data are available:
- Data about movies.
- Educational data from the web slepemapy.cz.
Main recommended sources:
- Recommender Systems: An Introduction. D. Jannach, M. Zanker, A.
Felfernig, G. Friedrich, 2010.
- Recommender Systems Handbook. F. Ricci, L. Rokach, B. Shapira, P.
B. Kantor, 2010.
to Recommender Systems (video lectures from Coursera course)
to Recommender Systems: A 4-hour lecture, Xavier Amatriain, Machine Learning Summer School 2014 @ CMU