PV254 Recommender Systems

Autumn 2014 is the first run of this course, changes during semester are possible.

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).

Schedule

Preliminary schedule:

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):

Model development and evalution

Individual project
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: Materials

Sources

Main recommended sources: