Recommender systems have become essential part of the Web in many domains. Research in this topic in the last few years led to design of a large variety of algorithms capable of generating personalized recommendations. Having so many algorithms, however, selecting the one that will be used to generate recommendations in a specific situation may be a challenging task. Common practice is to try multiple algorithms and then choose the one that performs the best. However, this approach requires non-trivial amount of time and effort.
In our work, we study an algorithm selection problem in recommender systems domain. Our goal is to design a solution that will recommend the use of an algorithm in a given situation. Understanding what affects performance of different algorithms is crucial for our approach. Therefore, our first task is to find out what data/domain/user characteristics cause some algorithms to success while the other algorithms fail.