Meta-recommending: Adaptive Selection of Personalized Recommendation Algorithms

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.