  The purpose of this article is to introduce a new analytical framework
dedicated to measuring performance of recommender systems. The standard
approach is to assess the quality of a system by means of accuracy related
statistics. However, the specificity of the environments in which recommender
systems are deployed requires to pay much attention to speed and memory
requirements of the algorithms. Unfortunately, it is implausible to assess
accurately the complexity of various algorithms with formal tools. This can be
attributed to the fact that such analyses are usually based on an assumption of
dense representation of underlying data structures. Whereas, in real life the
algorithms operate on sparse data and are implemented with collections
dedicated for them. Therefore, we propose to measure the complexity of
recommender systems with artificial datasets that posses real-life properties.
We utilize recently developed bipartite graph generator to evaluate how
state-of-the-art recommender systems' behavior is determined and diversified by
topological properties of the generated datasets.
