  Recommender systems have received great commercial success. Recommendation
has been used widely in areas such as e-commerce, online music FM, online news
portal, etc. However, several problems related to input data structure pose
serious challenge to recommender system performance. Two of these problems are
Matthew effect and sparsity problem. Matthew effect heavily skews recommender
system output towards popular items. Data sparsity problem directly affects the
coverage of recommendation result. Collaborative filtering is a simple
benchmark ubiquitously adopted in the industry as the baseline for recommender
system design. Understanding the underlying mechanism of collaborative
filtering is crucial for further optimization. In this paper, we do a thorough
quantitative analysis on Matthew effect and sparsity problem in the particular
context setting of collaborative filtering. We compare the underlying mechanism
of user-based and item-based collaborative filtering and give insight to
industrial recommender system builders.
