  With the rapid growth of the Internet and overwhelming amount of information
that people are confronted with, recommender systems have been developed to
effiectively support users' decision-making process in online systems. So far,
much attention has been paid to designing new recommendation algorithms and
improving existent ones. However, few works considered the different
contributions from different users to the performance of a recommender system.
Such studies can help us improve the recommendation efficiency by excluding
irrelevant users. In this paper, we argue that in each online system there
exists a group of core users who carry most of the information for
recommendation. With them, the recommender systems can already generate
satisfactory recommendation. Our core user extraction method enables the
recommender systems to achieve 90% of the accuracy by taking only 20% of the
data into account.
