In todayís networked business environment, with the endless increase in available information, relevant information is becoming more and more difficult to find. Collaborative filtering (CF) generates recommendations for users based on othersí evaluations. CF has great potential to improve information search and knowledge reuse. Previous studies have mostly focused on the improvement of CF algorithms. Little research has been done on the effect of users and types of product domains on the performance of CF systems. In this study, four factorsóproduct domain, user characteristics, userís search mode, and number of usersó that are expected to affect the accuracy of CF systems were identified and investigated. The effects of the four factors were tested using data collected from two experiments in two different product domains: movies and research papers. It was shown that CF systems work better for knowledge-intensive domains than consumer product domains. The accuracy of CF systems is affected by usersí search mode and knowledge in a domain. This study demonstrates that CF systems have great potential in information search and customization. It also shows that a successful CF system needs to be designed to handle multiple modes of search, even within a domain and user group.