A System for Sharing Recommendations

COMMUNICATIONS OF THE ACM March 1997/Vol. 40, No. 3 59 The feasibility of automatic recognition of recommendations is supported by empirical results. First, Usenet messages are a significant source of recommendations of Web resources: 23% of Usenet messages mention Web resources, and 30% of these mentions are recommendations. Second, recommendation instances can be machine-recognized with nearly 90% accuracy. Third, some resources are recommended by more than one person. These multiconfirmed recommendations appear to be significant resources for the relevant community. Finally, the number of distinct recommenders of a resource is a plausible measure of resource quality. A comparison of recommended resources with resources in FAQs (lists of Frequently Asked Questions maintained by human topic experts) indicates the more distinct recommenders a resource has, the more likely it is to appear in the FAQs. PHOAKS is distinguished from other recommender systems by two major design principles: role specialization and reuse. Many recommender systems, particularly ratings-based systems [1, 3, 4], are built on the assumption of role uniformity. They expect all users to do the same types of work in return for A System for Sharing Recommendations Loren Terveen, Will Hill, Brian Amento,