User modeling can be used in information filtering and retrieval systems to improve the representation of a users information needs. User models can be constructed by hand, or learned automatically based on feedback provided by the user about the relevance of documents that they have examined. By observing user behavior, it is possible to infer implicit feedback without requiring explicit relevance judgments. Previous studies based on Internet discussion groups (USENET news) have shown reading time to be a useful source of implicit feedback for predicting a users preferences. The study reported in this paper extends that work by providing framework for considering alternative sources of implicit feedback, examining whether reading time is useful for predicting a users preferences for academic and professional journal articles, and exploring whether retention behavior can usefully augment the information that reading time provides. Two user studies were conducted in which undergraduate students examined articles and abstracts related to the telecommunications and pharmaceutical industries. The results showed that reading time could be used to predict the users assessment of relevance, although reading time for journal articles and technical abstracts are longer than has been reported for USENET news documents. Observation of printing events, a type of retention behavior, was found to provide additional useful evidence about relevance beyond that which could be inferred from reading time. The paper concludes with a brief discussion of the implications of the reported results. (Also cross-referenced as UMIACS-TR-2000-29) (Also cross-referenced as HCIL-TR-2000-11)