Observable Behavior for Implicit User Modeling: A Framework and User Studies

This paper presents a framework for observable behavior that can be used as a basis for user modeling, and it reports the results of a pair of user studies that examine the joint utility of two specific behaviors. User models can be constructed by hand, or they can be 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 obtain implicit feedback without requiring explicit relevance judgments. Four broad categories of potentially observable behavior are identified: examine, retain, reference, and annotate, and examples of specific behaviors within a category are further subdivided based on the natural scope of information objects being manipulated: segment, object, or class. Previous studies using Internet discussion groups (USENET news) have shown reading time to be a useful source of implicit feedback for predicting a user’s preferences. The experiments reported in this paper extend that work to academic and professional journal articles and abstracts, and explore the relationship between printing behavior and reading time. Two user studies were conducted in which undergraduate students examined articles or abstracts from the telecommunications or pharmaceutical literature. The results showed that reading time can be used to predict the user’s assessment of relevance, that the mean reading time for journal articles and technical abstracts is longer than has been reported for USENET news documents, and that printing events provide additional useful evidence about relevance beyond that which can be inferred from reading time. The paper concludes with a brief discussion of the implications of the reported results.