Implicit Acquisition of User Models in Cooperative Advisory Systems

User modelling systems to date have relied heavily on user models that were hand crafted for use in a particular situation. Recently, attention has focused on the feasibility of general user models, models that can be transferred from one situation to another with little or no modification. Such a general user model could be implemented as a modular component easily integrated into diverse systems. This paper addresses one class of general user models, those general with respect to the underlying domain of the application. In particular, a domain independent user modelling module for cooperative advisory systems is discussed. A major problem in building user models is the difficulty of acquiring information about the user. Traditional approaches have relied heavily on information that is pre-encoded by the system designer. For a user model to be domain independent, acquisition of knowledge will have to be done implicitly, i.e., knowledge about the user must be acquired during his interaction with the system. The research proposed in this paper focuses on domain independent implicit user model acquisition techniques for cooperative advisory systems. These techniques have been formalized as a set of model acquisition rules that will serve as the basis for the implementation of the model acquisition portion of a general user modelling module. The acquisition rules have been developed by studying a large number of conversations between advice-seekers and an expert. The rules presented are capable of supporting most of the modelling requirements of the expert in these conversations. Future work includes implementing these acquisition rules in a general user modelling module to test their effectiveness and domain independence.