Stereotyping users and tasks with associative memories

This paper presents a user modeling mechanism which is based on associative memory. In the context of associative user modeling, all task-related attributes or assumptions, termed as concepts, are organized into associative memory. Which is considered as a universal stereotype toward all potential users. The modeling process extracts the concepts and their weighted connections from the associative memory to form a unique profile that fits a particular user or task. This process is conducted by propagating the activation level throughout the network. We suggest that this approach can be expected to overcome some inherent problems of the conventional stereotyping approaches in terms of providing noise tolerance, pattern completion and learning capabilities. It can also avoid the complexity of truth maintenance in default reasoning which exists in previously known stereotyping systems