Temperament-based information filtering: a human factors approach to information recommendation

The paper provides an intelligent multiagent approach to incorporate human temperaments into the filtering process of an information recommendation service. Our approach is to devise a new filtering mechanism, which addresses segmentation, learning, classification, and filtering techniques based on Keirsey's temperament theory (D. Keirsey and M. Bates, 1978), probability theory, the distributions of temperaments, and statistical reasoning. By presenting information units that are consistent with user interests as well as user temperament, the accuracy and precision of the recommendation service may be improved.