Exploratory study on cognitive information gain modeling and optimization of personalized recommendations for knowledge reuse

Abstract

Personalized recommendation for knowledge reuse provides a framework to share product knowledge such as assembly process, environmental impact and energy efficiency in manufacturing, in order to help engineers make their best decisions. It can reduce the search efforts of engineers and mitigate the encumbrance of information overload. However, traditional personalized knowledge recommendation method assumes that engineers differing characteristics—most notably their level of experience for simplify are the same. In this paper, we present a new method for handling personalized knowledge recommendation problem. A measurement model of cognitive information gain to predict the helpfulness of knowledge for engineers based on their level of experience is proposed. Knowledge is analyzed and helpfulness scores of knowledge are calculated by using the cognitive information gain measurement model. Then knowledge recommendations that are optimally helpful relative to engineers' experiences are generated. An example is used to depict the procedure of the proposed method. The example results show that the proposed method is effective and accurate in recommending knowledge that takes into account the level of engineer’s experience.