A user-knowledge dynamic pattern matching process and optimization strategy based on the expert knowledge recommendation system

摘要

When automated pattern matching tools are used to execute user-knowledge pattern matching (UKPM) in the expert knowledge recommendation system (EKRS), user-knowledge matching is uncertain and the matching efficiency is low. To solve the above problems, the dynamic UKPM mathematical model is established and the “Entropy-Beta” method of crowdsourcing task assignment is designed to solve the model in the study. Firstly, the concept of Entropy is combined with crowdsourcing. The uncertainty of user-knowledge matching results is measured and the magnitude of the uncertainty is calculated. Secondly, based on the Beta distribution function, the accuracy of matching results is measured. The optimal matching results are selected and the matching results were sent to EKRS according to the matching probability. Thirdly, the knowledge recommendation process of UKPM is dynamically adjusted according to the matching probability. Finally, the comparison results of several algorithms showed that the Entropy-Beta algorithm could largely improve the accuracy, efficiency, dynamic regulation, and other performances of EKRS.