Intelligent Information Recommendation Method in Web-Based Argumentation Support System

Abstract

In the web-based argumentation support system, a large amount of information is required for expert deliberation. If the system can recommend the qualified information to the experts in real time, it can not only save the time for experts to find the information, but also help the experts to activate the thinking and follow up the argumentation process. Most existing argumentation systems do not have intelligent recommendation systems. This paper proposes a hybrid recommendation method based on user behavior, firstly analyzes the historical preference behavior of experts and obtains the user data scoring matrix. Then, the scoring matrix is analyzed and calculated, and the recommendation information of the expert is obtained. Finally, the information that conforms to the topic of real-time discussion is classified and the information push for experts is completed. The experimental results show that this method can accurately push the effective data information, activate the expert thinking and improve the efficiency of the argumentation.