Personalized Knowledge Recommendation Model Based on Constructivist Learning Theory

Personalized recommendation is becoming a basic form of information network services in the era of Internet+ and big data. Its wide use in e-commerce and social media has produced huge commercial value, however, there are only limited research and applications in the field of personalized knowledge learning, which may have tremendous potential social value for public education and personalized information selection. This study proposes a novel personalized knowledge recommendation method—constructive recommendation model, based on constructivist learning theory. The new model uses knowledge networks to represent expected knowledge systems, uses the nearest neighbor priority strategy to select knowledge item candidates, and introduces top-K unstudied knowledge recommendation algorithm based on sorting knowledge candidate items by their learnable constructive degrees. The proposed constructive recommendation model can dig users potential knowledge demands by comparing domain knowledge network structure and users learnt knowledge network structure. Then it can orderly recommend most needful knowledge items to users for gaining the greatest constructive learning effect. We choose a very interesting healthy diet knowledge system as the experimental problem, in which 14600 knowledge documents are grabbed from public Internet Websites in China with knowledge subjects ‘health knowledge’, ‘dietary nutrition’and ‘dietary misconceptions’etc. Some meaningful experimental analysis are executed in this paper, and corresponding results demonstrate that recommended knowledge sequences given by our model can gain stronger knowledge continuity and higher knowledge learning efficiency than the existing related methods.