基于建构主义学习理论的个性化知识推荐模型

个性化推荐正成为“互联网+”和“大数据”时代信息网络服务的基本形式，虽然其已在电子商务和社交媒体的广泛应用中产生了巨大的商业价值，但在具有巨大潜在社会价值的个性化知识学习领域，相关研究与应用还较为稀少.研究提出一种基于建构主义学习理论的个性化知识推荐方法——建构推荐模型.新模型首先考虑将知识系统以知识网络的形式进行表达，随后引入最近邻优先的候选知识选择策略，以及基于最大可学习支撑度优先的top-K未学知识推荐算法.建构推荐模型通过知识网络的知识关联结构挖掘用户知识需求，并推荐给出最具建构学习价值的待学新知识.以饮食健康知识系统学习为例的实验分析表明，新模型在多种情况下推荐产生的个性化知识序列均具有较强的知识关联性和较高的知识体系覆盖率.

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.