融合用户社交与情境信息的虚拟知识社区个性化知识推荐研究

Research on Personalized Knowledge Recommendation of Virtual Knowledge Community Based on User Social and Situational Information

[目的/意义]通过融合用户社交与情境信息,构建虚拟知识社区个性化知识推荐模型并开展个性化知识推荐算法的设计,能够在一定程度上完善虚拟知识社区个性化知识推荐方法的理论体系,具有一定的理论价值和应用价值。[方法/过程]首先构建出基于用户社交与情境信息的虚拟知识社区个性化知识推荐模型,然后利用改进的最大团算法设计出虚拟知识社区个性化知识推荐算法,最后通过选取某虚拟知识社区的用户数据进行实例分析实现精准的个性化知识推荐。[结果/结论]在利用融合用户社交与情境信息进行虚拟知识社区个性化知识推荐过程中,通过对某虚拟知识社区的实例分析,表明其个性化知识推荐结果的精准度得到了显著的提升。

[Purpose/significance]Through integrating user social and situational information,the construction of virtual knowledge community personalized knowledge recommendation model and the design of personalized knowledge recommendation algorithm can improve the theoretical system of virtual knowledge community personalized knowledge recommendation method to a certain extent,which has certain theoretical value and application value.[Method/process]Firstly,a personalized knowledge recommendation model of virtual knowledge community based on user social and situational information is constructed.Then,an improved maximum clique algorithm is used to design personalized knowledge recommendation algorithm for virtual knowledge community.Finally,by selecting the user data of a virtual knowledge community platform for case analysis,accurate personalized knowledge recommendation can be achieved.[Result/conclusion]In the process of personalized knowledge recommendation for virtual knowledge community based on integrating user social and situational information,the case analysis of a virtual knowledge community platform shows that the accuracy of its personalized knowledge recommendation results has been significantly improved.