Research On Knowledge Recommendation Of Virtual Community Based On User Profile

With the development of Internet technology,the number of network information has grown rapidly,and the large number of data resources have created a rich and diverse environment for users.But what comes with it is the complexity and inefficiency of information retrieval.The huge number of data brings convenience but makes the selection difficult.The biggest function of knowledge recommendation is to reduce the difficulty of selecting information of users.Instead of user retrieval actively,Make the system analyzes users and filters the information according to different characteristics of the user,providing the user information with the basic of interest.In order to analyze the user behavior more accurately,this paper puts forward a method of knowledge recommendation based on the user's portrait on the basis of investigation and research,which helps users to quickly retrieve information.This thesis studies the related concepts of virtual community and user profile,classifies the users and types of virtual communities,summarizes the model construction methods and related technologies of user profile.Based on the research,the method of user profile can be used in virtual community for knowledge recommendation.Then the method of network text data mining is introduced.The text is processed by Chinese word segmentation.The text feature is extracted by TF-IDF algorithm and user tags are generated.Finally,the model of user profile is constructed with concrete examples.In order to verify that the knowledge recommendation method based on user profile has better recommendation performance,compare the traditional experiment with the traditional recommendation algorithm and select the evaluation index as the comparison.The final experimental results prove that the user profile can accurately extract the user behavior Features and help produce more accurate recommendations in the knowledge recommendation process.