Application Research Of Association Rules Mining In Socialized Question Answering Platform

With the continuous advancement of Internet technology,people’s access to information has diversified.However,the problem of information overload and the correctness of the information itself still plague many people.Under such circumstances,the socialized question and answer platform came into being,and it helped users to obtain information efficiently with high-quality content,which is more conducive to the organization and digestion of knowledge.However,the number of social question-and-answer platforms and the number of topics are constantly increasing.How to provide targeted services to users has become the primary problem facing the socialized Q&A platform.The socialization of the question and answer platform topic category organization and the inclusiveness of the topic are the key factors affecting the socialized question and answer platform service.The association rules can find the correlation between data with certain search meanings from many data.The socialized Q&A platform has a large number of data that users pay attention to,but there are relatively few studies that use association rules to discover the relationships and rules existing in topic data and predict the individual needs of users.Therefore,this paper will explore the topics of interest and other related attributes of users of China’s social question-answer platform based on association rules.Based on this,this paper combs the research status of association rules and socialized question and answer platform.For the purpose of personalized topic service and user experience improvement,the association rule mining will analyze the association between user topic data and social users.The association analysis of attributes and topic data is performed.In this paper,Matlab is used to implement the more classical Apriori algorithm in association rules.The mining process specifically includes the following four aspects:association rule mining between topic categories,association rule mining between topic secondary categories,association rule mining of user gender and topic data,and association rules of industry and topic data of users.Digging.In the empirical research stage,this paper is based on the most well-known socialized question and answer platform in China,and it is a multi-dimensional and multi-level mining of topic data that is of interest to users.Finally,according to the analysis results of association rules mining,the application strategies of topic recommendation,communication and marketing in socialized question and answer platform are guided,including the following strategies:focusing on topic recommendation quality;accurately distinguishing user groups and providing personalized topic recommendation services;Topic-based content dissemination;topic-based user communication;enhanced user experience,increased user loyalty and brand reputation,and enhanced socialist question and answer platform competitiveness.