User Profile Analysis Based On Collaborative Filtering

In recent years,the widespread popularity of social media has led to an explosive growth of social network data generated by users' behavior on social media,and countless user data including user attribute information has been collected and recorded.Inferring user attributes from publicly available online data has become a key foundation for personalized recommendations,and is of great significance.However,most users are reluctant to disclose too much in social networks based on privacy issues,so that the user's attribute information is missing or even false,which makes it more difficult for researchers to study social networks.Therefore,user attribute inference has attracted more and more research attention.However,the traditional attribute inference method is dedicated to inferring user attributes independently,ignoring the dependency between attributes,and there are two different conditions of unauthorized network and authorized network.Infer the problem of impact.Therefore,this paper proposes a method for inferring user attributes based on two different conditions,and analyzes and verifies the effect in the real social network data set.The following is the main research content of the article:(1)Acquisition and analysis of Weibo users' social network big dataBased on the Sina Weibo and Tencent Weibo in the social networking platform,this article selects the data of Sina Weibo and Tencent Weibo users for the collection and processing of data sets,designs a distributed crawler algorithm,and realizes the socialization of users Uninterrupted discrimination and automatic acquisition of network data,and preprocessing and analysis of the data,obtained the relevant data set for user attribute inference model needed for subsequent experiments.(2)Inference of user missing attributes based on collaborative filteringBased on the learning method,a user attribute inference method based on collaborative filtering is proposed to find and make full use of the similarity relationship between the user and the attribute.Based on the existing attribute information of the user,the attribute information unknown to the user is inferred.The model was compared and the experimental results verified that the model based on collaborative filtering proposed in this paper is better,and the performance of inferring user missing attributes is higher.(3)Inference of user missing attribute based on strong relationship of social networkIn the analysis of strong and weak relationships in social networks,a user attribute inference method based on strong relationships is proposed.On the basis of determining strong relationships on real large-scale social network data sets,the strong and weak relationships are marked and reduced to weak relationships.The proportion of the edges is compared and compared with these traditional algorithm.The result shows that the method proposed in the paper is more exact than these traditional algorithm,proving that the strong relationship between the nodes proposed in this paper will cause the lack of attributes of social network users inference has a positive effect.