An empirical study of personalized advertising recommendation based on DBSCAN clustering of sina weibo user-generated content

Abstract

Abstract Today, with the dividend of mobile Internet traffic disappearing, mobile advertising is focusing on powerful algorithms of personalized recommendation. Sina Weibo has abundant advertising spaces and the accuracy of its recommendation directly affects user experience. UGC (user-generated content) is the selected or even original edited content for interaction through users’ subjective interests, which reflects users’ inner interests within a certain period of time. Adaptive DBSCAN clustering is performed on Weibo UGC, and the clustering center represents the user’s interest to a certain extent. By refining the text theme of advertisements to be recommended, the similarity between clustering center and the recommended theme is obtained, and the advertising category with high similarity is recommended to user. The experiment proves that the method is effective.