Application Of Content Similarity Algorithm Based On User Clustering In Street Dance Education Platform

Hip-hop originated in the United States.It is a kind of rich dance that is produced by street culture or musical style.Many years ago,this kind of foreign culture was still a non-mainstream underground culture.With the rapid spread of the Internet,hip-hop gradually evolved from underground culture.On the ground.With the help of the media,the hip-hop culture has spread rapidly across major rivers and rivers.The online and offline street dance education platform is the backbone of promoting the development of street dance culture.The relevant Internet hip-hop application occupies an important position.With the development of the Internet,the number of information has exploded,and we have entered the era of information overload from the era of lack of information,and users are easily lost in the ocean of information.Nowadays,some typical information distribution platforms have introduced personalized recommendation systems,such as: domestic headlines today,NetEase news,and so on.The success of these applications is largely due to the personalized recommendation system.After the user has a certain historical behavior,the system will automatically tap the user's potential interests and hones,and then the smart recommendation can be made.The user will feel that the system is “passing humanity”.Increased user satisfaction and retention.The commonly used recommendation algorithms in the personalized recommendation system are: collaborative filtering algorithm,content-based similarity recommendation algorithm,etc.In this paper,the collaborative filtering algorithm and content similarity algorithm are introduced and compared,and the two algorithms are proposed.A content similarity recommendation algorithm based on user clustering,and in accordance with the attenuation rules of human memory,establishes a long-term and short-term interest model for correcting medium vector weights of the algorithm.When the user sparsity is relatively low,user similarity is the dominant factor.It is used to predict the user's potential features.When the user data set is dense,the similarity of the content occupies more weight,which can ensure that the personal characteristics are preserved.This algorithm fully considers the problem of sparse data when new users join the system,and gives full play to the advantages of each algorithm.This article obtains the street dance information of a street dance site and the user's historical behavior through crawler technology,collates and analyzes and obtains the original data,establishes the model to write the program code,and verifies the effectiveness of the algorithm through experiments.This system is a street dance education platform,which includes four major modules recommended by coaches,space dynamics,hip-hop video teaching and street dance information,and curriculum management.It meets the daily needs of hip-hop enthusiasts and realizes complete front and back office functions.The coaches and institutions are only recommended according to the localization of positioning,and the personalized recommendation algorithm of this article is introduced into the system,which can greatly improve the accuracy of street dance information recommendation,and thus can improve the user's satisfaction and retention rate.