Research and Development of User Clustering-Based Content Similarity Algorithms in Dance-Assisted Choreography Techniques.

With the gradual development of digital information and software computing capabilities, the use of computers in dance-assisted choreography is becoming more and more widespread. But although the level of computers is now in rapid development, the technical level of using computers in dance choreography is not yet very mature, technical support is not in place, dance-assisted choreography is not effective, and the existing technical level is not yet able to meet the new needs of dance choreography. In order to improve the dance-assisted choreography technology and provide a more complete educational user interface for dance-assisted choreography, the content similarity algorithm of user clustering has a wide range of operations and a strong ability to calculate the amount of data, combined with the computer to apply the content similarity algorithm of user clustering in dance-assisted choreography technology to build a dance-assisted choreography system based on user clustering. The article proposes three major methods based on collaborative filtering algorithm of user clustering, collaborative filtering algorithm based on similarity class and user preference, and fuzzy cluster analysis of users and analyses their principles. In the experimental part, the performance of IBCF algorithm and collaborative filtering algorithm in dance-assisted choreography system is compared and analysed to observe the change of MAE value under the change of user similarity with number under different