# Design and Construction of Hybrid Music Recommendation System Integrating Music Gene

Authors: Xuelin Zhao
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

In order to improve the effect of mixed music recommendation, this study combines music genes to construct a mixed music recommendation system. From the analysis of the complexity of each joint inference algorithm, GP + RKF has the highest complexity compared with the other three joint inference algorithms. Moreover, this study verifies it through the running time of the simulation experiment, using the growth method as the way of mutation. In addition, while adopting the optimal individual retention strategy, this study makes the eigenvalues of the input matrix IX all fall within the unit circle or unit circumference and makes the maximum fitness value of the individuals in the population equal to the global optimal fitness value. Finally, this study constructs an intelligent system. Through the experimental research, it can be seen that the hybrid music recommendation system based on the fusion of music genes proposed in this study has a good music recommendation effect.


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## Body

## 1. Introduction

In the context of the data age, it is an important task and challenge for internet platforms to filter out the information that users are interested in from massive amounts of information. The recommendation system builds a bridge between users and information, and is more and more widely used in major information service platforms, which has important practical significance and theoretical research value [1]. In the recommendation task of multimedia products, music recommendation has received extensive attention. However, because music has connotations such as emotion and style, it is difficult to quantitatively describe and extract features compared with other internet products, which makes the existing research still have many deficiencies [2]. At present, most of the existing studies only use audio, tags, or user feedback data and do not comprehensively use this information. Some methods simply classify music and set labels, or study explicit or implicit user feedback to predict user preferences, or only use audio to predict music features. However, recommendations based only on audio, tags, or categories will lose a lot of musical feature information, thus failing to provide better personalized services. In practical scenarios, the music cold start problem is common. It is difficult to solve the cold start problem by studying the explicit or implicit feedback data of users, that is, for newly added users and music, they cannot be recommended due to the lack of relevant feedback data [3].In addition to containing a large amount of user behavior data, the streaming music platform also records the metadata of songs, such as singer, lyrics, genre, cover, and audio, which can be used to design a new recommendation model and improve the recommendation accuracy of the model. The content-filtering method makes up for the insufficiency of collaborative filtering, recommending similar songs by mining the content characteristics of songs that users have listened to. However, content filtering ignores the “collaboration law” between users, because similar songs have similar properties and of users also have similar preferences.Based on the actual needs of users for music listening, this study combines music genre to construct a mixed music recommendation system to meet the music needs of different groups of people and improve the effect of mixed music recommendation.


## 2. Related Work

The high-order Markov chain model [4] considers more dependencies on previous behaviors. However, due to the large number of states in the multistep dependency calculation process, it leads to an excessive amount of calculation. In addition, the user behavior data are extremely sparse, resulting in inaccurate recommendation results. In order to solve the problem of computational complexity under multiple behaviors, the literature [5] proposed a state-space model based on the sequence of item attributes, which alleviated the problem of state-space explosion; literature [6] proposed a matrix decomposition-based model. The sequential recommendation model based on the proposed model generates the next recommendation item according to the interaction between the session and the candidate item; literature [7] proposes the FISM model, which performs matrix decomposition on the item-item collinear matrix without learning explicit user representations. In general, these Markov models based on matrix factorization only consider the interaction relationship in low-order cases and cannot solve the interaction in higher-order cases. At the same time, they ignore the interaction between sessions and different sessions. In recent years, recurrent neural network (RNN) has achieved very successful applications in the field of natural language processing [8]. This network structure has certain memory ability when processing time-series data, which is helpful for capturing the context in sentences. Literature [9] applied RNN to the time-series recommendation task and designed an RNN model (GRU4Rec) based on the gated recurrent unit (GRU). Literature [10] preprocesses sequences based on GRU4Rec, and embeds Dropout for data augmentation to reduce the overfitting problem. Literature [11] proposes a hierarchical RNN model that incorporates cross-session information into the recommendation model to capture intrasession and intersession dependencies. Literature [12] will change subreasoning that is added to RNN to deal with the uncertainty brought by sparse data. Besides RNN structure, a convolutional neural network (CNN) is also used for time-series recommendation task. Literature [13] proposes a session-aware recommendation method, which combines different types of user behaviors and learns a session representation, and then uses a deep ranking model for reranking; literature [14] proposes a session recommendation model based on deep neural networks, which simultaneously models static and dynamic user literature.; literature [15] models recent actions as “images” between temporal and latent dimensions, and uses CNNs to learn sequential patterns as local features of images, enabling the model to predict the top N likely interactions of users in the “near-future” items. Compared with traditional models, deep learning-based recommendation methods can mine long-term and short-term dependencies of user behavior. However, these methods can only mine global dependencies and cannot distinguish the differences in user literature from different historical behaviors.In literature [16], tags are clustered, and these clusters improve the effect of music recommendation to a certain extent. In music text information, music genre can prove to be useful information to help users choose favorite songs. In view of the above achievements, a hybrid recommendation method based on music genre and user community similarity is proposed in [17], which explicitly maps labels to the genre space while adding community network user similarity, so as to provide user song recommendation, and it achieves better results than the LSA method. Some of the above methods use the spatial matrix method, and some add collaborative relationship information but do not fully mine the latent semantic space of label text information. As a kind of textual information, the latent semantic space of tags plays a key role in song recommendation.Collaborative filtering recommendation algorithm and content-based recommendation algorithm have their own emphasis and shortcomings. The main idea of the hybrid recommendation algorithm is to combine the above two recommendation methods in order to make full use of the information of users and resources. In recent years, some scholars have also proposed recommendation algorithms based on graph structure. Literature [18] achieved the purpose of item recommendation by studying the application of clicks in the graph structure but did not effectively utilize the social relationship between users. Literature [19] proposed to use the random walk algorithm based on graph structure to calculate the similarity between items and implemented the algorithm on the MovieLens dataset. Although the random walk algorithm has shown superior performance in recommender systems, the contribution of social labeling to the calculation of item-to-item similarity has not been effectively verified and utilized. Considering that the tags given by users to items are of great use, some scholars also study the recommendation of tags. Literature [20] believes that the tags contained in the query submitted by the user clearly indicate the user's intention and can accurately describe an item. Therefore, he proposes a new method of forming tags, that is, extracting users' intentions and forming target-oriented item retrieval in social networks. The research performed by the above scholars all show the importance of social tagging, which is also the basis of this study.


## 3. Complexity and Convergence Analysis of Hybrid Inference Algorithms

The calculation of the time complexity of an algorithm involves basic operations such as addition, subtraction, multiplication, division, and sorting. Among them, the sorting algorithm includes comparison and exchange steps, and the number of operations is related to the specific sequence and sorting algorithm. The worst case is that an exchange operation is required every time. The time complexity of the algorithm can be measured using the amount of computationTs:(1)Ts=caAs+csSs+cmMs+cdDs+ccCs+ceEs.Among them,ca,cb,cc,cd,ce are the time spent in one addition, subtraction, multiplication, division, comparison, and exchange, respectively. The functions A,S,M,D,C, and E are the execution times of addition, subtraction, multiplication, division, comparison, and exchange, respectively. The complexity of genetic programming is related to the number of iterations, population size, tree depth, and fitness value. We assume that the number of iterations is H, the population size is P, the maximum depth of the individual tree is K, and the worst case of the fitness function applied to the individual is X; then, the complexity of genetic programming is OX×P×H×K.When using RLS to identify gene regulatory network model parameters,m is the number of parameters in the model. In the process of identifying model parameters, the RLS steps are as follows:(1)
The algorithm calculates the gain vectorKn as follows:(2)Kn=Pn−1Xnλ+XTnPn−1Xn.(2)
The algorithm calculates the prior estimation errorξn as follows:(3)ξn=dn−ωTn−1Xn.(3)
The algorithm updates the inverse correlation matrixPn as follows:(4)Pn=Pn−1−KnXTnPn−1λ.(4)
The algorithm updates the parameter matrixωn as follows:(5)ωn=ωn−1+Knξn.The calculation amount of the gain vector isT1=2m2+2m, the calculation amount of the prior estimation error is T2=m, the calculation amount of updating the inverse correlation matrix is T3=4m2, and the calculation amount of updating the parameter matrix is T4=2m. Based on the above analysis, the total calculation amount of the RLS algorithm can be expressed as follows:(6)T=T1+T2+T3+T4,=2m2+2m+m+4m2+2m,=6m2+5m.That is, the time complexity of RLS isOm2.The hybrid recommendation algorithm GP + RLS is a nested algorithm. When GP infers the structure of the gene regulation network model, it needs to iterateL times of filtering algorithm RLS to identify the parameters of the model; then, the time complexity of the hybrid recommendation algorithm GP + RLS is OX×P×H×K×L×m2.When using KF to identify gene regulatory network model parameters, it involves matrix inversion. For a matrix of sized×d, the complexity of inversion is about Od2.8.Ifm is the number of parameters in the model, that is, the dimension of the state value, then b is the dimension of the estimated value. In the process of identifying parameters, the steps of KF are as follows:(1)
State prior estimation refers to the specific relationship between variables at this moment and the previous moment as follows:(7)ω^−n=ω^+n−1.(2)
Variance prior estimation refers to calculating the variance of the estimated value and the actual value, which is used to evaluate the current estimation error.(8)P−n=P+n−1+Qn−1.(3)
The gain matrix enables KF to correct the value at the previous moment according to the size of the gain matrix.(9)Kn=P−nCTnCnP−nCTn+Rn−1.(4)
The state estimate is updated.(10)ω^+n=ω^−n+Kndn−Cnω^−n.(5)
The variance estimate is updated to correct the variance between the estimate and the actual value.(11)P+n=P−n−KnCnP−n.The computational cost of the state prior estimation isT1=m. The amount of computation for the variance prior estimate is T2=m2. As shown in the above matrix inversion complexity, the matrix inversion of each iteration of the Kalman filter algorithm is approximately Ob28. The amount of computation to update the state estimate is T3=2bm+m+b. Since the result of multiplying the matrix KnCn in equation (11) may be a sparse matrix, the complexity is at least Om2. Based on the above analysis, the time complexity of the KF algorithm is Ob28+m2.The hybrid recommendation algorithm GP + KF is a nested algorithm. When GP infers the structure of the gene regulation network model, it needs to use theL filter algorithm KF to identify the parameters of the model. Then, the time complexity of the hybrid recommendation algorithm GP+KF is OX×P×H×K×L×b28+m2.When using RKF to identify the parameters of the gene regulatory network model, ifm is the number of parameters in the model, that is, the dimension of the state value, then b is the dimension of the estimated value. In the process of identifying parameters, the steps of RKF are as follows:(1)
The algorithm finds the gainKn of the standard Kalman filter in steady state.(2)
The algorithm generates all robust Kalman filter gains in the rangeK1−ρ,K1+ρ, where 0<ρ<1, which is the performance indicator J∗ for calculating the Fishstick Kalman filter gain. Then, it replicates, crosses, and mutates, continuing to the next generation.(3)
If the performance indexJ∗ cannot be further improved or the maximum number of iterations is reached, the algorithm stops.The RKF is an algorithm obtained by optimizing the gain of the Kalman algorithm by the genetic algorithm GA based on the Kalman filter algorithm. In the process of each iteration, the gain of KF is obtained first, and the calculation amount at this time isOb28. In the process of optimizing the gain of the Kalman filter algorithm by the GA algorithm, if t is the number of iterations, h is the population size, c is the individual size, and the worst case of the fitness function applied to the individual is d, the complexity is about Ot×h×c×d. Finally, the algorithm updates the state estimates and variance estimates. Then, the complexity of RKF is about Ot×h×c×d×b28+m2.When GP infers the structure of the gene regulatory network model, it needs to iterateL times R K F to identify the parameters of the model; then, the time complexity of GP+RKF is OX×P×H×K×L×b28+m2×t×h×c×d.When using PF to identify gene regulatory network model parameters,m is the number of parameters in the model. In the process of identifying parameters, the steps of PF are as follows:(1)
The algorithm is initialized: the algorithm uses the setωkt,xktl=1N composed of N particles to represent the posterior probability density function pωy1.k, where the weight θk′=1/N.(2)
The algorithm calculates the particle weights as follows:(12)θkt=θk−1tpyk|xkt,ωk′pxkt|xk−1l,ωk′qxkI|ωkl,xk−1l,yk.(3)
The weightθkt is normalized as follows:(13)Neff=1∑k=1Nθk′2.(4)
The algorithm performs resampling: the algorithm resamples the particle set to obtain a new particle set.(14)ωki,xki,1Ni=1N=Resampleωki,xki,θkii=1N.(5)
The algorithm performs the output:(15)ω^k=∑i=1Nωk′θk′.According to the analysis of the algorithm, for then time, c1,c2, and c3 are used to represent the calculation amount of generating uniformly distributed random numbers, calculating Gaussian likelihood function and particle resampling. If xk∈Rm, then in the algorithm, the initial generation of N particles obeying the uniform distribution requires a calculation amount of T1=mc1N. To calculate the particle weight, the amount of calculation required is T2=c2+1N. To normalize the importance weights, N real multiplications are required, and the amount of computation is T3=N. The amount of computation required for particle releveling is T4=mc3N. Based on the above analysis, the time complexity of the PF algorithm is as follows:(16)T=c1m+c3m+c2+2N.In the above analysis operations, the calculation amount of the addition of real numbers is ignored. It can be seen from the above formula that forn time, the computational complexity of the standard particle filter algorithm is ON.When GP infers the structure of the gene regulatory network model, it needs to iterateL times of filtering algorithm PF to identify the parameters of the model; then, the time complexity of the hybrid recommendation algorithm GP+PF is OX×P×H×K×L×N.The algorithm analyzes the GP convergence. We assume that the maximum depth of the tree isDmax,F and E are function sets and endpoint sets, and the search space in question is a finite tree space S=Ti| tree Ti has a depth not exceeding Dmax, each root node of Tt is r∈kF, and each leaf node of T1 is lj∈E. Among them, the GP task is to search for an individual Tl∗ in S so that the fitness function f of equation (9) can achieve the maximum value.(17)fTt∗=f∗=maxfTı:Tt∈S.In this study, the E-CELL simulation data are used to infer the gene regulatory network between the three genes, the maximum depth of the tree isDmax=5, the function set is F=+,∗, and the endpoint set is E=x1,x2,x3. When the mutation probability of GP is pm=0.3, the optimal individual retention strategy is adopted, the growth method is used as the mutation path, GP is harvested with probability 1, and the convergence is independent of the initial population. In the process of evolution, if the optimal individual retention strategy is not adopted or some form of mutation operator is not used, the harvestability of GP is difficult to be guaranteed. Therefore, choosing an appropriate mutation operator can not only ensure the yieldability of GP in theory but also improve the performance of GP in practical applications.The relationship between the output signaldn and the input time series xn is as follows:(18)dn=ω0Txn+e0n.Among them,ω0 is the coefficient and e0n is the measurement noise with mean 0 and variance σ02. The input time series xn is a stationary and random process; then, there are the following:(19)limn⟶∞1nRxn=limn⟶∞1n∑i=1nxixTi,=ExnxTn,=Rxx.Among them,Rxn is the time-averaged correlation matrix, and Rxx is the set-averaged correlation matrix. Therefore, when n increases, we have the following:(20)1nRxxn≈Rxx,Rxx−1n≈1nRxx−1,Kn≈σ02nRxx−1.At timen, the output of RLS is as follows:(21)yn=ωTn−1xn.The learning curve of RLS is defined as follows:(22)ξn=Een2.The error vector is as follows:(23)vn=ω^n−ω0.The error is as follows:(24)en=dn−ωTn−1xn,=dn−ω0Txn−vTn−1xn,=e0n−vTn−1xn,ξn=Een2,=Ee2n,=Ee02n−2EvTn−1xne0n+EvTn−1xnxTnvn−1.In the formula, the expected value ofe02n is σ02. Since the following holds, the second expectation is equal to zero.(25)−2EvTn−1xne0n=−2EvTn−1xnEe0n,=0.The third expectation can be expressed as follows:(26)EvTn−1xnxTnvn−1=ExTnvn−1TxTnvn−1,=ETrxTnvn−1xTnvn−1T,=ETrxTnvn−1vTn−1xn,=ETrxnxTnvn−1vTn−1,=TrExnxTnEvn−1vTn−1,=TrRxxKn−1,ξn=σ02+0+TrRxxKn−1,=σ02+TrRxxσ02n−1Rxx−1,=σ02+Mn−1σ02n>M.M is the parameter dimension, which is the length of the RLS filter. As the number of iterations increases, the mean square errorξn tends to the variance σ02 of the measurement error e0n.The GP + RLS hybrid recommendation algorithm infers the convergence conditions of the gene regulatory network as follows: the growth method was used as a way of variation. While adopting the optimal individual retention strategy, the external noise is a noise with a mean of 0, a variance ofσ02, and the input time series xn is a stationary and random process.If the internal noise is zero, the input time series data in the gene regulatory network are known, and the input matrix is a nonsingular matrix. The model parameterω in the gene regulatory network is a random vector, and the model parameter ω is not related to the measurement of external noise. Therefore, the necessary and sufficient condition for the mean square error ω^n−ωn of the inferred model parameters to close to zero is that the eigenvalues of the input matrix X all fall within the unit circle or the unit circumference, that is,(27)λX≤1.In this study, the mutation probability ofpm=0.3 is used, the growth method is used as the way of mutation, and when the optimal individual retention strategy is adopted, the GP algorithm converges with probability 1. In the harvest condition of using the GP + KF hybrid recommendation algorithm to infer the gene regulatory network, it is also necessary to make the characteristic roots of the input matrix X all fall within the unit circle or the unit circumference.We assume thatTn=t1n,t2n,…,thn is the n-generation population of the genetic algorithm, tln is the individuals in the n-generation population, and i=1,2,…,h,h is the population size (the number of individuals in the population). We assume that fmax=maxftln|i=1,2,…,h is the maximum fitness value of individuals in the population, f∗=maxft|t∈S represents the global optimal fitness value of the problem sought by GA, S is the individual space, and t is any individual in S.(28)limj⟶∞pfmax=f∗=1.If and only if the above equation holds,GA is globally convergent. Among them, pfmax=f∗ represents the probability that the maximum fitness value of individuals in the population is equal to the global optimal fitness value.The sufficient and necessary condition for the convergence of KF is that the eigenvalues of the input matrixx all fall within the unit circle or the unit circumference, and the convergence condition of GA is that the maximum fitness value of the individuals in the population is equal to the global optimal fitness value. Then, the RKF converges as the characteristic roots of the input matrix x all fall within the unit circle or the unit circumference, and the maximum fitness value of the individuals in the population is equal to the global optimal fitness value. In this study, when using the GP + RKF hybrid recommendation algorithm to infer the gene regulatory network, the mutation probability of pm = 0.3 is used, the growth method is used as the way of mutation, and the optimal individual retention strategy is adopted. At the same time, the algorithm makes all the characteristic roots of the input matrix IX fall within the unit circle or unit circumference and makes the maximum fitness value of the individuals in the population equal to the global optimal fitness value. At this time, the hybrid recommendation algorithm GP + RKF converges.A particle filter is characterized by a globally convergent nonlinear, non-Gaussian filter method. Its convergence has a relatively loose theoretical guarantee, and the convergence of the particle filter algorithm is proved. The convergence theorem is as follows.When identifying gene regulatory network parameters, the importance weights are as follows:(29)θk∝pyk|xk′,ωk′pxk′|xk−1′,ωk′qxk′|ωk′,xk−1′,yk.The particle filter convergence theorem is as follows: the importance weight is bounded for anyωkt,xk−1t,yk, ∀k≥0. However, there exists a constant ck independent of the number N of particles, such that for any measurable function fk, there are the following:(30)E1N∑i=1Nfkx0k′−∫fkx0kpx0k,z1kdx0k2≤ckfk2N.From the abovementioned theorem, the harvesting speed has nothing to do with the dimension of the state, and the premise of harvesting is also very loose, as long as the support of the importance sampling function includes the support of the posterior density function.In this study, when using theGP+PF hybrid recommendation algorithm to infer the gene regulatory network, the mutation probability of pm=0.3 is used, the growth method is used as the mutation method, and the optimal individual retention strategy is adopted. At the same time, the support of the importance sampling function includes the support of the posterior density function, and the hybrid recommendation algorithm GP + PF converges at this time.This experiment uses the E-CELL simulation data to infer the second differential equation in the E-CELL model through four hybrid recommendation algorithms. When the hybrid recommendation algorithm obtains the optimal model structure, the filtering algorithm is used to infer the model parameters, and the convergence of the four hybrid recommendation algorithms is observed under the conditions of Gaussian noise and non-Gaussian noise (Laplace noise is selected in this experiment). The convergence of parameter identification in gene regulatory networks includes the convergence of the estimated parameterω^n and the convergence of the estimated error en.Considering the Gaussian sound, the convergence of the identification parameters of the four hybrid recommendation algorithms is shown in Figure1. Among them, the black dots are the parameter reference values, which are 9.72 and −17.5, respectively, the abscissa is the number of iterations, and the ordinate is the parameter value. It can be seen from the figure that when the number of iterations of GP + PF is about 10, the hybrid recommendation algorithm tends to converge, and the parameter identification results of GP+PF and GP+KF are the closest to the parameter reference values.Figure 1
Convergence of parameter estimation of the E-CELL model under Gaussian noise.Considering the Gaussian noise, the error estimation convergence of the four hybrid recommendation algorithms is shown in Figure2. Among them, the abscissa is the number of iterations, and the ordinate is the RMSE value of the estimated parameters and the real parameters of the model identified by the hybrid recommendation algorithm. The figure shows that as the number of iterations increases, each hybrid recommendation algorithm tends to be stable. When the number of iterations is about 10, the GP + PF algorithm has tended to converge. However, as the number of iterations increases, the RMSE value of GP + KF gradually decreases. When the number of iterations is about 50, GP + KF is already the smallest.Figure 2
Convergence of parameter estimation errors of the E-CELL model under Gaussian noise.Under the condition of non-Gaussian noise, the convergence of the identification parameters of the four hybrid recommendation algorithms is shown in Figure3. Among them, the black dots are the parameter reference values, which are 9.72 and −17.5, respectively, the abscissa is the number of iterations, and the ordinate is the parameter value. It can be seen from the figure that whether it is for the first parameter or the second parameter, the GP + PF identification model parameters are optimal, and when the number of iterations of GP + PF is about 10, the hybrid recommendation algorithm tends to converge.Figure 3
Convergence of parameter estimation of the E-CELL model under Laplace noise.Under the condition of considering non-Gaussian noise, the error estimation convergence of the four hybrid recommendation algorithms is shown in Figure4. The abscissa is the number of iterations, and the ordinate is the RMSE value of the estimated parameters and real parameters of the model identified by the hybrid recommendation algorithm. It can be seen from the figure that when the number of iterations is about 10, the GP + PF algorithm has tended to converge and the RMSE value is the smallest.Figure 4
Convergence of parameter estimation error of the E-CELL model under Laplace noise.


## 4. Design and Construction of Hybrid Music Recommendation System Integrating Music Gene

Gene refers to the DNA or RNA sequence that carries genetic information, which controls the individual traits of organisms, and is also determined by its own various characteristics. In order to facilitate the analysis of user behavior, this study describes various aspects of music through the music genome. The music genome structure is shown in Figure5.Figure 5
The structure diagram of the music genome.The data flow diagram is a graphical way to display the flow of data within the system and the process of processing. It can help system analysts to have an overall understanding of the framework and functions of the system. The data flow diagram of the music recommendation system in this study is shown in Figure6 below.Figure 6
Data flow diagram of hybrid music recommendation based on music genes, (a) background management database, and (b) music recommendation data flow diagram.
(a)(b)Database design is a very important step in the system requirement analysis stage, which is directly related to the operation of the system and the recommendation efficiency. According to the data flow diagram, it can be seen that the data tables that the system may need include the user login information table, user behavior data table, user preference information table, music gene table, and so on. The system entity contact diagram is shown in Figure7.Figure 7
Entity connection diagram of the system.The algorithm flow chart of the system is shown in Figure8.Figure 8
Flowchart of the music recommendation algorithm. (a) Overall algorithm flowchart. (b) Refinement system algorithm flowchart.
(a)(b)After constructing a hybrid music recommendation system that integrates music genes, the effect of the system is verified through multiple sets of experiments, the music recommendation effect of the model is mainly calculated, and the results shown in Table1 and Figure 9 are obtained.Table 1
Effect verification of hybrid music recommendation system with the fusion of music genes.
NumberRecommended effectNumberRecommended effectNumberRecommended effect133.262744.125359.83211.812831.265443.26339.912923.02559.84476.833060.395628.22557.883115.695742.65659.903246.275818.11777.513351.795935.90877.593423.556059.68959.143512.626163.751025.983622.25629.761136.023718.666377.401242.273863.116473.061368.783956.306543.081455.884062.166616.051534.344171.266765.921624.504266.626850.561770.824378.626927.261849.604420.037040.451967.684545.577143.232050.744650.897232.382161.964738.337313.382259.714872.947449.412356.224961.477563.822451.225021.167639.632512.565114.987717.762651.835251.367860.87Figure 9
Evaluation of the recommendation effect of the hybrid music recommendation system incorporating music genes.From the above research, it can be seen that the hybrid music recommendation system integrating music genes proposed in this study has a good music recommendation effect.


## 5. Conclusions

The user's music preference is estimated based on the existing knowledge, ignoring the influence of unknown knowledge on the user's preference, such as timing dependencies and contextual information. In recent years, domestic and foreign researchers have proposed a hybrid recommendation algorithm based on session and context, which fully exploits the influence of unknown knowledge on music preference and improves the quality of recommendation. However, these methods only consider the contextual information and temporal relationship of the user in the current session from a global level and lack the understanding of fine-grained features. For example, users have different points of interest in different songs in the list of songs they have listened to, and there are differences in the content that different users pay attention to the same song. Based on the actual needs of users for music listening, this study combines music genes to construct a hybrid music recommendation system to meet the music needs of different groups of people. The experimental study shows that the hybrid music recommendation system based on the fusion of music genes proposed in this study has a good music recommendation effect.



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