1. A hotspot music mining method is characterized by comprising the following steps:

determining seed users in all users of a music platform, and constructing a first feature vector based on operation behavior data of the seed users;

determining music to be mined based on the operation behavior data of the seed user, and constructing a second feature vector based on the basic information of the music to be mined;

splicing the first feature vector and the second feature vector to obtain a target feature vector of the music to be mined;

and inputting the target characteristic vector into a trained neural network model to obtain a prediction result of potential hotspot music.

2. The method of claim 1, wherein after determining the seed user among all users of the music platform, the method further comprises:

constructing user characteristics of the seed user based on the user information and the music preference portrait of the seed user;

selecting non-seed training users from non-seed users, and constructing user characteristics of the non-seed training users based on user information of the non-seed training users and the music preference portrait;

training a classification model by using the user characteristics of the seed user and the user characteristics of the non-seed training user;

inputting user information and music preference portrait of non-seed users except the non-seed training user into a trained classification model to predict and expand the seed user;

and adding the expanded seed user into the seed user.

3. The method of claim 1, wherein determining a seed user among all users of the music platform comprises:

determining current hot music in a music platform and a cold time period corresponding to the current hot music, and determining a user having an operation behavior on the current hot music in the cold time period as a seed user;

and/or determining high-quality music content in the music platform, and determining the user with operation behavior on the high-quality music content as a seed user;

and/or determining the user with the influence larger than the preset influence in the music platform as a seed user; wherein the influence comprises influence in the music platform and/or influence in other platforms.

4. The method for mining hot spot music according to claim 1, wherein the constructing a first feature vector based on the operation behavior data of the seed user comprises:

constructing a network topology based on the operation behavior data of the seed user; each piece of operation behavior data comprises an operation behavior, a seed user executing the operation behavior and operated music, nodes in the network topology represent the seed user executing the operation behavior or the operated music, and edges between the nodes represent the operation behavior;

traversing the network topology according to a preset rule to obtain a music sequence to be mined, and performing feature representation on the music sequence to be mined to obtain a first feature vector.

5. The method for mining hot spot music according to claim 4, wherein the determining music to be mined based on the operation behavior data of the seed user comprises:

and determining music to be mined based on the music sequence to be mined.

6. The method for mining hot spot music according to claim 4, wherein the constructing a network topology based on the operation behavior data of the seed user comprises:

distributing corresponding weight to each type of operation behavior data, and constructing a network topology based on the operation behavior data of the seed user and the weight corresponding to each type of operation behavior data;

correspondingly, traversing the network topology according to a preset rule to obtain a music sequence to be mined, including:

and traversing the network topology according to the weight corresponding to each type of operation behavior data to obtain the music sequence to be mined.

7. The method for mining the hot spot music according to claim 1, wherein the step of inputting the target feature vector into a trained neural network model to obtain a prediction result of potential hot spot music comprises the following steps:

inputting the target feature vector into a trained neural network model to obtain a potential value corresponding to each piece of music to be mined;

and determining the music to be mined with the potential value larger than the preset potential value as potential hotspot music.

8. A training method of a neural network model is characterized by comprising the following steps:

determining seed users in all users of a music platform, and determining current popular music, current cold music and a cold time period corresponding to the current popular music in the music platform;

constructing a training feature vector corresponding to the current popular music based on the operation behavior data of the seed user on the current popular music in the cold time period and the basic information of the current popular music;

constructing a training feature vector corresponding to the current cold music based on the operation behavior data of the seed user on the current cold music and the basic information of the current cold music;

and training a neural network model by taking the training feature vector corresponding to the current popular music as a positive sample and the training feature vector corresponding to the current cold music as a negative sample, wherein the trained neural network model is used for predicting potential hotspot music based on the target feature vector of the music to be mined.

9. An electronic device, comprising:

a memory for storing a computer program;

a processor for implementing the steps of the hot spot music mining method according to any one of claims 1 to 7 or the training method of the neural network model according to claim 8 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the hotspot music mining method according to any one of claims 1 to 7 or the training method of the neural network model according to claim 8.

Background

In the related technologies, the hotspot mining technology is mainly applied to the fields of videos, texts and the like, statistics and monitoring are carried out based on contents, only the heat change of related words can be passively monitored, and the content mining method cannot be migrated to the mining of the contents of music works and cannot be used for mining the contents which are cold at present but become hot in the future.

Therefore, how to improve the accuracy of mining the hot music is a technical problem to be solved by those skilled in the art.

Disclosure of Invention

The application aims to provide a hotspot music mining method, electronic equipment and a computer readable storage medium, and accuracy of hotspot music mining is improved.

In order to achieve the above object, a first aspect of the present application provides a method for mining hot spot music, including:

determining seed users in all users of a music platform, and constructing a first feature vector based on operation behavior data of the seed users;

determining music to be mined based on the operation behavior data of the seed user, and constructing a second feature vector based on the basic information of the music to be mined;

splicing the first feature vector and the second feature vector to obtain a target feature vector of the music to be mined;

and inputting the target characteristic vector into a trained neural network model to obtain a prediction result of potential hotspot music.

To achieve the above object, a second aspect of the present application provides an electronic device comprising:

a memory for storing a computer program;

and the processor is used for realizing the steps of the hot spot music mining method when executing the computer program.

To achieve the above object, a third aspect of the present application provides a computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, implements the steps of the above hot music mining method.

According to the scheme, the hotspot music mining method comprises the following steps: determining seed users in all users of a music platform, and constructing a first feature vector based on operation behavior data of the seed users; determining music to be mined based on the operation behavior data of the seed user, and constructing a second feature vector based on the basic information of the music to be mined; splicing the first feature vector and the second feature vector to obtain a target feature vector of the music to be mined; and inputting the target characteristic vector into a trained neural network model to obtain a prediction result of potential hotspot music. According to the hotspot music mining method, seed users are firstly screened from the music platform, and because the seed users have strong appreciation capability or large influence on music, the probability that music to be mined determined based on seed operation behavior data becomes potential hotspot music is large. Secondly, constructing a target characteristic vector based on the operation behavior data of the music to be mined and the basic information of the music to be mined by the seed user, and inputting the target characteristic vector into the neural network model to obtain potential hotspot music. The target characteristic vector is constructed based on the operation behavior data of the seed user, so that the music has certain propagation degree and detonation point, the target characteristic vector is constructed based on the basic information of the music to be mined, the music making quality is high enough, and the accuracy of mining the hot music can be improved by combining the target characteristic vector and the basic information. The application also discloses an electronic device and a computer readable storage medium, which can also achieve the technical effects.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:

fig. 1 is an architecture diagram of a hot spot music mining system according to an embodiment of the present application;

fig. 2 is a flowchart of a method for mining hot music according to an embodiment of the present application;

fig. 3 is a flowchart of another hot music mining method according to an embodiment of the present application;

fig. 4 is a flowchart of another hot spot music mining method according to an embodiment of the present application;

fig. 5 is a structural diagram of a hotspot music mining device according to an embodiment of the present application;

fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In order to facilitate understanding of the hot music mining method provided by the present application, a system for using the hot music mining method is described below. Referring to fig. 1, an architecture diagram of a hotspot music mining system provided by an embodiment of the present application is shown, and as shown in fig. 1, includes a server 10.

In this application, the server 10 is configured to execute the steps of the hotspot music mining method, including: determining a seed user in all users of the music platform; determining music to be mined based on the operation behavior data of the seed user, and constructing a target feature vector of the music to be mined based on the operation behavior data and the basic information of the music to be mined; and inputting the target characteristic vector into a trained neural network model to obtain a prediction result of potential hotspot music.

It can be understood that a neural network model may also be provided in the server 10, and is used for predicting potential hotspot music, the input of the neural network model is a target feature vector constructed based on the operation behavior data of the music to be mined by the seed user and the basic information of the music to be mined, the output of the neural network model may be a potential value corresponding to each music to be mined, and the music to be mined having a potential value greater than the preset potential value is potential hotspot music.

Further, the hotspot music mining system may further include a plurality of clients 20 establishing communication connection with the server 10, and the clients 20 may include fixed terminals such as a PC (Personal Computer, and mobile terminals such as a mobile phone), and are configured to display potential hotspot music.

The embodiment of the application discloses a method for mining hot music, which improves the accuracy of mining the hot music.

Referring to fig. 2, a flowchart of a method for mining hot spot music according to an embodiment of the present application is shown in fig. 2, and includes:

s101: determining seed users in all users of a music platform, and constructing a first feature vector based on operation behavior data of the seed users;

the execution subject of the embodiment is the above server, and the purpose is to screen potential hotspot music in the music platform. In this step, a seed user is first determined among all users in the music platform, and since the seed user has strong appreciation capability or great influence on music, the music related to the seed user has a great chance of becoming potential hotspot music.

As a possible implementation, the determining the seed user among all users of the music platform includes: determining current hot music in a music platform and a cold time period corresponding to the current hot music, and determining a user having an operation behavior on the current hot music in the cold time period as a seed user. In the present embodiment, a user who has a prospective operation behavior, i.e., a user who has successfully predicted the current popular music, is regarded as a high-quality user who has a look ahead and has a high eye, and the high-quality user is regarded as a seed user. The standard of the current popular music can be music with a current click rate larger than a preset click rate, the prospective operation behaviors comprise playing, ordinary playing, commenting, collecting or sharing and the like of the current popular music in a cold time period, and the click rate of the current popular music in the cold time period is smaller than the preset click rate. In specific implementation, the current popular music is analyzed in the music platform, a corresponding cold time period is determined by backtracking forward in time, and the user who has an operation behavior on the current popular music in the cold time period is the high-grade user. As a preferred embodiment, the number and duration of the prospective operation behaviors may be analyzed, and a user with the number of the prospective operation behaviors being greater than a preset number, or the duration being greater than a preset time, or a combination of the two may be determined as a seed user.

As another possible implementation, the determining the seed user among all users of the music platform includes: determining high-quality music content in a music platform, and determining a user with operation behavior on the high-quality music content as a seed user. In the present embodiment, Content (User Generated Content, UGC) produced by a general User in a music platform is analyzed, and high-quality music Content is screened. The high-quality music content may include the number of high-quality comments produced by the user, the number of high-quality song list videos or podcasts released, and the like, and the users who produce the high-quality music content or the users who comment, collect or analyze the music content are regarded as high-creativity users, and the high-creativity users are regarded as seed users.

As another possible implementation, the determining the seed user among all users of the music platform includes: determining users with influence larger than preset influence in the music platform as seed users; wherein the influence comprises influence in the music platform and/or influence in other platforms. In this embodiment, the influence of all users in the music platform is analyzed, and the user with the larger influence is used as the seed user. In a specific implementation, the influence of the user may be represented by the attention amount, the number of works, or the interaction data of the user, where the influence may include the influence in the music platform, or may include the influence in other platforms, that is, in this implementation, the influence of the user on the music platform may be analyzed, and the influence of the user on other platforms may also be analyzed.

Furthermore, the embodiment can also analyze the popularity, freshness, popularity and the like of the singing of the user, and accordingly screen out the seed users who have the opportunity to predict the popular music, and the embodiment is not specifically limited.

On the basis of this step, as a preferred embodiment, after determining the seed user among all users of the music platform, the method further includes: constructing user characteristics of the seed user based on the user information and the music preference portrait of the seed user; selecting non-seed training users from non-seed users, and constructing user characteristics of the non-seed training users based on user information of the non-seed training users and the music preference portrait; training a classification model by using the user characteristics of the seed user and the user characteristics of the non-seed training user; inputting user information and music preference portrait of non-seed users except the non-seed training user into a trained classification model to predict and expand the seed user; and adding the expanded seed user into the seed user.

In a specific implementation, the user characteristics are constructed based on user information of the user, which may include age, gender, region, academic calendar, etc., and a music preference profile for describing music preferred by the user, which may include a preferred music style, a preferred singer type, etc. of the user, which is not specifically limited herein. The user characteristics of the seed user are used as positive samples, and the user characteristics of some non-seed users are used as negative samples, and training of the classification model is performed, where the classification model may include a logistic regression model, and the like, and is not specifically limited herein. And the trained classification model is used for predicting the rest non-seed users, and screening expanded seed users with comprehensive capacity and low taste, creativity and influence to add into the seed users. Furthermore, the attention relationship network and the sharing propagation network of the seed user can be analyzed, and the seed user is expanded in the attention relationship network and the sharing propagation network of the seed user.

Secondly, a first feature vector is constructed based on operation behavior data of the seed user, and each piece of operation behavior data comprises an operation behavior, the seed user executing the operation behavior and the operated music. As a possible implementation, the constructing the first feature vector based on the operation behavior data of the seed user includes: constructing a network topology based on the operation behavior data of the seed user; wherein, the nodes in the network topology represent seed users who execute the operation behaviors or operated music, and edges between the nodes represent the operation behaviors; traversing the network topology according to a preset rule to obtain a music sequence to be mined, and performing feature representation on the music sequence to be mined to obtain a first feature vector. In specific implementation, a network topology is constructed based on the operation behavior data of the seed user on the music, nodes of the network topology are the seed user and the music operated by the seed user, namely the music to be mined, edges between the nodes represent the operation behavior of the seed user on the music to be mined, so that the music sequence to be mined can be determined by traversing the network topology through algorithms such as deepwalk or node2vec, and the like, wherein the identifiers of a plurality of pieces of music to be mined are included. Further, performing feature representation on the music sequence to be mined by using a word2vec algorithm to obtain a second feature vector.

S102: determining music to be mined based on the operation behavior data of the seed user, and constructing a second feature vector based on the basic information of the music to be mined;

in this step, music to be mined is determined based on the operation behavior data of the seed user, that is, music related to the seed user is determined as music to be mined, and the music to be mined may be determined based on a music sequence to be mined obtained after traversing the network topology in the previous step. Basic information of music to be mined may be characterized using Convolutional Neural Networks (CNN), Bidirectional Encoder representation techniques based on transducers (BERT), etc. to construct a second feature vector, where the basic information of the music to be mined may include a content knowledge graph, audio, lyric text, etc., and the content knowledge graph may include a speaker, composer, singer, language, genre label, etc. of the music composition, which is not specifically limited herein.

S103: splicing the first feature vector and the second feature vector to obtain a target feature vector of the music to be mined;

it should be noted that the first feature vector is constructed based on the operation behavior data of the seed user, so that the music has certain propagation degree and detonation point, the second feature vector is constructed based on the basic information of the music to be mined, so that the music production quality is high enough, and the target feature vector obtained by splicing the first feature vector and the second feature vector can be used for evaluating whether the music is potential hotspot music.

S104: and inputting the target characteristic vector into a trained neural network model to obtain a prediction result of potential hotspot music.

In a specific implementation, the neural network model is used to predict potential hot spot music. In the step, a target characteristic vector constructed based on the operation behavior data of the seed user and the basic information of the music to be mined is input into a trained neural network model to obtain a prediction result of potential hotspot music.

As a possible implementation, the step may include: inputting the target feature vector into a prediction result neural network model to obtain a potential value corresponding to each piece of music to be mined; and determining the music to be mined with the potential value larger than the preset potential value as potential hotspot music. In specific implementation, each line in the target feature vector corresponds to one piece of music to be mined, the neural network model is used for predicting the potential value of the music to be mined, namely the target feature vector is input into the neural network model to obtain the potential value corresponding to each piece of music to be mined, and the music to be mined with the potential value larger than the preset potential value is determined as potential hotspot music.

According to the method for mining the hotspot music, the seed users are firstly screened from the music platform, and the probability that the music to be mined becomes potential hotspot music determined based on the seed operation behavior data is high because the seed users have strong appreciation capability or great influence on the music. Secondly, constructing a target characteristic vector based on the operation behavior data of the music to be mined and the basic information of the music to be mined by the seed user, and inputting the target characteristic vector into the neural network model to obtain potential hotspot music. The target characteristic vector is constructed based on the operation behavior data of the seed user, so that the music has certain propagation degree and detonation point, the target characteristic vector is constructed based on the basic information of the music to be mined, the music making quality is high enough, and the accuracy of mining the hot music can be improved by combining the target characteristic vector and the basic information.

The embodiment of the application discloses a hotspot music mining method, and compared with the previous embodiments, the embodiment further explains and optimizes the technical scheme. Specifically, the method comprises the following steps:

referring to fig. 3, a flowchart of another method for mining hot music according to an embodiment of the present application is shown in fig. 3, and includes:

s201: determining a seed user in all users of the music platform;

s202: distributing corresponding weight to each type of operation behavior data, and constructing a network topology based on the operation behavior data of the seed user and the weight corresponding to each type of operation behavior data;

in this embodiment, a corresponding weight may be assigned to each type of operation behavior data, for example, the played weight is greater than the ordinary played weight, the shared weight is greater than the comment weight, and a specific weight assignment criterion may be flexibly set by a person skilled in the art according to an actual situation. And constructing a network topology based on the operation behavior data of the seed user and the weight corresponding to each type of operation behavior data, namely each edge in the network topology has a corresponding weight.

S203: traversing the network topology according to the weight corresponding to each type of operation behavior data to obtain a music sequence to be mined, and performing feature representation on the music sequence to be mined to obtain a first feature vector;

in the step, traversing is performed based on the weight of each edge in the network topology to obtain a music sequence to be mined, and feature representation is performed on the music sequence to be mined to construct a first feature vector.

S204: determining music to be mined based on the music sequence to be mined, and constructing a second feature vector based on the basic information of the music to be mined;

s205: splicing the first feature vector and the second feature vector to obtain a target feature vector of the music to be mined;

s206: and inputting the target characteristic vector into a trained neural network model to obtain a prediction result of potential hotspot music.

Therefore, different weights are distributed to different types of operation behavior data, the operation behaviors of the seed users can be more accurately represented by the network topology with the weights, more accurate target feature vectors can be obtained by traversing the network topology with the weights, and accordingly the accuracy of potential hotspot music obtained through prediction is higher.

This embodiment introduces a training process of a neural network model, specifically:

referring to fig. 4, a flowchart of a training method of a neural network model according to an embodiment of the present application is shown in fig. 4, and includes:

s301: determining seed users in all users of a music platform, and determining current popular music, current cold music and a cold time period corresponding to the current popular music in the music platform;

in this embodiment, current popular music in the music platform is used as a positive sample, a certain proportion of current cold music in the music platform is randomly extracted as a negative sample to train the neural network model, and the trained neural network model is used for predicting potential popular music.

S302: constructing a training feature vector corresponding to the current popular music based on the operation behavior data of the seed user on the current popular music in the cold time period and the basic information of the current popular music;

s303: constructing a training feature vector corresponding to the current cold music based on the operation behavior data of the seed user on the current cold music and the basic information of the current cold music;

s304: and training a neural network model by taking the training feature vector corresponding to the current popular music as a positive sample and the training feature vector corresponding to the current cold music as a negative sample, wherein the trained neural network model is used for predicting potential hotspot music based on the target feature vector of the music to be mined.

In specific implementation, a training feature vector corresponding to the current popular music, namely a training feature vector of a positive sample, is constructed based on the operation behavior data of the seed user on the current popular music in the cold time period and the basic information of the current popular music. And constructing a training feature vector corresponding to the current cold music, namely a training feature vector of a negative sample, based on the operation behavior data of the seed user on the current cold music and the basic information of the current cold music. And training the neural network model by using the training feature vectors corresponding to the positive sample and the negative sample respectively, so that whether the music to be mined corresponding to the input target feature vector is potential hotspot music or not can be predicted by the trained neural network model.

In the following, a hot music mining device provided by an embodiment of the present application is introduced, and a hot music mining device described below and a hot music mining method described above may be referred to each other.

Referring to fig. 5, a structural diagram of a hotspot music mining device provided in the embodiment of the present application is shown in fig. 5, and includes:

the first construction module 100 is configured to determine a seed user among all users of a music platform, and construct a first feature vector based on operation behavior data of the seed user;

a second constructing module 200, configured to determine music to be mined based on the operation behavior data of the seed user, and construct a second feature vector based on the basic information of the music to be mined;

the splicing module 300 is configured to splice the first feature vector and the second feature vector to obtain a target feature vector of the music to be mined;

and the input module 400 is configured to input the target feature vector into a trained neural network model to obtain a prediction result of potential hotspot music.

According to the hotspot music mining device provided by the embodiment of the application, seed users are firstly screened in the music platform, and because the seed users have strong appreciation capability or great influence on music, the probability that the music to be mined is determined to be potential hotspot music based on the seed operation behavior data is great. Secondly, constructing a target characteristic vector based on the operation behavior data of the music to be mined and the basic information of the music to be mined by the seed user, and inputting the target characteristic vector into the neural network model to obtain potential hotspot music. The target characteristic vector is constructed based on the operation behavior data of the seed user, so that the music has certain propagation degree and detonation point, the target characteristic vector is constructed based on the basic information of the music to be mined, the music making quality is high enough, and the accuracy of mining the hot music can be improved by combining the target characteristic vector and the basic information.

On the basis of the foregoing embodiment, as a preferred implementation manner, the first constructing module 100 is specifically a module that determines current popular music in a music platform and a cold time period corresponding to the current popular music, determines a user who has an operation behavior on the current popular music in the cold time period as a seed user, and constructs a first feature vector based on operation behavior data of the seed user;

on the basis of the foregoing embodiment, as a preferred implementation manner, the first building module 100 is specifically a module that determines high-quality music content in a music platform, determines a user who has an operation behavior on the high-quality music content as a seed user, and builds a first feature vector based on operation behavior data of the seed user;

on the basis of the foregoing embodiment, as a preferred implementation manner, the first constructing module 100 is specifically a module that determines a user with an influence greater than a preset influence in a music platform as a seed user, and constructs a first feature vector based on operation behavior data of the seed user; wherein the influence comprises influence in the music platform and/or influence in other platforms.

On the basis of the above embodiment, as a preferred implementation, the method further includes:

the third construction module is used for constructing the user characteristics of the seed user based on the user information of the seed user and the music preference portrait;

the fourth construction module is used for selecting non-seed training users from the non-seed users and constructing the user characteristics of the non-seed training users based on the user information of the non-seed training users and the music preference portrait;

the first training module is used for training a classification model by using the user characteristics of the seed user and the user characteristics of the non-seed training user;

the prediction module is used for inputting the user information and the music preference portrait of the non-seed users except the non-seed training users into the trained classification model so as to predict the expanded seed users;

and the adding module is used for adding the expanded seed users into the seed users.

On the basis of the above embodiment, as a preferred implementation, the first building block 100 includes:

a determining unit for determining a seed user among all users of the music platform;

the construction unit is used for constructing a network topology based on the operation behavior data of the seed user; each piece of operation behavior data comprises an operation behavior, a seed user executing the operation behavior and operated music, nodes in the network topology represent the seed user executing the operation behavior or the operated music, and edges between the nodes represent the operation behavior;

and the traversing unit is used for traversing the network topology according to a preset rule to obtain a music sequence to be mined, and performing characteristic representation on the music sequence to be mined to obtain a first characteristic vector.

On the basis of the foregoing embodiment, as a preferred implementation manner, the second constructing module 200 is specifically a module that determines music to be mined based on the music sequence to be mined, and constructs a second feature vector based on basic information of the music to be mined.

On the basis of the above embodiment, as a preferred implementation manner, the constructing unit specifically allocates a corresponding weight to each type of operation behavior data, constructs a network topology based on the operation behavior data of the seed user and the weight corresponding to each type of operation behavior data, and determines a unit to be mined for music according to the network topology;

correspondingly, the traversing unit is specifically a unit that traverses the network topology according to the weight corresponding to each type of operation behavior data to obtain a music sequence to be mined, and performs feature representation on the music sequence to be mined to obtain a first feature vector.

On the basis of the foregoing embodiment, as a preferred implementation manner, the input module 400 is specifically a module that inputs the target feature vector into a trained neural network model to obtain a potential value corresponding to each piece of music to be mined, and determines the piece of music to be mined, of which the potential value is greater than a preset potential value, as potential hotspot music.

On the basis of the above embodiment, as a preferred implementation, the method further includes:

the determining module is used for determining seed users in all users of the music platform and determining current popular music, current cold music and a cold time period corresponding to the current popular music in the music platform;

a fifth construction module, configured to construct a training feature vector corresponding to the current popular music based on the operation behavior data of the seed user on the current popular music in the cold time period and the basic information of the current popular music;

a sixth construction module, configured to construct a training feature vector corresponding to the current cold music based on the operation behavior data of the seed user on the current cold music and the basic information of the current cold music;

and the second training module is used for training a neural network model by taking the training feature vector corresponding to the current popular music as a positive sample and the training feature vector corresponding to the current cold music as a negative sample, and the trained neural network model is used for predicting potential hotspot music based on the target feature vector of the music to be mined.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

The present application also provides an electronic device, and referring to fig. 6, a structure diagram of an electronic device 60 provided in the embodiment of the present application, as shown in fig. 6, may include a processor 61 and a memory 62.

The processor 61 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 61 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 61 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 61 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 61 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

Memory 62 may include one or more computer-readable storage media, which may be non-transitory. The memory 62 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 62 is at least used for storing a computer program 621, wherein after being loaded and executed by the processor 61, the computer program can implement relevant steps in the hot spot music mining method executed by the server side disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 62 may also include an operating system 622 and data 623, etc., which may be stored in a transient or persistent manner. The operating system 622 may include Windows, Unix, Linux, etc.

In some embodiments, the electronic device 60 may further include a display 63, an input/output interface 64, a communication interface 65, a sensor 66, a power source 67, and a communication bus 68.

Of course, the structure of the electronic device shown in fig. 6 does not constitute a limitation of the electronic device in the embodiment of the present application, and the electronic device may include more or less components than those shown in fig. 6 or some components in combination in practical applications.

In another exemplary embodiment, a computer readable storage medium including program instructions is further provided, and the program instructions when executed by a processor implement the steps of the hotspot music mining method executed by the electronic device of any one of the above embodiments.

The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.

It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.