CN115329203A - Content recommendation method and apparatus - Google Patents

Abstract

The application discloses a content recommendation method and device, which are used in the field of big data and comprise the following steps: acquiring first user identity information provided inside a content recommendation platform and second user identity information provided by an external platform except the content recommendation platform, wherein the first user identity information and the second user identity information comprise different identity information aiming at the same user; determining a target user according to the first user identity information and the second user identity information; and recommending the content corresponding to the content recommendation platform to the target user. Therefore, the target user is determined according to the information provided by the internal platform and the information provided by the external platform, and then content recommendation is performed on the target user, wherein the internal information and the external information are complementary, and the phenomenon that the possessed user information is ambiguous and has a high deletion rate is avoided, so that the success rate of content recommendation is improved.

Description

A content recommendation method and device

technical field

The present application relates to the field of big data, in particular to a content recommendation method and device.

Background technique

In terms of product content recommendation, accurate and refined operation of existing customers is one of the important stages of customer management. The traditional method of product content recommendation is to reach out through simple and rude forms such as direct telemarketing and agent contact. This method will cause waste of resources, high costs, and scattered customer information.

And the current content recommendation method is to use the internal information of the content recommendation platform to recommend content to users. Since the internal information of the content recommendation platform is purely used to recommend content to customers, the customer information will be unclear and have a high missing rate. As a result, it has the disadvantage of low content recommendation success rate.

Therefore, how to improve the success rate of content recommendation is a key issue concerned by those skilled in the art.

Contents of the invention

Based on the above problems, the present application provides a content recommendation method and device to improve the success rate of content recommendation. The embodiment of the application discloses the following technical solutions:

In the first aspect, the present application discloses a content recommendation method, including:

Acquiring first user identity information provided inside the content recommendation platform and second user identity information provided by external platforms other than the content recommendation platform, where the first user identity information and the second user identity information include Different identity information of users;

determining a first target user according to the first user identity information and the second user identity information;

Content corresponding to the content recommendation platform is recommended to the first target user.

Optionally, the determining the first target user according to the first user identity information and the second user identity information includes:

generating user sets corresponding to different asset classes and user sets corresponding to different age groups according to the first user identity information and the second user identity information;

The first target user is determined according to the user sets corresponding to the different asset levels and the user sets corresponding to the different age groups.

Optionally, also include:

Obtain the user's purchased content information and the user's telephone communication information provided by the content recommendation platform;

determining the feature data type corresponding to the required feature data information based on the user purchase content information and the user telephone communication information;

Acquiring user feature data information corresponding to the feature data type provided by the external platform.

Optionally, also include:

Acquire the user characteristic data information, and determine a sample purchase tag corresponding to the user characteristic data information according to the user purchase content information, and the sample purchase tag is used to identify whether the user corresponding to the user characteristic data information purchases the Content recommended by the content recommendation platform;

Input the user characteristic data information into the initial user purchase rate model to obtain pending purchase tags;

According to the difference between the sample purchase tags and the pending purchase tags, the initial user purchase rate model is adjusted to obtain the user purchase rate model.

Optionally, also include:

Obtain user characteristic data information, and determine a sample telephone communication label corresponding to the user characteristic data information according to the user telephone communication information, and the sample telephone communication label is used to identify whether the user corresponding to the user characteristic data information is connected to the The phone number dialed by the content recommendation platform;

Inputting the user characteristic data information into the initial user telephone communication rate model to obtain a pending telephone communication label;

According to the difference between the sample telephone communication label and the pending telephone communication label, the initial user telephone communication rate model is adjusted to obtain the user telephone communication rate model.

Optionally, also include:

Obtain the target characteristic data information of the user to be analyzed;

Input the target feature data information into the user purchase rate model to generate the probability that the user to be analyzed purchases the content recommended by the content recommendation platform;

The target feature data information is input into the user telephone communication rate model to generate the probability that the user to be analyzed connects to the call made by the content recommendation platform.

Optionally, also include:

In response to the probability that the user to be analyzed purchases the content recommended by the content recommendation platform and the probability that the user to be analyzed is connected to the call made by the content recommendation platform are both greater than a first threshold, the user to be analyzed is determined to be Second target user;

Content corresponding to the content recommendation platform is recommended to the second target user.

In a second aspect, the present application discloses a content recommendation device, including:

An acquisition module, configured to acquire the first user identity information provided inside the content recommendation platform and the second user identity information provided by an external platform other than the content recommendation platform, the first user identity information and the second user identity information Identity information includes different identity information for the same user;

A determining module, configured to determine a first target user according to the first user identity information and the second user identity information;

A recommendation module, configured to recommend content corresponding to the content recommendation platform to the first target user.

Optionally, the determination module includes:

A generating module, configured to generate user sets corresponding to different asset classes and user sets corresponding to different age groups according to the first user identity information and the second user identity information;

The first determining submodule is configured to determine the first target user according to the user sets corresponding to the different asset classes and the user sets corresponding to the different age groups.

Optionally, also include:

The first obtaining sub-module is used to obtain the user's purchased content information and the user's telephone communication information provided by the content recommendation platform;

The second determining submodule is used to determine the characteristic data type corresponding to the required characteristic data information based on the user purchase content information and the user telephone communication information;

The second obtaining sub-module is used to obtain user characteristic data information corresponding to the characteristic data type provided by the external platform.

Optionally, also include:

The third acquisition sub-module is used to acquire the user characteristic data information, determine the sample purchase tag corresponding to the user characteristic data information according to the user purchase content information, and the sample purchase tag is used to identify the user characteristic data information Whether the corresponding user purchases the content recommended by the content recommendation platform;

The first obtaining module is used to input the user characteristic data information into the initial user purchase rate model to obtain pending purchase tags;

The first adjustment module is configured to adjust the initial user purchase rate model according to the difference between the sample purchase tags and the pending purchase tags to obtain the user purchase rate model.

Optionally, also include:

The fourth acquisition sub-module is used to acquire user characteristic data information, and determine a sample telephone communication label corresponding to the user characteristic data information according to the user telephone communication information, and the sample telephone communication label is used to identify the user characteristic data information Whether the corresponding user connects to the call made by the content recommendation platform;

The second obtaining module is used to input the user characteristic data information into the initial user telephone communication rate model, and obtain the pending telephone communication label;

The second adjustment module is configured to adjust the initial user telephone communication rate model according to the difference between the sample telephone communication label and the pending telephone communication label, so as to obtain the user telephone communication rate model.

Optionally, also include:

The fifth acquisition sub-module is used to acquire the target characteristic data information of the user to be analyzed;

The first generation module is used to input the target feature data information into the user purchase rate model to generate the probability that the user to be analyzed purchases the content recommended by the content recommendation platform;

The second generating module is configured to input the target feature data information into the user telephone communication rate model, and generate the probability that the user to be analyzed connects to the call made by the content recommendation platform.

Optionally, also include:

The third determination submodule is configured to respond to the probability that the user to be analyzed purchases the content recommended by the content recommendation platform and the probability that the user to be analyzed connects to the call made by the content recommendation platform are greater than a first threshold, determining the user to be analyzed as a second target user;

The first sub-recommendation module is configured to recommend content corresponding to the content recommendation platform to the second target user.

Compared with the prior art, the present application has the following beneficial effects:

The application first obtains the first user identity information provided by the content recommendation platform and the second user identity information provided by an external platform other than the content recommendation platform, the first user identity information and the second user identity information Including different identity information for the same user, then determining a target user according to the first user identity information and the second user identity information, and finally recommending content corresponding to the content recommendation platform to the target user. In this way, the application determines the target users based on the information provided by the internal platform and the information provided by the external platform, and then recommends content to the target users. The internal information and external information complement each other, and the user information owned will not be unclear, The phenomenon of high missing rate improves the success rate of content recommendation.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

FIG. 1 is a flow chart of a content recommendation method provided in an embodiment of the present application;

FIG. 2 is a flow chart of another content recommendation method provided by the embodiment of the present application;

FIG. 3 is a schematic structural diagram of a content recommendation device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of this application.

It should be noted that the content recommendation method and device provided in this application are used in the field of big data. The above are examples only, and do not limit the application fields of the method and device names provided in this application.

As described above, the current content recommendation method is to use the internal information of the content recommendation platform to recommend content to users. Since the internal information of the content recommendation platform is purely used to recommend content to customers, the customer information will be unclear and have a high missing rate. phenomenon, leading to the disadvantage of low content recommendation success rate. Therefore, how to improve the success rate of content recommendation is a key issue concerned by those skilled in the art.

Therefore, the inventor proposes the technical solution of this application. This application first obtains the first user identity information provided inside the content recommendation platform and the second user identity information provided by an external platform other than the content recommendation platform. The first user The identity information and the second user identity information include different identity information for the same user, then determine the target user according to the first user identity information and the second user identity information, and finally recommend the content to the target user Recommend content corresponding to the platform. In this way, the application determines the target users based on the information provided by the internal platform and the information provided by the external platform, and then recommends content to the target users. The internal information and external information complement each other, and the user information owned will not be unclear, The phenomenon of high missing rate improves the success rate of content recommendation.

The method provided in the embodiment of the present application may be executed by software on the terminal device. The terminal device may be, for example, a mobile phone, a tablet computer, a computer and other devices. The software can be, for example, system software.

In order to enable those skilled in the art to better understand the solution of the present application, the present application will be further described in detail below in conjunction with the drawings and specific implementation methods.

method embodiment

A method for recommending content provided by the present application will be described below through an embodiment.

Referring to FIG. 1, this figure is a flow chart of a content recommendation method provided in the embodiment of the present application. As shown in FIG. 1, the method may include:

S101: Obtain first user identity information provided by a content recommendation platform and second user identity information provided by an external platform other than the content recommendation platform.

In this step, the first user identity information and the second user identity information include different identity information for the same user, that is to say, there is complementarity between the first user identity information and the second user identity information, so that the identity of the same user The information is as complete as possible. Wherein, the first identity user information is provided by the internal recommendation platform, and the second user identity information is provided by an external platform other than the content recommendation platform. It should also be noted that after the first user identity information and the second user identity information are acquired, the data cleaning operation is performed.

S102: Determine a first target user according to the first user identity information and the second user identity information.

In this step, the first user identity information includes user basic information (such as age, whether there are children, whether married and elders, etc.), and the second user identity information also includes user basic information (such as age, whether there are children, whether married or not) and elders and other information), and then combine the first user identity information and the second user identity information to obtain the end user identity information, which is used to determine the target user, thereby enriching the user portrait and improving the content recommendation Success rate.

Further, user sets corresponding to different asset levels and user sets corresponding to different age groups are generated according to the first user identity information and the second user identity information, and then determined according to the user sets corresponding to different asset levels and the user sets corresponding to different age groups Target users. It can be understood that the asset level corresponding to the first user identity information is compared with the asset level corresponding to the second user identity information, and the largest asset level is selected as the user asset level; when the age corresponding to the first user identity information If the information is missing, the age information corresponding to the second user identity information is selected to be supplemented to obtain the age information.

S103: Recommend content corresponding to the content recommendation platform to the first target user.

In this step, recommend the content corresponding to the platform based on the determined target user recommendation content. Since the target user is determined based on internal and external information, the integrity of user information is high, and the success rate of content recommendation will also increase accordingly. .

It can be seen that this optional solution mainly explains how to determine target users and recommend content to them. Specifically, in this optional solution, the application first obtains the first user identity information provided inside the content recommendation platform and the second user identity information provided by an external platform other than the content recommendation platform, and the first user The identity information and the second user identity information include different identity information for the same user, then determine the target user according to the first user identity information and the second user identity information, and finally recommend the content to the target user Recommend content corresponding to the platform.

To sum up, in this embodiment, the target user is determined according to the information provided by the internal platform and the information provided by the external platform, and then content recommendations are made to the target user. The internal information and the external information complement each other, and the owned user information will not There is ambiguity and high missing rate, which improves the success rate of content recommendation.

Referring to FIG. 2, this figure is a flow chart of another content recommendation method provided in the embodiment of the present application. As shown in FIG. 2, the method may include:

S201: Obtain the user's purchased content information and the user's telephone communication information provided by the content recommendation platform.

In this step, the user purchase content information and user telephone communication information provided by the content recommendation platform are also obtained. The user purchase content information includes user purchased content information and user unpurchased content information, and user telephone communication information includes user connected phone information and The phone is not connected to the call information.

S202: Determine the feature data type corresponding to the required feature data information based on the user purchase content information and the user telephone communication information.

In this step, determine the feature data type corresponding to the required feature data information according to the user's purchased content information, for example, whether the user blocks the recommended content received by the mobile phone number corresponding to the user; determine the required feature data according to the user's phone communication information The characteristic data type corresponding to the information, for example, the active time period of the mobile phone number corresponding to the user.

S203: Obtain user feature data information corresponding to the feature data type provided by the external platform.

In this step, the user characteristic data information corresponding to the characteristic data type is acquired on the external platform, and the user characteristic data information includes the active time period of the mobile phone number corresponding to the user and the content blocked by the mobile phone number corresponding to the user. In this way, based on the active time period, the content recommendation platform knows in which time period calls to the user are easy to be connected, and the content recommendation platform knows which content to recommend to the user is not easy to be rejected based on the blocked content.

As an implementable implementation, the model is trained according to the user's purchase content information, user telephone communication information, and user characteristic data information. The model can be a secureboost model, which is not specifically limited here. Among them, a private channel will be established between the external platform and the internal platform to obtain user characteristic data information to ensure the security of user characteristic data information. Specifically, first determine the sample purchase tag corresponding to the user characteristic data information according to the user purchase content information, the sample purchase tag is used to identify whether the user corresponding to the user characteristic data information purchases the content recommended by the content recommendation platform, and then the user characteristic data information Input it into the initial user purchase rate model to obtain pending purchase tags, and finally adjust the initial user purchase rate model according to the difference between the sample purchase tags and pending purchase tags to obtain the user purchase rate model.

As another achievable implementation, first obtain the user characteristic data information, determine the sample telephone communication label corresponding to the user characteristic data information according to the user telephone communication information, and the sample telephone communication label is used to identify whether the user corresponding to the user characteristic data information is Connect the call made by the content recommendation platform, and then input the user characteristic data information into the initial user telephone communication rate model to obtain the pending telephone communication label, and finally adjust the initial telephone communication label according to the difference between the sample telephone communication label and the pending telephone communication label. The user telephone communication rate model is used to obtain the user telephone communication rate model. It also needs to be explained that the predictive ability of the user purchase rate model and the user telephone communication rate model is evaluated by using AUC, KS and lift index.

Further, first obtain the target characteristic data information corresponding to the user to be analyzed, and then input the target characteristic data information into the user purchase rate model to generate the probability that the user to be analyzed will purchase the content recommended by the content recommendation platform, and input the target characteristic data information Input it into the user telephone communication rate model to generate the probability that the user to be analyzed will connect to the call made by the content recommendation platform. Finally, when the probability of the user to be analyzed to purchase the content recommended by the content recommendation platform and the probability of the user to be analyzed to connect to the call made by the content recommendation platform When the probabilities of are greater than the first threshold, the user to be analyzed is determined as the target user.

It can be seen that this optional solution mainly explains how to determine target users and recommend content to them. Specifically, in this optional solution, the application first obtains the user purchase content information and user telephone communication information provided by the content recommendation platform, and then determines the required features based on the user purchase content information and the user telephone communication information The characteristic data type corresponding to the data information, and finally obtain the user characteristic data information provided by the external platform and corresponding to the characteristic data type.

In summary, in this application, the target users determined based on the first user identity information, the second user identity information, the user purchase rate model, and the user telephone communication rate model are more reliable, and the success rate of content recommendation will be higher. .

Device embodiment

A content recommendation device provided in an embodiment of the present application is introduced below. The content recommendation device described below and the content recommendation method described above may be referred to in correspondence.

Referring to FIG. 3, this figure is a schematic structural diagram of a content recommendation device provided in an embodiment of the present application. As shown in FIG. 3, the device may include:

The acquisition module 100 is configured to acquire first user identity information provided inside the content recommendation platform and second user identity information provided by an external platform other than the content recommendation platform, the first user identity information and the second user identity information User identity information includes different identity information for the same user;

A determining module 200, configured to determine a first target user according to the first user identity information and the second user identity information;

The recommendation module 300 is configured to recommend content corresponding to the content recommendation platform to the first target user.

Optionally, the determining module 200 includes:

A generating module, configured to generate user sets corresponding to different asset classes and user sets corresponding to different age groups according to the first user identity information and the second user identity information;

The first determining submodule is configured to determine the first target user according to the user sets corresponding to the different asset classes and the user sets corresponding to the different age groups.

Optionally, also include:

The first obtaining sub-module is used to obtain the user's purchased content information and the user's telephone communication information provided by the content recommendation platform;

The second determining submodule is used to determine the characteristic data type corresponding to the required characteristic data information based on the user purchase content information and the user telephone communication information;

The second obtaining sub-module is used to obtain user characteristic data information corresponding to the characteristic data type provided by the external platform.

Optionally, also include:

The third acquisition sub-module is used to acquire the user characteristic data information, determine the sample purchase tag corresponding to the user characteristic data information according to the user purchase content information, and the sample purchase tag is used to identify the user characteristic data information Whether the corresponding user purchases the content recommended by the content recommendation platform;

The first obtaining module is used to input the user characteristic data information into the initial user purchase rate model to obtain pending purchase tags;

The first adjustment module is configured to adjust the initial user purchase rate model according to the difference between the sample purchase tags and the pending purchase tags to obtain the user purchase rate model.

Optionally, also include:

The fourth acquisition sub-module is used to acquire user characteristic data information, and determine a sample telephone communication label corresponding to the user characteristic data information according to the user telephone communication information, and the sample telephone communication label is used to identify the user characteristic data information Whether the corresponding user connects to the call made by the content recommendation platform;

The second obtaining module is used to input the user characteristic data information into the initial user telephone communication rate model, and obtain the pending telephone communication label;

The second adjustment module is configured to adjust the initial user telephone communication rate model according to the difference between the sample telephone communication label and the pending telephone communication label, so as to obtain the user telephone communication rate model.

Optionally, also include:

The fifth acquisition sub-module is used to acquire the target characteristic data information of the user to be analyzed;

The first generation module is used to input the target feature data information into the user purchase rate model to generate the probability that the user to be analyzed purchases the content recommended by the content recommendation platform;

The second generating module is configured to input the target feature data information into the user telephone communication rate model, and generate the probability that the user to be analyzed connects to the call made by the content recommendation platform.

Optionally, also include:

The third determination submodule is configured to respond to the probability that the user to be analyzed purchases the content recommended by the content recommendation platform and the probability that the user to be analyzed connects to the call made by the content recommendation platform are greater than a first threshold, determining the user to be analyzed as a second target user;

The first sub-recommendation module is configured to recommend content corresponding to the content recommendation platform to the second target user. .

The content recommendation device provided by the embodiment of the present application has the same beneficial effect as the content recommendation method provided by the above embodiment, so details are not repeated here.

It should be noted that the "first" and "second" in the names of "first" and "second" (if they exist) mentioned in the embodiments of this application are only used for name identification and do not represent the order First and second on the list.

Each embodiment in the description is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the related information, please refer to the description of the method part.

Professionals can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the possible For interchangeability, in the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be directly implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known storage medium.

The content recommendation method and device provided in the present application have been introduced in detail above. In this paper, specific examples are used to illustrate the principles and implementation methods of the present application, and the descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. It should be pointed out that those skilled in the art can make some improvements and modifications to the application without departing from the principles of the application, and these improvements and modifications also fall within the protection scope of the claims of the application.

Claims (10)

1. A content recommendation method, characterized in that, comprising: Obtaining first user identity information provided inside the content recommendation platform and second user identity information provided by an external platform other than the content recommendation platform, where the first user identity information and the second user identity information include Different identity information of users; determining a first target user according to the first user identity information and the second user identity information; Content corresponding to the content recommendation platform is recommended to the first target user. 2. The method according to claim 1, wherein the determining the first target user according to the first user identity information and the second user identity information comprises: generating user sets corresponding to different asset classes and user sets corresponding to different age groups according to the first user identity information and the second user identity information; The first target user is determined according to the user sets corresponding to the different asset levels and the user sets corresponding to the different age groups. 3. The method according to claim 1, further comprising: Obtain the user's purchased content information and the user's telephone communication information provided by the content recommendation platform; determining the feature data type corresponding to the required feature data information based on the user purchase content information and the user telephone communication information; Acquiring user feature data information corresponding to the feature data type provided by the external platform. 4. The method according to claim 3, further comprising: Acquire the user characteristic data information, and determine a sample purchase tag corresponding to the user characteristic data information according to the user purchase content information, and the sample purchase tag is used to identify whether the user corresponding to the user characteristic data information purchases the Content recommended by the content recommendation platform; Input the user feature data information into the initial user purchase rate model to obtain pending purchase tags; According to the difference between the sample purchase tags and the pending purchase tags, the initial user purchase rate model is adjusted to obtain the user purchase rate model. 5. The method according to claim 4, further comprising: Obtain user characteristic data information, and determine a sample telephone communication label corresponding to the user characteristic data information according to the user telephone communication information, and the sample telephone communication label is used to identify whether the user corresponding to the user characteristic data information is connected to the The phone number dialed by the content recommendation platform; Inputting the user characteristic data information into the initial user telephone communication rate model to obtain a pending telephone communication label; According to the difference between the sample telephone communication label and the pending telephone communication label, the initial user telephone communication rate model is adjusted to obtain the user telephone communication rate model. 6. The method according to claim 4, further comprising: Obtain the target characteristic data information of the user to be analyzed; Input the target feature data information into the user purchase rate model to generate the probability that the user to be analyzed purchases the content recommended by the content recommendation platform; The target feature data information is input into the user telephone communication rate model to generate the probability that the user to be analyzed connects to the call made by the content recommendation platform. 7. The method according to claim 6, further comprising: In response to the probability that the user to be analyzed purchases the content recommended by the content recommendation platform and the probability that the user to be analyzed is connected to the call made by the content recommendation platform are both greater than a first threshold, the user to be analyzed is determined to be Second target user; Content corresponding to the content recommendation platform is recommended to the second target user. 8. A content recommendation device, characterized in that it comprises: An acquisition module, configured to acquire the first user identity information provided inside the content recommendation platform and the second user identity information provided by an external platform other than the content recommendation platform, the first user identity information and the second user identity information Identity information includes different identity information for the same user; A determining module, configured to determine a first target user according to the first user identity information and the second user identity information; A recommendation module, configured to recommend content corresponding to the content recommendation platform to the first target user. 9. The device according to claim 8, wherein the determining module comprises: A generating module, configured to generate user sets corresponding to different asset classes and user sets corresponding to different age groups according to the first user identity information and the second user identity information; The first determining submodule is configured to determine the first target user according to the user sets corresponding to the different asset classes and the user sets corresponding to the different age groups. 10. The device according to claim 8, further comprising: The first obtaining sub-module is used to obtain the user's purchased content information and the user's telephone communication information provided by the content recommendation platform; The second determining submodule is used to determine the characteristic data type corresponding to the required characteristic data information based on the user purchase content information and the user telephone communication information; The second obtaining sub-module is used to obtain user characteristic data information corresponding to the characteristic data type provided by the external platform.

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