CN116595253A - Data recommendation method and device - Google Patents
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- CN116595253A CN116595253A CN202310553216.XA CN202310553216A CN116595253A CN 116595253 A CN116595253 A CN 116595253A CN 202310553216 A CN202310553216 A CN 202310553216A CN 116595253 A CN116595253 A CN 116595253A
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Abstract
The application provides a data recommendation method and a data recommendation device, wherein the method comprises the following steps: acquiring a data set to be recommended; scoring the data set to be recommended through a preset recommendation model to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended; adjusting the recommendation score through a pre-configured scoring regulator to obtain a target score of each piece of data to be recommended in the data set to be recommended; determining target recommendation data from the data set to be recommended according to the target score; and pushing the data according to the target recommended data. Therefore, the method and the device can dynamically adjust the recommended result proportion, balance the recommended proportion, are beneficial to improving the fault tolerance rate, and have good applicability.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a data recommendation method and device.
Background
At present, with the increase of data scenes, how to recommend proper data to users becomes one of the main problems in the financial field. Existing data recommendation methods typically use recommendation models to make data recommendations. However, in practice, it is found that in some online exposure scenes (such as financial advertisements and financial products), the fault tolerance is low by simply relying on the recommended model results, and the exposure proportion of different recommended financial products is easily unbalanced. Therefore, the existing method has low applicability and fault tolerance, and the recommended proportion cannot be balanced.
Disclosure of Invention
The embodiment of the application aims to provide a data recommendation method and device, which can dynamically adjust the recommendation result proportion, balance the recommendation proportion, be beneficial to improving the fault tolerance and have good applicability.
An embodiment of the present application provides a data recommendation method, including:
acquiring a data set to be recommended;
scoring the data set to be recommended through a preset recommendation model to obtain recommendation scores of each piece of data to be recommended in the data set to be recommended;
the recommendation score is adjusted through a pre-configured scoring regulator, and a target score of each piece of data to be recommended in the data set to be recommended is obtained;
determining target recommendation data from the data set to be recommended according to the target score;
and carrying out data pushing according to the target recommended data.
In the implementation process, the method can obtain the data set to be recommended preferentially; then scoring the data set to be recommended through a preset recommendation model to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended; the recommendation score is adjusted through a pre-configured scoring regulator, and the target score of each piece of data to be recommended in the data set to be recommended is obtained; then, determining target recommendation data from the data set to be recommended according to the target score; and finally, carrying out data pushing according to the target recommended data. Therefore, the method can dynamically adjust the recommended result proportion, balances the recommended proportion, is favorable for improving the fault tolerance and has good applicability.
Further, before the acquiring the data set to be recommended, the method further includes:
constructing a PID regulator;
acquiring preset control proportion parameters and historical scoring data;
determining an adjustment amplitude parameter according to the historical scoring data;
and configuring the PID regulator according to the control proportion parameter and the regulation amplitude parameter to obtain a scoring regulator.
Further, the adjusting the recommendation score by a preconfigured scoring adjustor to obtain a target score of each data to be recommended in the data set to be recommended includes:
comparing the recommended score with the control proportion parameter to obtain a comparison result;
and adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score.
Further, the step of adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score includes:
when the recommendation score is determined to be increased and adjusted according to the comparison result, an increase amplitude parameter is determined according to the adjustment amplitude parameter;
and increasing and adjusting the recommended score according to the increasing amplitude parameter to obtain an adjusted target score.
Further, the step of adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score includes:
when determining that the recommended score needs to be reduced and adjusted according to the comparison result, determining a reduction amplitude parameter according to the adjustment amplitude parameter;
and reducing and adjusting the recommended score according to the reduction amplitude parameter to obtain an adjusted target score.
A second aspect of an embodiment of the present application provides a data recommendation device, including:
the acquisition unit is used for acquiring the data set to be recommended;
the scoring unit is used for scoring the data set to be recommended through a preset recommendation model to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended;
the adjustment unit is used for adjusting the recommendation score through a pre-configured scoring regulator to obtain a target score of each piece of data to be recommended in the data set to be recommended;
the data determining unit is used for determining target recommendation data from the data set to be recommended according to the target score;
and the pushing unit is used for pushing the data according to the target recommended data.
In the implementation process, the data recommending device can acquire the data set to be recommended through an acquiring unit; scoring the data set to be recommended through a preset recommendation model by a scoring unit to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended; the recommendation score is adjusted through a pre-configured scoring adjuster through an adjusting unit, and a target score of each piece of data to be recommended in the data set to be recommended is obtained; determining target recommendation data from the data set to be recommended according to the target score by a data determining unit; and then carrying out data pushing according to the target recommended data by a pushing unit. Therefore, the data recommendation device can dynamically adjust the recommendation result proportion, balance the recommendation proportion, and is favorable for improving the fault tolerance rate and good in applicability.
Further, the data recommendation device further includes:
the construction unit is used for constructing a PID regulator before the data set to be recommended is acquired;
the acquisition unit is also used for acquiring preset control proportion parameters and historical scoring data;
a parameter determining unit for determining an adjustment amplitude parameter according to the historical scoring data;
and the configuration unit is used for configuring the PID regulator according to the control proportion parameter and the regulating amplitude parameter to obtain a scoring regulator.
Further, the adjusting unit includes:
the comparison subunit is used for comparing the recommended score with the control proportion parameter to obtain a comparison result;
and the adjustment subunit is used for adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score.
Further, the adjustment subunit includes:
the determining module is used for determining an increase amplitude parameter according to the adjustment amplitude parameter when determining that the recommended score needs to be increased and adjusted according to the comparison result;
and the adjusting module is used for increasing and adjusting the recommended score according to the increasing amplitude parameter to obtain an adjusted target score.
Further, the adjustment subunit includes:
the determining module is further configured to determine a reduction amplitude parameter according to the adjustment amplitude parameter when it is determined that the recommendation score needs to be reduced and adjusted according to the comparison result;
and the adjusting module is also used for reducing and adjusting the recommended score according to the reducing amplitude parameter to obtain an adjusted target score.
A third aspect of the embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the data recommendation method according to any one of the first aspect of the embodiment of the present application.
A fourth aspect of the embodiments of the present application provides a computer readable storage medium storing computer program instructions which, when read and executed by a processor, perform the data recommendation method according to any one of the first aspect of the embodiments of the present application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a data recommendation method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating another data recommendation method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a data recommendation device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another data recommendation device according to an embodiment of the present application;
fig. 5 is an exemplary schematic diagram of a PID regulator according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a data recommendation method according to the present embodiment. The data recommendation method comprises the following steps:
s101, acquiring a data set to be recommended.
S102, scoring the data set to be recommended through a preset recommendation model to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended.
And S103, adjusting the recommendation score through a pre-configured scoring regulator to obtain a target score of each piece of data to be recommended in the data set to be recommended.
S104, determining target recommendation data from the data set to be recommended according to the target score.
S105, data pushing is carried out according to the target recommendation data.
In this embodiment, the application proposes a method for real-time intervention of a PID control regulator after model scoring results aiming at the requirements of different proportions of on-line exposure of a recommended model. The flow of the method can refer to the following relation: recommendation model scoring → PID regulator → output scoring.
Specifically, the recommendation system can perform normal scoring according to a preset recommendation model and output the score to the PID regulator; then, the PID regulator adjusts the scoring result according to the scoring result, the control proportion requirement and other contents to obtain an adjusted scoring result; and finally, outputting the scoring result after adjustment.
In this embodiment, the method may be applied to the financial domain, and in particular, to a recommendation scenario of financial domain data. For example, the method can evaluate the financial products to be recommended, so as to obtain the financial products with better evaluation results, and then push the financial products, so that the pushing effect of the financial products is better. Similarly, for advertisements in the financial field, the method can evaluate the advertisements and push better financial advertisements, so that the effect of popularization of financial products is achieved.
In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, which is not limited in this embodiment.
In this embodiment, the execution body of the method may be an intelligent device such as a smart phone or a tablet computer, which is not limited in this embodiment.
Therefore, by implementing the data recommendation method described in the embodiment, the PID automatic control algorithm can be integrated into the online recommendation model, so that the effects of dynamically adjusting the proportion of the recommendation result and optimizing the scoring of the model are achieved. Meanwhile, the requirement of business personnel on proportion control can be met, so that the mobility of main view adjustment is greatly enhanced, and the fault tolerance is also improved.
Example 2
Referring to fig. 2, fig. 2 is a flowchart of a data recommendation method according to the present embodiment. The data recommendation method comprises the following steps:
s201, constructing a PID regulator.
S202, acquiring preset control proportion parameters and historical scoring data.
S203, determining an adjustment amplitude parameter according to the historical scoring data.
S204, configuring the PID regulator according to the control proportion parameter and the regulation amplitude parameter to obtain the scoring regulator.
S205, acquiring a data set to be recommended.
S206, scoring the data set to be recommended through a preset recommendation model to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended.
S207, comparing the recommended score with the control proportion parameter to obtain a comparison result.
And S208, adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score.
As an optional implementation manner, the recommended score is adjusted according to the comparison result and the adjustment amplitude parameter, so as to obtain an adjusted target score, which includes:
when determining that the recommended score needs to be increased and adjusted according to the comparison result, determining an increase amplitude parameter according to the adjustment amplitude parameter;
and increasing and adjusting the recommended score according to the increasing amplitude parameter to obtain an adjusted target score.
In this embodiment, the above-described increase amplitude parameter may be adjusted by the following integral I; wherein, the control proportion P, the integral I and the differential D are three major parameters of the PID algorithm.
As an optional implementation manner, the recommended score is adjusted according to the comparison result and the adjustment amplitude parameter, so as to obtain an adjusted target score, which includes:
when determining that the recommended score needs to be reduced and adjusted according to the comparison result, determining a reduction amplitude parameter according to the adjustment amplitude parameter;
and reducing and adjusting the recommended score according to the reducing amplitude parameter to obtain an adjusted target score.
In this embodiment, the above-described reduction amplitude parameter may be adjusted by the following differential D; wherein, the control proportion P, the integral I and the differential D are three major parameters of the PID algorithm.
S209, determining target recommendation data from the data set to be recommended according to the target score.
S210, data pushing is carried out according to the target recommendation data.
For example, the PID regulator has the following relationship with the original scoring result, the control scale parameter, the historical scoring data, and the adjustment scoring result:
original scoring result- & gt PID algorithm;
controlling a proportion parameter-PID algorithm;
historical scoring data- & gt PID algorithm;
PID algorithm→adjusting scoring result.
The relationship between the PID regulator and the original scoring result, the control scale parameter, the historical scoring data, and the adjustment scoring result can be seen with reference to fig. 5. It can be seen from this that the PID algorithm has three main parameters: the control ratio P, integral I, derivative D.
Specifically, the control proportion parameter is a switch which is configured independently and is mainly used for adjusting, namely the parameter P in the PID algorithm; parameters I and P of the PID algorithm are the magnitude of the adjustment, mainly by reference to historical scoring data.
An example flow of the algorithm is as follows: when scoring is carried out each time, the existing scoring data is compared with the target control proportion P, if the scoring proportion of a certain class is relatively low, the scoring result of the certain class is increased, the increasing amplitude is regulated by I, and conversely, if the scoring proportion of the certain class is too high, the scoring is regulated by D. It can be seen that the original scoring result is the new result score after being adjusted by the adjuster, and the result score is output by the method.
In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, which is not limited in this embodiment.
In this embodiment, the execution body of the method may be an intelligent device such as a smart phone or a tablet computer, which is not limited in this embodiment.
Therefore, by implementing the data recommendation method described in the embodiment, the PID automatic control algorithm can be integrated into the online recommendation model, so that the effects of dynamically adjusting the proportion of the recommendation result and optimizing the scoring of the model are achieved. Meanwhile, the requirement of business personnel on proportion control can be met, so that the mobility of main view adjustment is greatly enhanced, and the fault tolerance is also improved.
Example 3
Referring to fig. 3, fig. 3 is a schematic structural diagram of a data recommendation device according to the present embodiment. As shown in fig. 3, the data recommendation device includes:
an obtaining unit 310, configured to obtain a data set to be recommended;
the scoring unit 320 is configured to score the to-be-recommended data set through a preset recommendation model, so as to obtain a recommendation score of each to-be-recommended data in the to-be-recommended data set;
the adjusting unit 330 is configured to adjust the recommendation score through a pre-configured scoring adjuster, so as to obtain a target score of each piece of data to be recommended in the data set to be recommended;
a data determining unit 340, configured to determine target recommendation data from the data set to be recommended according to the target score;
and the pushing unit 350 is configured to perform data pushing according to the target recommendation data.
In this embodiment, the explanation of the data recommendation device may refer to the description in embodiment 1 or embodiment 2, and the description is not repeated in this embodiment.
Therefore, by implementing the data recommendation device described in the embodiment, the PID automatic control algorithm can be integrated into the online recommendation model, so that the effects of dynamically adjusting the proportion of the recommendation result and optimizing the scoring of the model are achieved. Meanwhile, the requirement of business personnel on proportion control can be met, so that the mobility of main view adjustment is greatly enhanced, and the fault tolerance is also improved.
Example 4
Referring to fig. 4, fig. 4 is a schematic structural diagram of a data recommendation device according to the present embodiment. As shown in fig. 4, the data recommendation device includes:
an obtaining unit 310, configured to obtain a data set to be recommended;
the scoring unit 320 is configured to score the to-be-recommended data set through a preset recommendation model, so as to obtain a recommendation score of each to-be-recommended data in the to-be-recommended data set;
the adjusting unit 330 is configured to adjust the recommendation score through a pre-configured scoring adjuster, so as to obtain a target score of each piece of data to be recommended in the data set to be recommended;
a data determining unit 340, configured to determine target recommendation data from the data set to be recommended according to the target score;
and the pushing unit 350 is configured to perform data pushing according to the target recommendation data.
As an alternative embodiment, the data recommendation device further includes:
a construction unit 360, configured to construct a PID regulator before acquiring the data set to be recommended;
an obtaining unit 370, configured to obtain preset control proportion parameters and historical scoring data;
a parameter determining unit 380 for determining an adjustment amplitude parameter according to the historical scoring data;
a configuration unit 390 configured to configure the PID regulator according to the control proportion parameter and the adjustment amplitude parameter, resulting in a scoring regulator.
As an alternative embodiment, the adjusting unit 330 includes:
the comparison subunit 331 is configured to compare the recommended score with the control proportion parameter to obtain a comparison result;
the adjustment subunit 332 is configured to adjust the recommendation score according to the comparison result and the adjustment amplitude parameter, to obtain an adjusted target score.
As an alternative embodiment, the adjustment subunit 332 includes:
the determining module is used for determining an increase amplitude parameter according to the adjustment amplitude parameter when determining that the recommended score needs to be increased and adjusted according to the comparison result;
and the adjusting module is used for increasing and adjusting the recommended score according to the increasing amplitude parameter to obtain an adjusted target score.
As an alternative embodiment, the adjustment subunit 332 includes:
the determining module is also used for determining a reduction amplitude parameter according to the adjustment amplitude parameter when determining that the recommended score needs to be subjected to reduction adjustment according to the comparison result;
and the adjusting module is also used for reducing and adjusting the recommended score according to the reduction amplitude parameter to obtain an adjusted target score.
In this embodiment, the explanation of the data recommendation device may refer to the description in embodiment 1 or embodiment 2, and the description is not repeated in this embodiment.
Therefore, by implementing the data recommendation device described in the embodiment, the PID automatic control algorithm can be integrated into the online recommendation model, so that the effects of dynamically adjusting the proportion of the recommendation result and optimizing the scoring of the model are achieved. Meanwhile, the requirement of business personnel on proportion control can be met, so that the mobility of main view adjustment is greatly enhanced, and the fault tolerance is also improved.
An embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute a data recommendation method in embodiment 1 or embodiment 2 of the present application.
An embodiment of the present application provides a computer readable storage medium storing computer program instructions that, when read and executed by a processor, perform the data recommendation method of embodiment 1 or embodiment 2 of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A data recommendation method, comprising:
acquiring a data set to be recommended;
scoring the data set to be recommended through a preset recommendation model to obtain recommendation scores of each piece of data to be recommended in the data set to be recommended;
the recommendation score is adjusted through a pre-configured scoring regulator, and a target score of each piece of data to be recommended in the data set to be recommended is obtained;
determining target recommendation data from the data set to be recommended according to the target score;
and carrying out data pushing according to the target recommended data.
2. The data recommendation method according to claim 1, wherein prior to said acquiring the set of data to be recommended, the method further comprises:
constructing a PID regulator;
acquiring preset control proportion parameters and historical scoring data;
determining an adjustment amplitude parameter according to the historical scoring data;
and configuring the PID regulator according to the control proportion parameter and the regulation amplitude parameter to obtain a scoring regulator.
3. The data recommendation method according to claim 2, wherein the adjusting the recommendation score by a pre-configured scoring adjuster obtains a target score of each data to be recommended in the data set to be recommended, including:
comparing the recommended score with the control proportion parameter to obtain a comparison result;
and adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score.
4. The data recommendation method according to claim 3, wherein said adjusting the recommendation score according to the comparison result and the adjustment amplitude parameter, to obtain an adjusted target score, comprises:
when the recommendation score is determined to be increased and adjusted according to the comparison result, an increase amplitude parameter is determined according to the adjustment amplitude parameter;
and increasing and adjusting the recommended score according to the increasing amplitude parameter to obtain an adjusted target score.
5. The data recommendation method according to claim 3, wherein said adjusting the recommendation score according to the comparison result and the adjustment amplitude parameter, to obtain an adjusted target score, comprises:
when determining that the recommended score needs to be reduced and adjusted according to the comparison result, determining a reduction amplitude parameter according to the adjustment amplitude parameter;
and reducing and adjusting the recommended score according to the reduction amplitude parameter to obtain an adjusted target score.
6. A data recommendation device, characterized in that the data recommendation device comprises:
the acquisition unit is used for acquiring the data set to be recommended;
the scoring unit is used for scoring the data set to be recommended through a preset recommendation model to obtain the recommendation score of each piece of data to be recommended in the data set to be recommended;
the adjustment unit is used for adjusting the recommendation score through a pre-configured scoring regulator to obtain a target score of each piece of data to be recommended in the data set to be recommended;
the data determining unit is used for determining target recommendation data from the data set to be recommended according to the target score;
and the pushing unit is used for pushing the data according to the target recommended data.
7. The data recommendation device of claim 6, further comprising:
the construction unit is used for constructing a PID regulator before the data set to be recommended is acquired;
the acquisition unit is also used for acquiring preset control proportion parameters and historical scoring data;
a parameter determining unit for determining an adjustment amplitude parameter according to the historical scoring data;
and the configuration unit is used for configuring the PID regulator according to the control proportion parameter and the regulating amplitude parameter to obtain a scoring regulator.
8. The data recommendation device of claim 7, wherein the adjustment unit comprises:
the comparison subunit is used for comparing the recommended score with the control proportion parameter to obtain a comparison result;
and the adjustment subunit is used for adjusting the recommended score according to the comparison result and the adjustment amplitude parameter to obtain an adjusted target score.
9. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the data recommendation method of any one of claims 1 to 5.
10. A readable storage medium having stored therein computer program instructions which, when read and executed by a processor, perform the data recommendation method of any one of claims 1 to 5.
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| CN202310553216.XA CN116595253A (en) | 2023-05-16 | 2023-05-16 | Data recommendation method and device |
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| CN202310553216.XA CN116595253A (en) | 2023-05-16 | 2023-05-16 | Data recommendation method and device |
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