CN113159927A - Method and device for determining client label - Google Patents
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- CN113159927A CN113159927A CN202110486429.6A CN202110486429A CN113159927A CN 113159927 A CN113159927 A CN 113159927A CN 202110486429 A CN202110486429 A CN 202110486429A CN 113159927 A CN113159927 A CN 113159927A
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Abstract
The invention provides a method and a device for determining a client label, which are characterized in that under the condition that a target client has label information, input data at least comprising a labeled product type and a corresponding label value in the label information and the association degree between the labeled product type and the target product type are input into a first label prediction model which is constructed in advance, the label of the target client under the target product type is predicted, the label value of the target user under the labeled product type and the association degree between the labeled product type and the target product type are utilized, the label of the target client under the target product type is accurately predicted, and the accuracy of the client label is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for determining a client tag.
Background
In order to realize accurate marketing, different customers are labeled, and proper products are recommended to the customers according to the labels of the customers.
The accuracy of the customer label seriously affects the success rate of product trading after the product is recommended to the customer. Therefore, how to accurately label the client becomes a technical problem to be solved urgently in the field.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for determining a customer label, which improve the accuracy of the customer label.
In order to achieve the above purpose, the invention provides the following specific technical scheme:
a method of customer tag determination, comprising:
responding to a labeling instruction, analyzing the labeling instruction to obtain a target customer and a target product type;
under the condition that the target customer is inquired to have label information, acquiring the type of the labeled product and a corresponding label value in the label information;
acquiring the pre-stored association degree between the labeled product type and the target product type;
and inputting input data at least comprising the label information and the relevance into a first label prediction model which is constructed in advance to obtain the label of the target customer under the target product type.
Optionally, before inputting the input data at least including the tag information and the relevance into the first pre-constructed tag prediction model, the method further includes:
inquiring whether behavior data or purchase data of the target customer under the target product type exists;
and in the case that the behavior data or the purchase data of the target customer under the target product type is inquired, adding the inquired data into the input data.
Optionally, the method further includes:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period and the correlation degree among different product types as training samples of the first label prediction model;
and training a first neural network model by using the training samples of the first label prediction model to obtain the first label prediction model, wherein the structure of the first neural network model is set according to the number of data types in the input and output data of the first neural network model.
Optionally, the method further includes:
under the condition that the label information of the target customer is not inquired, inquiring whether behavior data or purchase data of the target customer under the target product type exists or not;
and under the condition that behavior data or purchase data of the target customer under the target product type is inquired, inputting the inquired data into a second label prediction model which is constructed in advance to obtain a label of the target customer under the target product type.
Optionally, the method further includes:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period as training samples of the second label prediction model;
and training a second neural network model by using the training samples of the second label prediction model to obtain the second label prediction model, wherein the structure of the second neural network model is set according to the number of data types in the input and output data of the second neural network model.
An apparatus for determining a customer label, comprising:
the instruction analyzing unit is used for responding to the labeling instruction and analyzing the labeling instruction to obtain a target customer and a target product type;
the label inquiring unit is used for acquiring the type of the labeled product and the corresponding label value in the label information under the condition that the label information exists in the target customer is inquired;
the association degree acquisition unit is used for acquiring the association degree between the pre-stored labeled product type and the target product type;
and the first label prediction unit is used for inputting input data at least comprising the label information and the relevance into a first label prediction model which is constructed in advance to obtain the label of the target customer under the target product type.
Optionally, the apparatus further comprises:
the historical data query unit is used for querying whether behavior data or purchase data of the target customer under the target product type exists; and in the case that the behavior data or the purchase data of the target customer under the target product type is inquired, adding the inquired data into the input data.
Optionally, the apparatus further includes a first model training unit, configured to:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period and the correlation degree among different product types as training samples of the first label prediction model;
and training a first neural network model by using the training samples of the first label prediction model to obtain the first label prediction model, wherein the structure of the first neural network model is set according to the number of data types in the input and output data of the first neural network model.
Optionally, the apparatus further comprises:
the historical data query unit is used for querying whether behavior data or purchase data of the target customer under the target product type exists or not under the condition that the label information of the target customer is not queried;
and the second label prediction unit is used for inputting the inquired data into a second label prediction model which is constructed in advance under the condition that behavior data or purchase data of the target customer under the target product type is inquired, so that the label of the target customer under the target product type is obtained.
Optionally, the apparatus further includes a second model training unit, configured to:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period as training samples of the second label prediction model;
and training a second neural network model by using the training samples of the second label prediction model to obtain the second label prediction model, wherein the structure of the second neural network model is set according to the number of data types in the input and output data of the second neural network model.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a method for determining a client label, which is characterized in that under the condition that a target client has label information, input data at least comprising a labeled product type and a corresponding label value in the label information and the correlation degree between the labeled product type and the target product type are input into a first label prediction model which is constructed in advance, so that the label of the target client under the target product type is predicted, the label value of the target user under the labeled product type and the correlation degree between the labeled product type and the target product type are utilized, the accurate prediction of the label of the target client under the target product type is realized, and the accuracy of the client label is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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 embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart illustrating a method for determining a client tag according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating another method for determining a customer tag according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a client tag determination apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of another apparatus for determining a customer tag according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
The invention provides a method for determining a customer label to improve the accuracy of the customer label, so as to recommend a product which is interested by a customer to the customer and realize accurate marketing, and please refer to fig. 1, wherein the method specifically comprises the following steps:
s101: responding to the labeling instruction, analyzing the labeling instruction to obtain a target customer and a target product type;
the target client is a client needing to be labeled, specifically, the target client is labeled under the target product type, and if the target client is client a and the target product type is a risk investment product, the client a needs to be labeled under the risk investment product.
S102: under the condition that the target customer has label information, acquiring the type of a labeled product and a corresponding label value in the label information;
the target customer may be a customer who does not have any tag information in the system, or may be a customer who has tag information under other product types in the system.
For different application scenarios, the classification method of the product types is adjusted according to actual requirements, and when the classification method is applied to a bank product scenario, the product types may include: risk investment products, loan products, commemorative products, collectible products, and the like.
When a client has label information under a certain product type or a plurality of product types, the label information is stored locally, and then under the condition that the label information of a target client is inquired, the type of the labeled product in the label information and a corresponding label value are obtained.
The tag value indicates the interest degree of the target user in the type of the tagged product, specifically, the tag value may be a specific numerical value, or may be a value indicating the interest degree such as no interest, general interest, great interest, and the like.
S103: acquiring the association degree between the pre-stored labeled product type and the target product type;
it should be noted that, the system presets and stores the association between different product types, specifically, the association between different product types may be set according to experience, or the association between different product types may be set by using an induction method according to historical data, such as counting the probability that a client purchases product type a and then product type B within a period of time according to historical data, similarly counting the probability that the client purchases other two product types within a period of time, and finally calculating and setting the association between different product types by using a linear regression algorithm according to the statistical result.
S104: inputting input data at least comprising label information and relevance into a first label prediction model which is constructed in advance to obtain a label of a target customer under a target product type.
It is understood that the first label prediction model is constructed in advance, and the neural network model needs to be trained by using training samples. The structure of the first label prediction model is set according to the number of data types in the input and output data of the first neural network model.
When the input data of the first label prediction model only comprises label information and the association degree, determining the association degree between the label values of different customers under different product types and different product types in a preset historical time period as a training sample of the first label prediction model. And determining the BP neural network structure according to the number of the input and the output of the first label prediction network model, and further determining the number of parameters needing to be optimized in the genetic algorithm. According to the kolmogorov principle, one three-layer BP neural network can sufficiently complete any mapping from n dimension to m dimension, generally only one hidden layer is needed, and the number of hidden layer nodes is determined by a trial and error method, so that the GA-BP neural network structure is determined. And dividing the training sample into a training set and a verification set, and training and verifying the neural network model to obtain an effective first label prediction model.
In order to improve the accuracy of model prediction, when querying behavior data or purchase data of a target customer in a target product type, the behavior data and purchase data of the target customer in the target product type may be added to input data of a first tag prediction model, and the behavior data may be related to behaviors such as browsing, collecting and the like, that is, the input data of the first tag prediction model includes: the label information of the target customer, the degree of association between the target product type and the marked product type, and the behavior data and purchase data of the target customer under the target product type (0 when one or both of the behavior data and the purchase data are not present). The behavior data, the purchase data and the label values of different customers under different product types in a preset historical time period and the correlation degree among different product types are determined as training samples of the first label prediction model, and on the basis, the BP neural network structure is determined according to the number of input and output data of the first label prediction model, so that the number of parameters needing to be optimized in the genetic algorithm is determined.
The label value output by the first label prediction model represents the interest degree of the target user on the type of the labeled product, specifically, the label value may be a specific numerical value, and in order to facilitate the label to represent the interest degree of the target user on the type of the labeled product, the label value may also be converted into one of uninteresting, general, very interesting and the like according to the corresponding relationship between the label value and the label.
After obtaining the label of the target customer under the target product type, whether the target product type is recommended to the target customer can be determined according to the label.
Further, under the condition that the label information of the target customer cannot be inquired, the method and the system can still predict the label of the target customer under the target product type. Referring to fig. 2, a method for determining a client tag disclosed in this embodiment includes the following steps:
s201: responding to the labeling instruction, analyzing the labeling instruction to obtain a target customer and a target product type;
s202: inquiring whether the target client has label information or not;
if the tag information exists, S203: acquiring the type of a labeled product and a corresponding label value in the label information;
s204: acquiring the association degree between the pre-stored labeled product type and the target product type;
s205: inputting input data at least comprising label information and relevance into a first label prediction model which is constructed in advance to obtain a label of a target customer under a target product type;
if no tag information exists, S206: inquiring whether behavior data or purchase data of the target customer under the target product type exists;
s207: and under the condition that behavior data or purchase data of the target customer under the target product type is inquired, inputting the inquired data into a pre-constructed second label prediction model to obtain a label of the target customer under the target product type.
It should be noted that the second label prediction model also needs to be constructed in advance, and the construction method of the second label prediction model is as follows:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period as training samples of a second label prediction model;
and training the second neural network model by using the training samples of the second label prediction model to obtain the second label prediction model, wherein the structure of the second neural network model is set according to the number of data types in the input and output data of the second neural network model.
In the method for determining a client tag disclosed in this embodiment, when a target client has tag information, input data at least including a tagged product type and a corresponding tag value in the tag information and a degree of association between the tagged product type and the target product type is input into a first tag prediction model that is constructed in advance, so as to predict a tag of the target client in the target product type, and by using the tag value of the target user in the tagged product type and the degree of association between the tagged product type and the target product type, accurate prediction of the tag of the target client in the target product type is achieved, and accuracy of the client tag is improved.
Based on the method for determining a client tag disclosed in the foregoing embodiment, this embodiment correspondingly discloses a device for determining a client tag, please refer to fig. 3, where the device includes:
the instruction analyzing unit 100 is configured to respond to a labeling instruction and analyze the labeling instruction to obtain a target customer and a target product type;
the tag querying unit 200 is configured to, when querying that the target client has tag information, obtain a type of a tagged product in the tag information and a corresponding tag value;
the association degree acquiring unit 300 is configured to acquire an association degree between the pre-stored labeled product type and the target product type;
a first label prediction unit 400, configured to input data at least including the label information and the relevance into a first label prediction model that is constructed in advance, so as to obtain a label of the target customer in the target product type.
Optionally, the apparatus further comprises:
the historical data query unit is used for querying whether behavior data or purchase data of the target customer under the target product type exists; and in the case that the behavior data or the purchase data of the target customer under the target product type is inquired, adding the inquired data into the input data.
Optionally, the apparatus further includes a first model training unit, configured to:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period and the correlation degree among different product types as training samples of the first label prediction model;
and training a first neural network model by using the training samples of the first label prediction model to obtain the first label prediction model, wherein the structure of the first neural network model is set according to the number of data types in the input and output data of the first neural network model.
Referring to fig. 4, the present embodiment discloses another apparatus for determining a client tag, including:
the instruction analyzing unit 100 is configured to respond to a labeling instruction and analyze the labeling instruction to obtain a target customer and a target product type;
the tag querying unit 200 is configured to, when querying that the target client has tag information, obtain a type of a tagged product in the tag information and a corresponding tag value;
the association degree acquiring unit 300 is configured to acquire an association degree between the pre-stored labeled product type and the target product type;
a first label prediction unit 400, configured to input data at least including the label information and the relevance into a first label prediction model that is constructed in advance, so as to obtain a label of the target customer in the target product type.
A historical data query unit 500, configured to query whether behavior data or purchase data of the target customer in the target product type exists or not if the tag information of the target customer is not queried;
a second label prediction unit 600, configured to, when behavior data or purchase data of the target customer in the target product type is queried, input the queried data into a second label prediction model that is constructed in advance, so as to obtain a label of the target customer in the target product type.
Optionally, the apparatus further includes a second model training unit, configured to:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period as training samples of the second label prediction model;
and training a second neural network model by using the training samples of the second label prediction model to obtain the second label prediction model, wherein the structure of the second neural network model is set according to the number of data types in the input and output data of the second neural network model.
In the device for determining a client tag disclosed in this embodiment, when a target client has tag information, input data at least including a tagged product type and a corresponding tag value in the tag information and a degree of association between the tagged product type and the target product type is input into a first tag prediction model which is constructed in advance, so as to predict a tag of the target client in the target product type, and by using the tag value of the target user in the tagged product type and the degree of association between the tagged product type and the target product type, accurate prediction of the tag of the target client in the target product type is achieved, and accuracy of the client tag is improved.
The embodiments in the present description are described in a progressive manner, 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 is further noted that, herein, relational terms such as first and second, and the like may be 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.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments can be combined arbitrarily, and the features described in the embodiments in the present specification can be replaced or combined with each other in the above description of the disclosed embodiments, so that those skilled in the art can implement or use the present application.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for determining a customer label, comprising:
responding to a labeling instruction, analyzing the labeling instruction to obtain a target customer and a target product type;
under the condition that the target customer is inquired to have label information, acquiring the type of the labeled product and a corresponding label value in the label information;
acquiring the pre-stored association degree between the labeled product type and the target product type;
and inputting input data at least comprising the label information and the relevance into a first label prediction model which is constructed in advance to obtain the label of the target customer under the target product type.
2. The method of claim 1, wherein prior to entering input data comprising at least the label information and the degree of association into a pre-constructed first label prediction model, the method further comprises:
inquiring whether behavior data or purchase data of the target customer under the target product type exists;
and in the case that the behavior data or the purchase data of the target customer under the target product type is inquired, adding the inquired data into the input data.
3. The method of claim 2, further comprising:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period and the correlation degree among different product types as training samples of the first label prediction model;
and training a first neural network model by using the training samples of the first label prediction model to obtain the first label prediction model, wherein the structure of the first neural network model is set according to the number of data types in the input and output data of the first neural network model.
4. The method of claim 1, further comprising:
under the condition that the label information of the target customer is not inquired, inquiring whether behavior data or purchase data of the target customer under the target product type exists or not;
and under the condition that behavior data or purchase data of the target customer under the target product type is inquired, inputting the inquired data into a second label prediction model which is constructed in advance to obtain a label of the target customer under the target product type.
5. The method of claim 4, further comprising:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period as training samples of the second label prediction model;
and training a second neural network model by using the training samples of the second label prediction model to obtain the second label prediction model, wherein the structure of the second neural network model is set according to the number of data types in the input and output data of the second neural network model.
6. An apparatus for determining a customer label, comprising:
the instruction analyzing unit is used for responding to the labeling instruction and analyzing the labeling instruction to obtain a target customer and a target product type;
the label inquiring unit is used for acquiring the type of the labeled product and the corresponding label value in the label information under the condition that the label information exists in the target customer is inquired;
the association degree acquisition unit is used for acquiring the association degree between the pre-stored labeled product type and the target product type;
and the first label prediction unit is used for inputting input data at least comprising the label information and the relevance into a first label prediction model which is constructed in advance to obtain the label of the target customer under the target product type.
7. The apparatus of claim 6, further comprising:
the historical data query unit is used for querying whether behavior data or purchase data of the target customer under the target product type exists; and in the case that the behavior data or the purchase data of the target customer under the target product type is inquired, adding the inquired data into the input data.
8. The apparatus of claim 7, further comprising a first model training unit to:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period and the correlation degree among different product types as training samples of the first label prediction model;
and training a first neural network model by using the training samples of the first label prediction model to obtain the first label prediction model, wherein the structure of the first neural network model is set according to the number of data types in the input and output data of the first neural network model.
9. The apparatus of claim 6, further comprising:
the historical data query unit is used for querying whether behavior data or purchase data of the target customer under the target product type exists or not under the condition that the label information of the target customer is not queried;
and the second label prediction unit is used for inputting the inquired data into a second label prediction model which is constructed in advance under the condition that behavior data or purchase data of the target customer under the target product type is inquired, so that the label of the target customer under the target product type is obtained.
10. The apparatus according to claim 9, further comprising a second model training unit for:
determining behavior data, purchase data and label values of different customers under different product types in a preset historical time period as training samples of the second label prediction model;
and training a second neural network model by using the training samples of the second label prediction model to obtain the second label prediction model, wherein the structure of the second neural network model is set according to the number of data types in the input and output data of the second neural network model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116705340A (en) * | 2023-04-07 | 2023-09-05 | 中南大学湘雅三医院 | Public health intelligent monitoring system and method based on blockchain |
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| CN116705340A (en) * | 2023-04-07 | 2023-09-05 | 中南大学湘雅三医院 | Public health intelligent monitoring system and method based on blockchain |
| CN116705340B (en) * | 2023-04-07 | 2024-02-02 | 中南大学湘雅三医院 | Public health intelligent monitoring system and method based on blockchain |
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