CN113052689A - Product recommendation method and device based on decision tree - Google Patents
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Abstract
The application discloses a product recommendation method and device based on a decision tree, the characteristics of a client to be tested are obtained, the characteristics are input into the decision tree corresponding to the product aiming at each product, the client risk bearing grade of each product is obtained, the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of a sample client, and the higher the client risk bearing grade is, the stronger the risk bearing capacity of the client to be tested is. And taking the product with the risk bearing grade of the client greater than the preset grade as the product recommended to the client to be tested. By the method, the client risk bearing grade of each product can be accurately evaluated based on the pre-constructed decision tree, the product is recommended to the client to be tested according to the client risk bearing grade, and the product recommended to the client to be tested can meet the self risk bearing capacity.
Description
Technical Field
The application relates to the technical field of machine learning, in particular to a product recommendation method and device based on a decision tree.
Background
After a customer purchases a product recommended by a bank, the customer is limited by the influence of the factors of the customer, and the customer may not bear the risk brought by purchasing the product, so that unpredictable consequences are caused, and huge negative effects are brought to the bank and the customer.
Therefore, how to recommend products meeting the self risk tolerance to the customers becomes a problem to be solved urgently in the field.
Disclosure of Invention
The application provides a product recommendation method and device based on a decision tree, and aims to recommend a product meeting the risk tolerance of the product to a client.
In order to achieve the above object, the present application provides the following technical solutions:
a method for decision tree based product recommendation, comprising:
acquiring the characteristics of a client to be detected;
inputting the characteristics into a decision tree corresponding to each product to obtain the client risk bearing grade of each product; the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; the higher the risk bearing grade of the client is, the stronger the risk bearing capacity of the client to be tested is;
and taking the product with the risk bearing grade of the customer being greater than the preset grade as the product recommended to the customer to be tested.
Optionally, the decision tree is pre-constructed based on a corresponding relationship between a client risk tolerance level, a characteristic, and a characteristic proportion of a sample client, and includes:
obtaining sample customer information for the product;
analyzing the sample client information to obtain the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; wherein the feature fraction is a proportion of the total number of sample customers characterizing the feature in the total number of samples;
constructing a training data set based on the corresponding relation;
computing an information gain for the feature based on the training data set;
and constructing a decision tree based on the information gain of each characteristic.
Optionally, the constructing a training data set based on the correspondence includes:
sequencing the features according to the sequence of the corresponding feature proportion from large to small to obtain a feature sequence;
selecting the first m-bit characteristics in the characteristic sequence as target characteristics;
and constructing a training data set based on the corresponding relation among the client risk tolerance grade, the target characteristics and the characteristic proportion.
Optionally, the calculating an information gain of the feature based on the training data set includes:
calculating the information entropy of the risk bearing level of the client by using the feature proportion corresponding to the target feature;
and calculating the information gain of the target feature by using the information entropy and a preset conditional entropy.
Optionally, the constructing a decision tree based on the information gain of each feature includes:
and sequencing the target characteristics according to the sequence of the information gains from large to small to generate a decision tree.
Optionally, the obtaining the characteristics of the customer to be tested includes:
carrying out identity verification on the client to be tested by using a face recognition algorithm;
and acquiring the characteristics of the client to be tested under the condition that the client to be tested passes the identity authentication.
Optionally, the recommending the product with the customer risk tolerance level greater than a preset level to the customer to be tested as a product recommended to the customer to be tested includes:
identifying the product purchased by the customer to be tested as a target product;
identifying the product with the client risk bearing grade greater than a preset grade as a first product;
calculating the similarity between the first product and the target product;
and under the condition that the similarity between the first product and the product is greater than a preset threshold value, taking the first product as a product recommended to the customer to be tested.
A decision tree based product recommendation apparatus comprising:
the acquisition unit is used for acquiring the characteristics of the client to be detected;
the evaluation unit is used for inputting the characteristics into a decision tree corresponding to each product to obtain the client risk bearing grade of each product; the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; the higher the risk bearing grade of the client is, the stronger the risk bearing capacity of the client to be tested is;
and the recommending unit is used for recommending the product with the risk bearing grade of the client greater than the preset grade to the client to be tested.
A computer-readable storage medium comprising a stored program, wherein the program performs the decision tree based product recommendation method.
A decision tree based product recommendation device comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing a program, and the processor is used for executing the program, wherein the program executes the decision tree-based product recommendation method during running.
According to the technical scheme, the characteristics of the client to be tested are obtained, the characteristics are input into the decision tree corresponding to the product aiming at each product, the client risk bearing grade of each product is obtained, the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client, and the higher the client risk bearing grade is, the stronger the risk bearing capacity of the client to be tested is. And taking the product with the risk bearing grade of the client greater than the preset grade as the product recommended to the client to be tested. By the method, the client risk bearing grade of each product can be accurately evaluated based on the pre-constructed decision tree, the product is recommended to the client to be tested according to the client risk bearing grade, and the product recommended to the client to be tested can meet the self risk bearing capacity.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a decision tree-based product recommendation method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of another decision tree-based product recommendation method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a decision tree-based product recommendation apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, a schematic diagram of a decision tree-based product recommendation method provided in an embodiment of the present application includes the following steps:
s101: sample customer information for different types of products is obtained in advance.
Among them, the so-called products, for the financial field, include but are not limited to: funds, stocks, bonds, and the like.
S102: and analyzing the sample client information aiming at each product to obtain the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client.
The so-called client risk tolerance level represents the risk tolerance of the sample client, and the higher the client risk tolerance level is, the stronger the risk tolerance of the sample client is.
The so-called characteristics, i.e., characteristics of the sample customer, for example, characteristics possessed by the sample customer who purchased the fund include: the annual net income is more than 15 ten thousand yuan, the age is 20-25 years old, more than two years of fund purchasing experience exists, and no adverse evidence records exist.
The feature percentage represents a percentage of the total number of sample customers having features in the total number of samples, and specifically, it is assumed that the total number of samples for purchasing funds is 100, the total number of sample customers having features with a net annual income of 15 ten thousand or more is 60 (the corresponding feature percentage is 60%), the total number of sample customers having features with an age of 20 to 25 is 45 (the corresponding feature percentage is 45%), the total number of sample customers having features with a fund purchasing experience of two or more years is 30 (the corresponding feature percentage is 30%), and the total number of sample customers having features without bad credit records is 95 (the corresponding feature percentage is 95%).
S103: and sequencing the features according to the sequence of the corresponding feature proportion from large to small to obtain a feature sequence.
S104: and selecting the first m-bit characteristics in the characteristic sequence as target characteristics.
Wherein m is a positive integer.
S105: and constructing a training data set based on the corresponding relation among the risk bearing grade of the client, the target characteristics and the characteristic proportion.
Wherein the training data set includes a target feature, a feature proportion corresponding to the target feature, and a customer risk tolerance level.
S106: and calculating the information entropy of the risk bearing level of the client by using the feature ratio corresponding to the target feature.
The specific process of calculating the information entropy of the customer risk tolerance level by using the feature ratio corresponding to the target feature is shown in formula (1).
In formula (1), H (D) represents information entropy, D represents training data set, pi＝P(A＝ai) N, n is a positive integer, P (a ═ a ·, n is a positive integer, and i is 1,2,3i) Representing the feature ratio, and A representing the target feature.
S107: and calculating the information gain of the target feature by using the information entropy and the preset conditional entropy.
The specific process of calculating the information gain of the target feature by using the information entropy and the preset conditional entropy is shown as formula (2).
g(D，A)＝H(D)-H(D|A) (2)
In formula (2), g (D, a) represents the information gain, and H (D | a) represents the conditional entropy.
The conditional entropy H (D | a) is shown in formula (3).
S108: and sequencing the target characteristics according to the sequence of the information gain from large to small to generate a decision tree.
S109: and carrying out identity verification on the client to be tested by using a face recognition algorithm.
S110: and under the condition that the to-be-tested customer passes the identity authentication, acquiring the characteristics of the to-be-tested customer and the purchased products of the to-be-tested customer, and identifying the purchased products of the to-be-tested customer as target products.
S111: and inputting the characteristics of the customer to be tested into a decision tree corresponding to the product aiming at each product to obtain the customer risk bearing grade of each product.
S112: and identifying the product with the client risk bearing grade greater than the preset grade as a first product.
S113: and calculating the similarity between the first product and the target product.
The specific implementation manner for calculating the similarity between the first product and the target product is common knowledge familiar to those skilled in the art, and is not described herein again.
S114: and under the condition that the similarity between the first product and the target product is greater than a preset threshold value, taking the first product as a product recommended to the customer to be tested.
In summary, with the method of the embodiment, based on the pre-constructed decision tree, the client risk bearing level of each product can be accurately evaluated, and the product is recommended to the client to be tested according to the client risk bearing level, so that the product recommended to the client to be tested can meet the risk bearing capacity of the product.
It should be noted that, in the above embodiment, the reference S101 is an optional specific implementation manner of the decision tree-based product recommendation method described in this application. In addition, S102 mentioned in the above embodiment is also an optional specific implementation manner of the decision tree based product recommendation method described in this application. For this reason, the flow mentioned in the above embodiment can be summarized as the method shown in fig. 2.
As shown in fig. 2, a schematic diagram of another decision tree-based product recommendation method provided in the embodiment of the present application includes the following steps:
s201: and acquiring the characteristics of the client to be tested.
S202: and inputting the characteristics into a decision tree corresponding to each product to obtain the client risk bearing grade of each product.
The decision tree is constructed in advance based on the corresponding relation among the client risk bearing grades, the characteristics and the characteristic proportion of the sample client, and the higher the client risk bearing grade is, the stronger the risk bearing capacity of the client to be tested is.
S203: and taking the product with the risk bearing grade of the client greater than the preset grade as the product recommended to the client to be tested.
In summary, with the method of the embodiment, based on the pre-constructed decision tree, the client risk bearing level of each product can be accurately evaluated, and the product is recommended to the client to be tested according to the client risk bearing level, so that the product recommended to the client to be tested can meet the risk bearing capacity of the product.
Corresponding to the method for recommending products based on the decision tree provided by the embodiment of the application, the embodiment of the application also provides a device for recommending products based on the decision tree.
As shown in fig. 3, an architecture diagram of a decision tree-based product recommendation apparatus provided in an embodiment of the present application includes:
the obtaining unit 100 is configured to obtain characteristics of a customer to be tested.
The obtaining unit 100 is specifically configured to: carrying out identity verification on the client to be tested by using a face recognition algorithm; and acquiring the characteristics of the client to be tested under the condition that the client to be tested passes the identity authentication.
The evaluation unit 200 is configured to, for each product, input the features into a decision tree corresponding to the product to obtain a customer risk tolerance level of each product; the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; the higher the risk bearing grade of the client is, the stronger the risk bearing capacity of the client to be tested is.
Wherein, the evaluation unit 200 is specifically configured to: obtaining sample customer information of a product; analyzing the sample client information to obtain the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; wherein the feature accounts for a proportion of the total number of sample clients characterizing the feature in the total number of samples; constructing a training data set based on the corresponding relation; calculating an information gain of the features based on the training data set; and constructing a decision tree based on the information gain of each characteristic.
The process of the evaluation unit 200 for constructing the training data set based on the correspondence relationship includes: sequencing the features according to the sequence of the corresponding feature proportion from large to small to obtain a feature sequence; selecting the first m-bit characteristics in the characteristic sequence as target characteristics; and constructing a training data set based on the corresponding relation among the risk bearing grade of the client, the target characteristics and the characteristic proportion.
The process of the evaluation unit 200 for calculating the information gain of the features based on the training data set comprises: calculating the information entropy of the risk bearing level of the client by using the feature ratio corresponding to the target feature; and calculating the information gain of the target feature by using the information entropy and the preset conditional entropy.
The evaluation unit 200 is configured to construct a decision tree based on the information gain of each feature, and the process of constructing the decision tree includes: and sequencing the target characteristics according to the sequence of the information gain from large to small to generate a decision tree.
The recommending unit 300 is configured to recommend a product with a risk bearing level of the customer being greater than a preset level as a product to be recommended to the customer to be tested.
The recommending unit 300 is specifically configured to: identifying a product purchased by a customer to be tested as a target product; identifying a product with a customer risk bearing grade greater than a preset grade as a first product; calculating the similarity between the first product and the target product; and under the condition that the similarity between the first product and the product is greater than a preset threshold value, taking the first product as the product recommended to the customer to be tested.
In summary, with the method of the embodiment, based on the pre-constructed decision tree, the client risk bearing level of each product can be accurately evaluated, and the product is recommended to the client to be tested according to the client risk bearing level, so that the product recommended to the client to be tested can meet the risk bearing capacity of the product.
The present application also provides a computer-readable storage medium including a stored program, wherein the program performs the decision tree based product recommendation method provided by the present application.
The present application further provides a decision tree-based product recommendation device, comprising: a processor, a memory, and a bus. The processor is connected with the memory through a bus, the memory is used for storing programs, and the processor is used for running the programs, wherein when the programs are run, the method for recommending the product based on the decision tree, provided by the application, comprises the following steps:
acquiring the characteristics of a client to be detected;
inputting the characteristics into a decision tree corresponding to each product to obtain the client risk bearing grade of each product; the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; the higher the risk bearing grade of the client is, the stronger the risk bearing capacity of the client to be tested is;
and taking the product with the risk bearing grade of the customer being greater than the preset grade as the product recommended to the customer to be tested.
Optionally, the decision tree is pre-constructed based on a corresponding relationship between a client risk tolerance level, a characteristic, and a characteristic proportion of a sample client, and includes:
obtaining sample customer information for the product;
analyzing the sample client information to obtain the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; wherein the feature fraction is a proportion of the total number of sample customers characterizing the feature in the total number of samples;
constructing a training data set based on the corresponding relation;
computing an information gain for the feature based on the training data set;
and constructing a decision tree based on the information gain of each characteristic.
Optionally, the constructing a training data set based on the correspondence includes:
sequencing the features according to the sequence of the corresponding feature proportion from large to small to obtain a feature sequence;
selecting the first m-bit characteristics in the characteristic sequence as target characteristics;
and constructing a training data set based on the corresponding relation among the client risk tolerance grade, the target characteristics and the characteristic proportion.
Optionally, the calculating an information gain of the feature based on the training data set includes:
calculating the information entropy of the risk bearing level of the client by using the feature proportion corresponding to the target feature;
and calculating the information gain of the target feature by using the information entropy and a preset conditional entropy.
Optionally, the constructing a decision tree based on the information gain of each feature includes:
and sequencing the target characteristics according to the sequence of the information gains from large to small to generate a decision tree.
Optionally, the obtaining the characteristics of the customer to be tested includes:
carrying out identity verification on the client to be tested by using a face recognition algorithm;
and acquiring the characteristics of the client to be tested under the condition that the client to be tested passes the identity authentication.
Optionally, the recommending the product with the customer risk tolerance level greater than a preset level to the customer to be tested as a product recommended to the customer to be tested includes:
identifying the product purchased by the customer to be tested as a target product;
identifying the product with the client risk bearing grade greater than a preset grade as a first product;
calculating the similarity between the first product and the target product;
and under the condition that the similarity between the first product and the product is greater than a preset threshold value, taking the first product as a product recommended to the customer to be tested.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in 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), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. 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 application. Thus, the present application 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 recommending products based on decision trees is characterized by comprising the following steps:
acquiring the characteristics of a client to be detected;
inputting the characteristics into a decision tree corresponding to each product to obtain the client risk bearing grade of each product; the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; the higher the risk bearing grade of the client is, the stronger the risk bearing capacity of the client to be tested is;
and taking the product with the risk bearing grade of the customer being greater than the preset grade as the product recommended to the customer to be tested.
2. The method of claim 1, wherein the decision tree is pre-constructed based on correspondence between customer risk tolerance levels, characteristics, and feature fractions of sample customers, comprising:
obtaining sample customer information for the product;
analyzing the sample client information to obtain the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; wherein the feature fraction is a proportion of the total number of sample customers characterizing the feature in the total number of samples;
constructing a training data set based on the corresponding relation;
computing an information gain for the feature based on the training data set;
and constructing a decision tree based on the information gain of each characteristic.
3. The method of claim 2, wherein constructing a training data set based on the correspondence comprises:
sequencing the features according to the sequence of the corresponding feature proportion from large to small to obtain a feature sequence;
selecting the first m-bit characteristics in the characteristic sequence as target characteristics;
and constructing a training data set based on the corresponding relation among the client risk tolerance grade, the target characteristics and the characteristic proportion.
4. The method of claim 3, wherein computing the information gain for the feature based on the training data set comprises:
calculating the information entropy of the risk bearing level of the client by using the feature proportion corresponding to the target feature;
and calculating the information gain of the target feature by using the information entropy and a preset conditional entropy.
5. The method of claim 4, wherein constructing a decision tree based on the information gain of each of the features comprises:
and sequencing the target characteristics according to the sequence of the information gains from large to small to generate a decision tree.
6. The method of claim 1, wherein the obtaining characteristics of the customer to be tested comprises:
carrying out identity verification on the client to be tested by using a face recognition algorithm;
and acquiring the characteristics of the client to be tested under the condition that the client to be tested passes the identity authentication.
7. The method of claim 1, wherein the step of recommending the product with the customer risk tolerance level greater than a preset level to the customer to be tested comprises:
identifying the product purchased by the customer to be tested as a target product;
identifying the product with the client risk bearing grade greater than a preset grade as a first product;
calculating the similarity between the first product and the target product;
and under the condition that the similarity between the first product and the product is greater than a preset threshold value, taking the first product as a product recommended to the customer to be tested.
8. A decision tree based product recommendation device, comprising:
the acquisition unit is used for acquiring the characteristics of the client to be detected;
the evaluation unit is used for inputting the characteristics into a decision tree corresponding to each product to obtain the client risk bearing grade of each product; the decision tree is constructed in advance based on the corresponding relation among the client risk bearing grade, the characteristics and the characteristic proportion of the sample client; the higher the risk bearing grade of the client is, the stronger the risk bearing capacity of the client to be tested is;
and the recommending unit is used for recommending the product with the risk bearing grade of the client greater than the preset grade to the client to be tested.
9. A computer-readable storage medium, comprising a stored program, wherein the program performs the decision tree based product recommendation method of any one of claims 1-7.
10. A decision tree based product recommendation device, comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is configured to store a program and the processor is configured to execute the program, wherein the program is configured to execute the method for decision tree based product recommendation of any of claims 1-7 when executing.
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| CN202110486328.9A CN113052689B (en) | 2021-04-30 | 2021-04-30 | Product recommendation method and device based on decision tree |
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| CN202110486328.9A CN113052689B (en) | 2021-04-30 | 2021-04-30 | Product recommendation method and device based on decision tree |
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| CN113052689B CN113052689B (en) | 2024-03-26 |
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