CN113034246A - Financial product recommendation method and device - Google Patents
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Abstract
The invention provides a financial product recommendation method and device, and relates to the technical field of big data analysis and artificial intelligence, wherein the recommendation method comprises the following steps: acquiring financial data of a family client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model. The invention can quickly and accurately recommend financial products to users from mass financial products.
Description
Technical Field
The invention relates to the technical field of big data analysis and artificial intelligence, in particular to a financial product recommendation method and device.
Background
With the development of industrial revolution and the establishment of cities, people are driven by profit-making ideas to carry out financial management, for a large number of professionals in the non-financial field at present, income cannot obtain very good financial management configuration, and the people miss the opportunity of money earning in directionless investment or consumption, and hopefully hope to have a high-safety scheme of saving and directly executing financial management.
In the field of financing service of small and medium-sized micro enterprises, various financial products are provided for different user types, the requirements of the financial products on application objects are different, the difficulty degree and the payment time of application are also different, so that the users cannot quickly, accurately and rapidly find the financial products suitable for the users, the enterprises need to spend a large amount of time for finding the financial products suitable for the users, the user experience is influenced, and the cost of the financial institution products reaching the users is increased. At present, more intelligent recommended financing schemes are designed based on big data, the collection of big data information is only aiming at the self information of the financing product, and the finally recommended financing product does not meet the requirements of customers very well, namely the accuracy is not high.
Therefore, how to rapidly and accurately recommend financial products to users from a large amount of financial products becomes a technical problem to be solved urgently in the field.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a financial product recommendation method and device, which can quickly and accurately recommend financial products to users from mass financial products.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a financial product recommendation method, including:
acquiring financial data of a family client; wherein the financial data comprises: the type of investment;
performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels;
determining a customer representation of each type of the family customer and a preference for different financial products;
and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model.
Further, after acquiring the financial data of the family client, the method further comprises the following steps:
cleaning and cross-verifying the financing data to obtain effective financing data;
correspondingly, the step of performing labeling processing on the investment types in the financial data to obtain the client labels of the family clients comprises the following steps:
and performing labeling processing on the investment types in the effective financing data to obtain the client labels of the family clients.
Wherein the determining the customer image of each type of the family customer comprises:
analyzing the correlation between the client label of the family client and the financial product to obtain a user label strongly correlated with the financial product;
according to a preset label structure of the family client, dividing the user labels of the family client, which are strongly related to financial products, into a plurality of levels, and generating a user portrait of the family client.
Wherein the determining of the financial product recommendation list corresponding to the family customer based on the customer profile, the preference degree and a preset classification model comprises:
calculating a product recommendation weight of each financial product relative to the family customer based on the customer representation and the preference;
and generating a financial product recommendation list corresponding to the family customer by the product recommendation weight and the classification model.
Further, the method also comprises the following steps:
acquiring historical data of a family client, training based on the historical data and a Wide & Deep Learning algorithm to obtain a classification model, and specifically comprising the following steps:
splitting historical data into training samples and verification samples; and training the Wide & Deep Learning-based classification model through the training sample, and verifying the trained classification model through the verification sample.
The method includes splitting historical data into training samples and verification samples, and specifically includes: and taking the historical data before the set time as a training sample, and taking the historical data after the set time as a verification sample.
In a second aspect, the present invention provides a financial product recommendation apparatus comprising:
the acquisition module is used for acquiring financial data of the family client; wherein the financial data comprises: the type of investment;
the label module is used for performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering the financial types of the family clients according to the client labels;
the preference module is used for determining client figures of each type of family clients and preference degrees of different financial products;
and the recommending module is used for determining a financial product recommending list corresponding to the family client based on the client portrait, the preference degree and a preset classification model.
Further, still include:
the cleaning module is used for cleaning and cross-verifying the financing data to obtain effective financing data;
correspondingly, the label module comprises:
and the label unit is used for performing labeling processing on the investment types in the effective financial data to obtain the client labels of the family clients.
Wherein the preference module comprises:
the tag unit is used for analyzing the correlation between the client tag of the family client and the financial product to obtain a user tag which is strongly correlated with the financial product;
and the portrait unit is used for dividing the user tags which are strongly related to the family clients and the financial products into a plurality of levels according to the tag structures of the preset family clients and generating the user portrait of the family clients.
Wherein the recommendation module comprises:
a weighting unit for calculating a product recommendation weight of each financial product relative to the family customers based on the customer figures and the preference degrees;
and the list unit is used for generating a financial product recommendation list corresponding to the family client by the product recommendation weight and the classification model.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the financial product recommendation method when executing the program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the financial product recommendation method described herein.
According to the technical scheme, the financial product recommendation method and device provided by the invention have the advantages that the financial data of the family client are obtained; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model. The invention can quickly and accurately recommend financial products to users from mass financial products.
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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a first flowchart of a financial product recommendation method according to an embodiment of the present invention.
Fig. 2 is a second flowchart of the financial product recommendation method in the embodiment of the present invention.
Fig. 3 is a flow diagram illustrating a full flow of a financial product recommendation method in an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a financial product recommendation device in an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Before the technical solution in this embodiment is completely described, the technical field related to the technical solution in this embodiment is explained. The technical scheme in the embodiment can analyze and process a large amount of data, so that the technical scheme in the embodiment relates to the technical field of big data, and an artificial intelligence algorithm is utilized in the process of analyzing and processing the large amount of data. Therefore, the technical scheme in the embodiment also relates to the technical field of artificial intelligence. The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides an embodiment of a financial product recommendation method, which specifically comprises the following contents in reference to fig. 1:
s101: acquiring financial data of a family client; wherein the financial data comprises: the type of investment;
in the step, proper financial data of the family client are collected, and the family client fills in a questionnaire to collect various types of data such as capital, consumption, investment behaviors and the like of the family client through a well-designed questionnaire, so that the problem of cold start without user data when the product is used for the first time by the family client is solved, and the collection of the data is realized; after the product is used for a period of time, the family customer data will also be collected by means of online real-time collection.
S102: performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels;
s103: determining a customer representation of each type of the family customer and a preference for different financial products;
in this step, determining the customer images of each type of the home customers specifically includes: analyzing the correlation between the client label of the family client and the financial product to obtain a user label strongly correlated with the financial product; according to a preset label structure of the family client, dividing the user labels of the family client, which are strongly related to financial products, into a plurality of levels, and generating a user portrait of the family client.
S104: and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model.
The method specifically comprises the following steps: calculating a product recommendation weight of each financial product relative to the family customer based on the customer representation and the preference; and generating a financial product recommendation list corresponding to the family customer by the product recommendation weight and the classification model.
In the embodiment, the customers are clustered into three types of family customers, namely conservative family customers, conservative family customers and aggressive family customers; if the product runs and uses for a period of time, according to the data of other clients, the operation and analysis of multidimensional data such as client age, family asset financing style, family asset risk tolerance and the like in the data are combined according to the unsupervised clustering analysis method, and the clients with similar characteristics are clustered into the same cluster.
According to the clustering result of the new client, Deep analysis is carried out on the same class to which the new client belongs to mine potential characteristics and preferences, financial schemes of other clients in the class are mined, and Wide & Deep Learning is used for realizing intelligent financial recommendation of the new family client.
The embodiment provides a specific embodiment for obtaining a classification model based on Wide & Deep Learning algorithm, which includes:
acquiring historical data of a family client, training based on the historical data and a Wide & Deep Learning algorithm to obtain a classification model, and specifically comprising the following steps:
splitting historical data into training samples and verification samples; and training the Wide & Deep Learning-based classification model through the training sample, and verifying the trained classification model through the verification sample.
It should be noted that splitting the historical data into a training sample and a verification sample specifically includes: and taking the historical data before the set time as a training sample, and taking the historical data after the set time as a verification sample. The classification model comprises a Wide model and a Deep model. And calculating the influence weight of each type of characteristics on the current new family client by analyzing the characteristic data of the historical distribution scheme, the financial product purchase record and the like of the old family client by using the Wide model. The Wide model is mainly used for analyzing and calculating historical data of customers for purchasing, browsing, consulting and the like to generate a rough financial scheme recommendation. And analyzing and mining various characteristics such as age, investment style, risk bearing capacity and historical data filled by a client in the questionnaire by using a Deep model, and processing and converting the characteristic data into 1200-dimensional high-order vectors for training, so that more refined new characteristic combinations are mined by combining various data, and the diversity of recommended combinations is improved. The Wide and Deep part weights and sums the feature weights through a common model, and then outputs financing products intelligently recommended to the family clients.
Recommendations are made to the home consumer for items to purchase in the home objective, including shopping consumption, travel consumption, entertainment consumption, and the like. According to the characteristics and the category of similar articles and the browsing relation of the same type of cluster clients in history, a multi-way recall strategy is adopted, the articles are subjected to vector representation, parts of the articles which accord with the target are screened out from all the articles, the screened articles are subjected to similarity sorting, finally displayed articles recommended to the clients are determined, and the consumption of the clients is recommended.
As can be seen from the above description, the method for recommending financial products according to the embodiment of the present invention obtains financial data of a family client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model. The invention can quickly and accurately recommend financial products to users from mass financial products.
In an embodiment of the present invention, referring to fig. 2, after step S101 of the method for recommending a financial product, the method specifically includes the following steps:
s105: cleaning and cross-verifying the financing data to obtain effective financing data;
correspondingly, step S102 performs tagging processing on the investment types in the financial data to obtain a client tag of the family client, including:
and S1021, performing labeling processing on the investment types in the effective financing data to obtain a client label of the family client.
In this embodiment, the accuracy of financial data can be improved by eliminating abnormal data and noise data in the roll-up gather, correcting inconsistent data, removing noise in the data, and performing standardized processing.
To further explain the present solution, the present invention provides a full-flow embodiment of a method for recommending a financial product, as shown in fig. 3, the method for recommending a financial product specifically includes the following contents:
step 1: and (4) acquiring and preprocessing the family client data.
Collecting user behavior data of the new family client data, removing noise and inconsistent data in the data by eliminating abnormal data, smooth noise data and other modes, and converting the data into a data set suitable for model training by normalization or standardization;
step 2: new home users cluster portraits.
And (3) carrying out cluster analysis on the new family client according to the data in the step (1), and dividing the new family client into user clusters with the same characteristics to realize the user cluster portrait.
And step 3: and intelligently recommending financial products according to family behavior data characteristics of family users in the same family cluster.
Deep analysis is carried out on the same type of cluster to which the new client belongs to mine potential characteristics and preferences, and training analysis is carried out on multidimensional data of other clients in the same type of cluster by analyzing historical asset allocation schemes, financial product purchase records, consultation records and the like of other family clients in the same type of cluster by using Wide & Deep Learning algorithm, so that the intelligent recommendation of proper financial products to family users is realized.
And 4, step 4: and recommending the life articles according to the family life target of the family user in the same family cluster.
According to the family targets of other users in the cluster to which the new family client belongs, common living consumption habits, browsing and purchasing information of the users of the same cluster in history and the like, after the articles are screened and sorted, the purpose that the articles with the living targets are accurately recommended to the family client is achieved.
And 5: the recommendation result is displayed on the front-end page.
And (4) intelligent financial products, recommending and displaying the target articles of family life on a front-end page, and finishing product display.
The embodiment of the invention provides a specific implementation mode of a financial product recommending device capable of realizing all contents in the financial product recommending method, and referring to fig. 4, the financial product recommending device specifically comprises the following contents:
the acquisition module 10 is used for acquiring financial data of family customers; wherein the financial data comprises: the type of investment;
the label module 20 is used for performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering the financial types of the family clients according to the client labels;
a preference module 30 for determining a customer representation of each type of home customer and a preference for different financial products;
and the recommending module 40 is used for determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model.
Further, still include:
the cleaning module is used for cleaning and cross-verifying the financing data to obtain effective financing data;
correspondingly, the method comprises the following steps:
and the cleaning unit is used for performing labeling processing on the investment types in the effective financial data to obtain the client labels of the family clients.
Wherein the preference module comprises:
the tag unit is used for analyzing the correlation between the client tag of the family client and the financial product to obtain a user tag which is strongly correlated with the financial product;
and performing labeling processing on the investment types in the effective financing data to obtain the client labels of the family clients.
Wherein the preference module comprises:
the tag unit is used for analyzing the correlation between the client tag of the family client and the financial product to obtain a user tag which is strongly correlated with the financial product;
and the portrait unit is used for dividing the user tags which are strongly related to the family clients and the financial products into a plurality of levels according to the tag structures of the preset family clients and generating the user portrait of the family clients.
Wherein the recommendation module comprises:
a weighting unit for calculating a product recommendation weight of each financial product relative to the family customers based on the customer figures and the preference degrees;
and the list unit is used for generating a financial product recommendation list corresponding to the family client by the product recommendation weight and the classification model.
The embodiment of the financial product recommendation device provided by the invention can be specifically used for executing the processing flow of the embodiment of the financial product recommendation method in the above embodiment, and the functions thereof are not described herein again, and reference can be made to the detailed description of the embodiment of the method.
As can be seen from the above description, the financial product recommendation device provided in the embodiment of the present invention obtains financial data of a family client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model. The invention can quickly and accurately recommend financial products to users from mass financial products.
The application provides an embodiment of an electronic device for implementing all or part of contents in the financial product recommendation method, wherein the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between related devices; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the embodiment for implementing the method for recommending a financial product and the embodiment for implementing the device for recommending a financial product in the embodiments, which are incorporated herein, and repeated details are not repeated herein.
Fig. 5 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 5, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 5 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the financial product recommendation function may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
acquiring financial data of a family client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model.
As can be seen from the above description, the electronic device provided in the embodiments of the present application obtains financial data of a home client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model. The invention can quickly and accurately recommend financial products to users from mass financial products.
In another embodiment, the financial product recommendation device may be configured separately from the central processor 9100, for example, the financial product recommendation device may be configured as a chip connected to the central processor 9100, and the function of the financial product recommendation device may be implemented under the control of the central processor.
As shown in fig. 5, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 5; further, the electronic device 9600 may further include components not shown in fig. 5, which may be referred to in the art.
As shown in fig. 5, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the financial product recommendation method in the above embodiment, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the financial product recommendation method in the above embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
acquiring financial data of a family client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model.
As can be seen from the above description, the computer-readable storage medium provided by the embodiment of the present invention obtains financial data of a home client; wherein the financial data comprises: the type of investment; performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels; determining a customer representation of each type of the family customer and a preference for different financial products; and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model. The invention can quickly and accurately recommend financial products to users from mass financial products.
Although the present invention provides method steps as described in the examples or flowcharts, more or fewer steps may be included based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, apparatus (system) or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A financial product recommendation method, comprising:
acquiring financial data of a family client; wherein the financial data comprises: the type of investment;
performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering and dividing the financial types of the family clients according to the client labels;
determining a customer representation of each type of the family customer and a preference for different financial products;
and determining a financial product recommendation list corresponding to the family customer based on the customer portrait, the preference degree and a preset classification model.
2. The financial product recommendation method according to claim 1, further comprising, after acquiring financial data of the home client:
cleaning and cross-verifying the financing data to obtain effective financing data;
correspondingly, the step of performing labeling processing on the investment types in the financial data to obtain the client labels of the family clients comprises the following steps:
and performing labeling processing on the investment types in the effective financing data to obtain the client labels of the family clients.
3. The financial product recommendation method of claim 1, wherein said determining a customer image for each type of family customer comprises:
analyzing the correlation between the client label of the family client and the financial product to obtain a user label strongly correlated with the financial product;
according to a preset label structure of the family client, dividing the user labels of the family client, which are strongly related to financial products, into a plurality of levels, and generating a user portrait of the family client.
4. The financial product recommendation method of claim 1, wherein said determining a financial product recommendation list corresponding to a family customer based on said customer representation, said preference and a preset classification model comprises:
calculating a product recommendation weight of each financial product relative to the family customer based on the customer representation and the preference;
and generating a financial product recommendation list corresponding to the family customer by the product recommendation weight and the classification model.
5. The financial product recommendation method of claim 1, further comprising:
acquiring historical data of a family client, training based on the historical data and a Wide & Deep Learning algorithm to obtain a classification model, and specifically comprising the following steps:
splitting historical data into training samples and verification samples; and training the Wide & Deep Learning-based classification model through the training sample, and verifying the trained classification model through the verification sample.
6. The financial product recommendation method according to claim 5, wherein splitting the historical data into training samples and verification samples specifically comprises: and taking the historical data before the set time as a training sample, and taking the historical data after the set time as a verification sample.
7. A financial product recommendation device, comprising:
the acquisition module is used for acquiring financial data of the family client; wherein the financial data comprises: the type of investment;
the label module is used for performing labeling processing on the investment types in the financial data to obtain client labels of the family clients, and clustering the financial types of the family clients according to the client labels;
the preference module is used for determining client figures of each type of family clients and preference degrees of different financial products;
and the recommending module is used for determining a financial product recommending list corresponding to the family client based on the client portrait, the preference degree and a preset classification model.
8. The financial product recommendation device of claim 7, further comprising:
the cleaning module is used for cleaning and cross-verifying the financing data to obtain effective financing data;
correspondingly, the label module comprises:
and the label unit is used for performing labeling processing on the investment types in the effective financial data to obtain the client labels of the family clients.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of recommending a financial product of any of claims 1 to 6 are implemented when the program is executed by the processor.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the financial product recommendation method according to any one of claims 1 to 6.
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