CN112330404A - Data processing method and device, server and storage medium - Google Patents
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Abstract
The embodiment of the application provides a data processing method and device, a server and a storage medium, and relates to the technical field of data processing. The data processing method comprises the following steps: firstly, obtaining a product label to be recommended and a user label; secondly, inputting a product label to be recommended and a user label into a preset recommendation model, and calculating to obtain a matching relation between the product to be recommended and a user; and then, carrying out distribution processing on the products to be recommended according to the matching relation. By the method, distribution can be realized according to the matching relationship between the product to be recommended and the user, and the problem that in the prior art, the product which the user likes to buy is recommended to the user, but the product which the user likes to buy does not necessarily have good profit, even exceeds the risk tolerance range of the user, and the reliability of product recommendation is low is solved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, a server, and a storage medium.
Background
With the rapid development of financial markets, the investment demands of users are increasing day by day, and the number of mass customers is increasing. In order to promote financial product transactions, many financial product recommendation systems are available in the market, and most of the existing financial product recommendation systems recommend products which are like to buy or content information which is like to see to users through information such as user purchase records, asset conditions, information browsing records and the like. However, financial products are characterized by risks and benefits, and products that users like to buy do not always have good benefits, even exceed the risk tolerance range of users, so that the reliability of product recommendation is low.
Disclosure of Invention
In view of the above, an object of the present application is to provide a data processing method and apparatus, a server, and a storage medium, so as to solve the problems in the prior art.
In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:
in a first aspect, the present invention provides a data processing method, including:
obtaining a product label to be recommended and a user label;
inputting the product label to be recommended and the user label into a preset recommendation model, and calculating to obtain a matching relation between the product to be recommended and the user;
and distributing the products to be recommended according to the matching relation.
In an optional implementation manner, the number of the products to be recommended is multiple, the matching relationship includes an investment strategy, and the step of performing allocation processing on the products to be recommended according to the matching relationship includes:
acquiring at least one investment strategy of a plurality of products to be recommended, wherein the investment strategy represents the combination and proportion of the plurality of products to be recommended;
obtaining a target investment strategy from the at least one investment strategy according to the user label;
and selecting a target product to be recommended according to the target investment strategy, and distributing the target product to be recommended to the user.
In an optional embodiment, the step of obtaining the product tag to be recommended and the user tag includes:
acquiring product data of a product and user data of a user;
performing label processing on the product data to obtain a product label to be recommended;
and performing label processing on the user data to obtain a user label.
In an alternative embodiment, the step of obtaining product data of the product and user data of the user includes:
acquiring product related data of a product and user related data of a user;
and preprocessing the product related data to obtain product data, and preprocessing the user related data to obtain user data.
In an optional embodiment, the step of performing label processing on the product data to obtain a label of a product to be recommended includes:
performing characteristic analysis processing on the product data to obtain a product label;
and screening the product labels to obtain the product labels to be recommended.
In an optional embodiment, the step of performing a screening process on the product label to obtain a product label to be recommended includes:
classifying the products according to the product labels to obtain at least one category;
for each category, performing evaluation processing according to the product label of the category to obtain the evaluation of the product label;
and screening according to the evaluation of the product label to obtain the product label to be recommended.
In an optional embodiment, the data processing method further includes:
acquiring feedback data of the user on the distributed products to be recommended;
and updating the preset recommendation model according to the feedback data.
In a second aspect, the present invention provides a data processing apparatus comprising:
the data acquisition module is used for acquiring a product label to be recommended and a user label;
the data calculation module is used for inputting the product label to be recommended and the user label into a preset recommendation model, and calculating to obtain a matching relation between the product to be recommended and the user;
and the product distribution module is used for distributing the products to be recommended according to the matching relation.
In a third aspect, the present invention provides a server, comprising a memory and a processor, wherein the processor is configured to execute an executable computer program stored in the memory to implement the data processing method of any one of the foregoing embodiments.
In a fourth aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed, implements the steps of the data processing method of any one of the preceding embodiments.
According to the data processing method and device, the server and the storage medium, the product label to be recommended and the user label are input into the preset recommendation model, the matching relation between the product to be recommended and the user is obtained through calculation, the product to be recommended is distributed according to the matching relation, the distribution according to the matching relation between the product to be recommended and the user is achieved, the problem that products which like to buy are recommended to the user in the prior art, but the products which the user likes to buy do not necessarily have good benefits, even the risk bearing range of the user is exceeded, and the reliability of product recommendation is low is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram of a data processing system according to an embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application.
Fig. 3 is another schematic flow chart of a data processing method according to an embodiment of the present application.
Fig. 4 is another schematic flow chart of the data processing method according to the embodiment of the present application.
Fig. 5 is another schematic flow chart of the data processing method according to the embodiment of the present application.
Fig. 6 is another schematic flow chart of the data processing method according to the embodiment of the present application.
Fig. 7 is another schematic flow chart of a data processing method according to an embodiment of the present application.
Fig. 8 is another schematic flow chart of a data processing method according to an embodiment of the present application.
Fig. 9 is a block diagram of a data processing apparatus according to an embodiment of the present application.
Icon: 10-a data processing system; 100-a server; 200-a terminal device; 900-a data processing apparatus; 910-a data acquisition module; 920-a data calculation module; 930 — product dispensing module.
Detailed Description
In order to improve at least one of the above technical problems proposed by the present application, embodiments of the present application provide a data processing method and apparatus, a server, and a storage medium, and the following describes technical solutions of the present application through possible implementation manners.
The defects of the above solutions are the results of the inventor after practice and careful study, and therefore, the discovery process of the above problems and the solution proposed by the present application to the above problems should be the contribution of the inventor to the present application in the process of the present application.
For purposes of making the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
In order to enable a person skilled in the art to make use of the present disclosure, the following embodiments are given. It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.
It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Fig. 1 is a block diagram of a data processing system 10 provided in an embodiment of the present application, which provides a possible implementation manner of the data processing system 10, and referring to fig. 1, the data processing system 10 may include one or more of a server 100 and a terminal device 200.
The server 100 is in communication connection with the terminal device 200 to obtain data sent by the terminal device 200 for processing, and send a product to be recommended to the terminal device 200, where the terminal device 200 is configured to push the product to be recommended to a user.
For the server 100, it should be noted that, in some embodiments, the server 100 may be a single server 100 or a server group. The set of servers may be centralized or distributed (e.g., server 100 may be a distributed system). In some embodiments, the server 100 may be local or remote to the terminal device 200. For example, the server 100 may access information and/or data stored in the terminal device 200 via a network. As another example, the server 100 may be directly connected to the terminal device 200 to access stored information and/or data. In some embodiments, the server 100 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a resilient cloud, a community cloud (community cloud), a distributed cloud, a cross-cloud (inter-cloud), a multi-cloud (multi-cloud), and the like, or any combination thereof. In some embodiments, the server 100 may be implemented on the terminal device 200.
In some embodiments, the server 100 may include a processor. The processor may process information and/or data transmitted by terminal device 200 to perform one or more of the functions described herein. In some embodiments, a processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set computer (Reduced Instruction Set computer, RISC), a microprocessor, or the like, or any combination thereof.
The network may be used for the exchange of information and/or data. In some embodiments, one or more components in data processing system 10 (e.g., server 100 and terminal device 200) may send information and/or data to other components. For example, the server 100 may acquire data from the terminal device 200 via a network. In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, the Network may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof.
In some embodiments, the network may include one or more network access points. For example, a network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of data processing system 10 may connect to the network to exchange data and/or information.
A database may be included in server 100 and may store data and/or instructions. In some embodiments, the database may store data obtained from the terminal device 200. In some embodiments, a database may store data and/or instructions for the exemplary methods described herein. In some embodiments, the database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, the database may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, cross-cloud, multi-cloud, elastic cloud, or the like, or any combination thereof.
In some embodiments, the database may be connected to a network to communicate with one or more components in the data processing system 10 (e.g., the server 100 and the terminal device 200). One or more components in data processing system 10 may access data or instructions stored in a database via a network. In some embodiments, the database may be directly connected to one or more components in data processing system 10 (e.g., server 100 and terminal device 200). Alternatively, in some embodiments, the database may also be part of the server 100. In some embodiments, one or more components in data processing system 10 (e.g., server 100 and terminal device 200) may have access to a database.
Fig. 2 shows one of flowcharts of a data processing method provided in an embodiment of the present application, where the method is applicable to the server 100 shown in fig. 1 and is executed by the server 100 in fig. 1. It should be understood that, in other embodiments, the order of some steps in the data processing method of this embodiment may be interchanged according to actual needs, or some steps may be omitted or deleted. The flow of the data processing method shown in fig. 2 is described in detail below.
Step S210, obtaining a product label to be recommended and a user label.
Step S220, inputting the label of the product to be recommended and the label of the user into a preset recommendation model, and calculating to obtain the matching relation between the product to be recommended and the user.
And step S230, distributing the products to be recommended according to the matching relation.
According to the method, the product label to be recommended and the user label are input into the preset recommendation model, the matching relation between the product to be recommended and the user is obtained through calculation, the product to be recommended is distributed according to the matching relation, the distribution according to the matching relation between the product to be recommended and the user is realized, and the problem that in the prior art, the product which the user likes to buy is recommended to the user, but the product which the user likes to buy does not necessarily have good benefits, and even exceeds the risk bearing range of the user, so that the reliability of product recommendation is low is solved.
For step S210, it should be noted that the specific manner of obtaining the tag is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S210 may include a step of performing label processing. Therefore, on the basis of fig. 2, fig. 3 is a schematic flowchart of another data processing method provided in the embodiment of the present application, and referring to fig. 3, step S210 may include:
step S211, product data of the product and user data of the user are acquired.
And step S212, performing label processing on the product data to obtain a product label to be recommended.
Step S213, performs label processing on the user data to obtain a user label.
For step S211, it should be noted that the specific manner of acquiring data is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S211 may include a step of performing preprocessing. Therefore, on the basis of fig. 3, fig. 4 is a schematic flowchart of another data processing method provided in the embodiment of the present application, and referring to fig. 4, step S211 may include:
step S2111, product related data of the product and user related data of the user are acquired.
In detail, the specific type of the product is not limited, and can be set according to the actual application requirements. For example, in embodiments of the present application, the products may include financial products and information. The product related data of the financial product refers to basic information, market data and derivative index data of the financial product, and the basic information can comprise a product name, a product type, a product investment range, a product scale and the like; the market data may include closing price, profitability, etc. of the financial product; the derivative index data may include maximum withdrawal, sharp rate, volatility, etc. of the financial product. The product-related data of the information refers to information data related to the financial product, and may include data such as announcements, news reports, and comments of the financial product. It should be noted that the data can be collected through a financial database and the internet. The user-related data refers to behavior data generated when the user uses the terminal device 200, and may include financial product transaction records, information browsing records, basic information of the user, and the like.
Step S2112, the product data is obtained by preprocessing the product related data, and the user data is obtained by preprocessing the user related data.
In detail, the step of pre-processing may include: firstly, for text data, the text data needs to be decoded, character converted, word segmented and the like, and then converted into structured data; secondly, processing abnormal values of the structured data, including missing value processing, extreme value processing and the like.
For step S212, it should be noted that the specific way of performing the label processing on the product data is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S212 may include a step of performing a screening process. Therefore, on the basis of fig. 3, fig. 5 is a schematic flowchart of another data processing method provided in the embodiment of the present application, and referring to fig. 5, step S212 may include:
and step S2121, performing characteristic analysis processing on the product data to obtain a product label.
In detail, the financial product label can obtain a characteristic description of the financial product through characteristic analysis, which may include a profit characteristic, a risk characteristic, a liquidity characteristic, a technical surface characteristic, and the like, and the characteristic may be generated according to a preset financial engineering model and historical performance of the financial product. The information tag can obtain the characteristic description of the information through natural language processing technology, and can comprise the financial product main body, event type, emotion intensity and the like related to the information.
And S2122, screening the product labels to obtain the product labels to be recommended.
In detail, the financial products in the whole market can be screened according to financial product labels, quantitative analysis models, researchers and the point of view of putting into service, high-quality financial products are obtained and added into a product library, products to be recommended are all from the product library, and the comprehensive income of the financial products recommended to users is improved.
For step S2122, it should be noted that the specific manner of performing the screening process is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S2122 may include a step of performing evaluation processing. Therefore, on the basis of fig. 5, fig. 6 is a schematic flowchart of another data processing method provided in the embodiment of the present application, and referring to fig. 6, step S2122 may include:
and S2122-1, classifying the products according to the product labels to obtain at least one category.
In detail, the financial products in the whole market may be classified into different categories, and the classification manner may be shown in table 1, taking the fund products as an example.
TABLE 1 fund product category classification type table
And S2122-2, evaluating each category according to the product label of the category to obtain the evaluation of the product label.
In detail, in each category, a multi-dimensional evaluation scoring schema may be formulated according to the characteristic labels of the financial products. For example, the financial products with the value of the "profitability" label ranked 50% first may be scored by 100, the financial products with the value of the "profitability" label ranked 50% last may be scored by 0, the other labels may also be formulated into scoring rules, and finally, the scores of each feature label are weighted and summed to obtain the evaluation score of each product label.
And S2122-3, screening according to the evaluation of the product label to obtain a product label to be recommended.
In detail, financial products that are evaluated well can be screened out according to the evaluation score of each product label, and researchers and investment consultants can further screen and audit the financial products according to product investigation and qualitative analysis, and the financial products that are approved are brought into a product library. When the product is a financial product, whether the product label is in the product library can be directly judged, and the product label of the product in the product library is used as a data set of the financial product label to be recommended. When the product is information, the information related to the product in the product library can be screened, and the related information tag is used as a data set of the information tag to be recommended.
For step S213, it should be noted that the user tag may obtain a feature description of the user by performing feature analysis on the user data, and the user tag in this embodiment may include a behavior feature tag, an investment bias tag, and an expected risk income tag. The behavior feature tags represent the behavior preferences of the user, such as financial product purchasing preferences, information browsing preferences and the like of the user. The investment bias label is a shortage in the aspect of obtaining the investment of the user based on the investment characteristic analysis of the user, and after the investment bias label is obtained, a targeted investment suggestion can be provided for the user. The investment bias labels can be obtained by constructing artificial intelligent model analysis on the investment behaviors of the users, such as buying and selling test points, stock holding preference and asset configuration characteristics. The expected risk benefit label can comprise the risk level and benefit target of the user, and the label is generally obtained by filling out a questionnaire when the user initially opens the business, and can be properly adjusted and updated according to the purchase record of the financial product.
For step S220, it should be noted that, in an alternative example, when the matching relationship includes a matching degree, the matching degree between the user and the financial product may be obtained according to the built preset recommendation model. The behavior feature labels, the investment bias labels and the expected risk income labels of the users form a user label data set, and the matching degree between the users and the financial products is calculated according to a recommendation algorithm of a preset recommendation model, and specifically the matching degree between one user and one product, the matching degree between one product and one class of users, the matching degree between one user and one class of products and the matching degree between one class of products and one class of users can be included. Recommendation algorithms may include collaborative filtering, association rule based recommendations, and the like.
In another alternative example, the matching degree between the user and the information can be obtained according to a preset recommendation model. The matching degree between the user and the information content can be calculated according to a recommendation algorithm of a preset recommendation model, and the matching degree between the user and one piece of information, the matching degree between the information and one type of user, the matching degree between the user and one type of information and the matching degree between one type of information and one type of user can be specifically calculated.
For step S230, it should be noted that the specific manner of performing the allocation processing is not limited, and may be set according to the actual application requirement. For example, in an alternative example, when the matching relationship includes a degree of matching, the assignment may be made directly according to the degree of matching. That is, the financial products and/or information with high matching degree can be directly distributed to the users.
For another example, in another alternative example, when the matching relationship includes an investment policy, the step S230 may include a step of allocating according to the investment policy. Therefore, on the basis of fig. 2, fig. 7 is a schematic flowchart of another data processing method provided in the embodiment of the present application, and referring to fig. 7, step S230 may include:
and step S231, acquiring at least one investment strategy of a plurality of products to be recommended.
Wherein the investment strategy characterizes the combination and proportions of a plurality of products to be recommended. In detail, an investment strategy can be generated according to a quantitative model or a manual configuration method, and the investment strategy is a configuration suggestion for various financial assets in the current investment market, for example, a certain proportion is configured for a certain financial asset. The strategy models stored in the investment strategy library are strategy groups, and each strategy model generates different strategy results according to expected risk income labels of users.
The generation of the investment strategy library can comprise two modes, corresponding to two sub-modules, namely an intelligent investment module and a manual investment module. The intelligent casting and caring module can generate an investment strategy through an intelligent casting and caring strategy model, and the intelligent casting and caring strategy model can comprise a risk flat price model, a two-eight rotation model, a Markov model and the like. The manual commissioning module can configure the investment strategy through manual customization, and can obtain the strategy result added into the investment strategy library by a researcher or an investment advisor so as to update the strategy result in the investment strategy library.
Step S232, a target investment strategy is obtained from at least one investment strategy according to the user label.
In detail, the target investment strategy can be screened in the investment strategy library according to the investment bias label and the expected risk income label of the user. For example, for a user with a high investment risk, a risk balanced investment strategy may be matched to improve the investment risk of the user.
And step S233, selecting the target product to be recommended according to the target investment strategy, and distributing the target product to be recommended to the user.
In detail, the investment strategy result of the user can be calculated according to the expected risk income label of the user, the matched target investment strategy and the position taken analysis of the user, and the investment strategy result generally represents the configuration proportion of each financial product. Firstly, calculating a configuration result of an investment strategy according to an expected risk income label of a user, and then comparing the position taken by the user with the configuration result of the investment strategy to obtain a difference item which is a final investment strategy result of the user.
And screening the final target products to be recommended (the target financial products and the target information) of the user according to the final investment strategy result of the user in the products to be recommended (the financial products and the information) of the user.
After step S230, the data processing method provided in the embodiment of the present application may further include a step of performing update processing on the preset recommendation model. Therefore, on the basis of fig. 2, fig. 8 is a schematic flowchart of another data processing method provided in the embodiment of the present application, and referring to fig. 8, the data processing method may further include:
step S240, feedback data of the user to the distributed products to be recommended are obtained.
In detail, the feedback data may include a user's browsing record of the distributed products to be recommended. The terminal device 200 can collect the browsing records of the user on the distributed financial products and information contents, and feed back the browsing records to the server 100.
And step S250, updating the preset recommendation model according to the feedback data.
In detail, the server 100 may perform recommendation matching optimization recalculation on a preset recommendation model according to the browsing record, and update the recommendation result (the distributed product to be recommended) of the user.
Further, the server 100 provided in the embodiment of the present application may include a traffic distribution module, which is a traffic distribution middlebox for the recommendation result, and implements functions of fast query of the recommendation result, custom tag screening, configuration of a push scheme, and pushing of the recommended financial product and information. The pushing scheme is a recommendation scheme made for user-defined user groups, financial products and information. That is, the investment consultant or the salesperson can set parameters such as recommended matching schemes, recommended financial products, recommended users and the like through the traffic distribution center station to determine the distribution contents and objects.
The embodiment of the application provides an intelligent customer-throwing type financial product and information pushing method, which integrates an intelligent customer-throwing model into a financial product and information recommending system, and recommends contents such as good income, risk matching, products, investment suggestions and product information wanted to be bought by a user based on an intelligent customer-throwing mode on the premise of ensuring the appropriateness of compliance, so that the quality and user experience of product and information pushing are improved.
With reference to fig. 9, an embodiment of the present application further provides a data processing apparatus 900, where the functions implemented by the data processing apparatus 900 correspond to the steps executed by the foregoing method. The data processing apparatus 900 may be understood as a processor of the server 100, or may be understood as a component that is independent of the server 100 or a processor and that implements the functions of the present application under the control of the server 100. The data processing apparatus 900 may include, among other things, a data acquisition module 910, a data calculation module 920, and a product assignment module 930.
And the data acquisition module 910 is configured to acquire a product tag to be recommended and a user tag. In the embodiment of the present application, the data obtaining module 910 may be configured to perform step S210 shown in fig. 2, and for relevant contents of the data obtaining module 910, reference may be made to the foregoing description of step S210.
And the data calculation module 920 is configured to input the product label to be recommended and the user label into a preset recommendation model, and calculate to obtain a matching relationship between the product to be recommended and the user. In this embodiment of the application, the data calculation module 920 may be configured to perform step S220 shown in fig. 2, and reference may be made to the foregoing description of step S220 regarding relevant contents of the data calculation module 920.
And a product allocation module 930, configured to allocate the to-be-recommended product according to the matching relationship. In the embodiment of the present application, the product dispensing module 930 may be configured to perform step S230 shown in fig. 2, and the related content of the product dispensing module 930 may refer to the description of step S230.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the data processing method.
The computer program product of the data processing method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute steps of the data processing method in the above method embodiment, which may be referred to specifically in the above method embodiment, and are not described herein again.
To sum up, according to the data processing method and apparatus, the server and the storage medium provided in the embodiments of the present application, the product tag to be recommended and the user tag are input into the preset recommendation model, the matching relationship between the product to be recommended and the user is obtained through calculation, and the product to be recommended is distributed according to the matching relationship, so that the distribution according to the matching relationship between the product to be recommended and the user is realized, and the problem of low reliability of product recommendation caused by the fact that a product that the user likes to buy is recommended to the user in the prior art, but the product that the user likes to buy does not necessarily have good profit, or even exceeds the risk tolerance range of the user is solved.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A data processing method, comprising:
obtaining a product label to be recommended and a user label;
inputting the product label to be recommended and the user label into a preset recommendation model, and calculating to obtain a matching relation between the product to be recommended and the user;
and distributing the products to be recommended according to the matching relation.
2. The data processing method according to claim 1, wherein the number of the products to be recommended is plural, the matching relationship includes an investment strategy, and the step of performing the distribution processing on the products to be recommended according to the matching relationship includes:
acquiring at least one investment strategy of a plurality of products to be recommended, wherein the investment strategy represents the combination and proportion of the plurality of products to be recommended;
obtaining a target investment strategy from the at least one investment strategy according to the user label;
and selecting a target product to be recommended according to the target investment strategy, and distributing the target product to be recommended to the user.
3. The data processing method of claim 1, wherein the step of obtaining the product label to be recommended and the user label comprises:
acquiring product data of a product and user data of a user;
performing label processing on the product data to obtain a product label to be recommended;
and performing label processing on the user data to obtain a user label.
4. The data processing method of claim 3, wherein the step of acquiring product data of the product and user data of the user comprises:
acquiring product related data of a product and user related data of a user;
and preprocessing the product related data to obtain product data, and preprocessing the user related data to obtain user data.
5. The data processing method of claim 3, wherein the step of performing label processing on the product data to obtain a label of a product to be recommended comprises:
performing characteristic analysis processing on the product data to obtain a product label;
and screening the product labels to obtain the product labels to be recommended.
6. The data processing method of claim 5, wherein the step of screening the product tags to obtain the product tags to be recommended comprises:
classifying the products according to the product labels to obtain at least one category;
for each category, performing evaluation processing according to the product label of the category to obtain the evaluation of the product label;
and screening according to the evaluation of the product label to obtain the product label to be recommended.
7. The data processing method of claim 1, wherein the data processing method further comprises:
acquiring feedback data of the user on the distributed products to be recommended;
and updating the preset recommendation model according to the feedback data.
8. A data processing apparatus, comprising:
the data acquisition module is used for acquiring a product label to be recommended and a user label;
the data calculation module is used for inputting the product label to be recommended and the user label into a preset recommendation model, and calculating to obtain a matching relation between the product to be recommended and the user;
and the product distribution module is used for distributing the products to be recommended according to the matching relation.
9. A server, comprising a memory and a processor for executing an executable computer program stored in the memory to implement the data processing method of any one of claims 1 to 7.
10. A storage medium, characterized in that a computer program is stored thereon, which when executed performs the steps of the data processing method of any one of claims 1-7.
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