CN116664227A - Intelligent recommendation method and device for financial products - Google Patents

Intelligent recommendation method and device for financial products Download PDF

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Publication number
CN116664227A
CN116664227A CN202310545227.3A CN202310545227A CN116664227A CN 116664227 A CN116664227 A CN 116664227A CN 202310545227 A CN202310545227 A CN 202310545227A CN 116664227 A CN116664227 A CN 116664227A
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user
intelligent
information
financial
recommendation
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张茜
胡松鄂
纪宏民
苗天
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The embodiment of the application provides an intelligent recommendation method and device for financial products, which relate to the field of meta universe and can also be used in the digital twin field or the financial field, and the method comprises the following steps: acquiring label information, track information and interaction information generated by a user in a virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user; the application can effectively improve the marketing efficiency and accuracy of the financial products.

Description

Intelligent recommendation method and device for financial products
Technical Field
The application relates to the field of meta universe, and also can be used in the field of digital twinning or finance, in particular to an intelligent recommending method and device for financial products.
Background
Along with the rapid development and application of the AI technology and the 5G technology, the meta universe realizes the interactive fusion of the virtual world and the real world, and the immersion experience and the sense of reality brought to customers by the meta universe are utilized to rapidly fire out the circle, so far, the technology has wide prospect and huge potential in the continuous evolution and rich development, and new power is continuously injected into the emerging digital consumer market.
In the world today, the number of customers and employees of the Z generation that banks need to contact is increasing, and this part of the customers group has strong acceptance of new things and has higher requirements for financial services. Therefore, the bank quickens exploration and layout of the metauniverse, the virtual business hall starts to become a hot spot for taking root of the bank, virtual, infinite and vivid interactive experience scenes are provided for bank clients by providing immersive financial services, the viscosity and the good grade of the clients are improved while the business channels are widened, and the virtual business hall becomes an important incision for the capability of the banks to acquire and reserve the clients.
The virtual business hall is a measure for exploring construction of the intelligent network points of the meta-universe, changes the mode of the offline interaction of clients in the traditional financial scene, breaks through the limitation of time and space, can effectively reduce the operation cost of the network points of banks, and liberates the manpower of the network points. In the virtual business hall, by fusing the virtual digital man technology, a customer can visit the virtual network point by adopting a first person view angle to know financial products and services in a viewing and listening mode, and the customer can perform text interaction with the virtual digital customer service to know interesting business contents.
Most banks currently search the virtual business hall in an initial stage, and the financial scene provided for clients is single and needs to be further searched. After a customer arrives at a banking site, the customer often expects that the bank can recommend certain targeted, customized financial products and services for himself. However, intelligent recommendation of bank products still mostly adopts a mode of extracting recommendation only based on the attribute of a client tag or only based on content, the intelligent degree is to be improved, after a bank starts to lay out a meta universe, off-line network points are converted into on-line network points, the bank builds a virtual business hall in the meta universe, less value added services are provided for clients, the intelligent recommendation method is widely applied to the aspects of centralized propaganda display and marketing of financial products, and mass display of the products and simple authentication login of the clients cannot achieve accurate recommendation of the financial products and services in the virtual business hall of the bank.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides the intelligent recommendation method and the intelligent recommendation device for the financial products, which can effectively improve the marketing efficiency and the accuracy of the financial products.
In order to solve at least one of the problems, the application provides the following technical scheme:
in a first aspect, the present application provides an intelligent recommendation method for financial products, including:
acquiring label information, track information and interaction information generated by a user in a virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation;
according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation;
inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user.
Further, the collecting label information, track information and interaction information generated by the user in the virtual business hall, and taking the label information, track information and interaction information as key elements of intelligent product recommendation includes:
And acquiring basic tag attribute contents selected by a user at an entrance of the virtual business hall, and determining the basic tag attribute contents as a first key element of intelligent product recommendation.
Further, the collecting label information, track information and interaction information generated by the user in the virtual business hall, and taking the label information, track information and interaction information as key elements of intelligent product recommendation includes:
and acquiring access point position data and click point position data of the user in the operation process of the virtual business hall, and taking the access point position data and the click point position data as second key elements of intelligent product recommendation.
Further, the collecting label information, track information and interaction information generated by the user in the virtual business hall, and taking the label information, track information and interaction information as key elements of intelligent product recommendation includes:
and acquiring text content of the user in the interaction process of the virtual digital person, acquiring key financial information in the text content, and taking the key financial information as a third key element of intelligent product recommendation.
Further, the method further comprises the following steps:
And carrying out information preprocessing operation on the text content, wherein the information preprocessing operation comprises filtering interaction information irrelevant to financial requirements.
Further, the data matching is performed according to the attribute content of the basic tag and a setting database, the user portrait of the user is determined, and the user portrait is used as another key element of intelligent product recommendation, which comprises the following steps:
and carrying out data matching according to the attribute content of the basic tag and a set user asset database, determining the financial user portrait of the user, and taking the financial user portrait as a fourth key element of intelligent product recommendation.
Further, the data matching is performed according to the attribute content of the basic tag and a setting database, the user portrait of the user is determined, and the user portrait is used as another key element of intelligent product recommendation, which comprises the following steps:
and carrying out data matching according to the attribute content of the basic tag and a set user social relation database, determining a non-financial user portrait of the user, and taking the non-financial user portrait as a fifth key element of intelligent product recommendation.
Further, the inputting all key elements of the intelligent product recommendation into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending to the user includes:
Inputting the first key element, the second key element, the third key element, the fourth key element and the fifth key element into a preset marketing sensitivity rule model;
and determining financial products corresponding to the user through the business recommendation strategy and recommendation rules of the marketing sensitive rule model and recommending the financial products to the user.
In a second aspect, the present application provides an intelligent recommendation device for financial products, comprising:
the information acquisition module is used for acquiring label information, track information and interaction information generated by a user in the virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation;
the data matching module is used for carrying out data matching according to the attribute content of the basic tag and a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation;
and the product recommending module is used for inputting all key elements of intelligent product recommending into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending to the user.
Further, the information acquisition module includes:
And the first key element determining unit is used for acquiring basic tag attribute contents selected by a user at the entrance of the virtual business hall and determining the basic tag attribute contents as first key elements of intelligent product recommendation.
Further, the information acquisition module further includes:
the second key element determining unit is used for collecting the access point position data and the click point position data of the user in the operation process of the virtual business hall, and taking the access point position data and the click point position data as second key elements for intelligent product recommendation.
Further, the information acquisition module further includes:
and the third key element determining unit is used for acquiring text contents of the user in the interaction process of the virtual digital person, acquiring key financial information in the text contents and taking the key financial information as a third key element for intelligent product recommendation.
Further, the information acquisition module further includes:
and the information preprocessing unit is used for carrying out information preprocessing operation on the text content, wherein the information preprocessing operation comprises filtering interaction information irrelevant to financial requirements.
Further, the data matching module includes:
And the fourth key element determining unit is used for performing data matching with a set user asset database according to the attribute content of the basic tag, determining the financial user portrait of the user, and taking the financial user portrait as a fourth key element for intelligent product recommendation.
Further, the data matching module further includes:
and the fifth key element determining unit is used for performing data matching with a set user social relation database according to the attribute content of the basic tag, determining the non-financial user portrait of the user, and taking the non-financial user portrait as a fifth key element for intelligent product recommendation.
Further, the product recommendation module includes:
the model input unit is used for inputting the first key element, the second key element, the third key element, the fourth key element and the fifth key element into a preset marketing sensitivity rule model;
and the model recommending unit is used for determining the financial products corresponding to the user through the business recommending strategy and recommending rules of the marketing sensitive rule model and recommending the financial products to the user.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the intelligent recommendation method for financial products when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the intelligent recommendation method for financial products.
In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of the intelligent recommendation method for financial products.
According to the technical scheme, the intelligent recommending method and the intelligent recommending device for the financial products are provided, label information, track information and interaction information generated by a user in a virtual business hall are collected, and the label information, the track information and the interaction information are used as key elements of intelligent recommending of the products; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an intelligent recommendation method for financial products according to an embodiment of the application;
FIG. 2 is a second flowchart of an intelligent recommendation method for financial products according to an embodiment of the application;
FIG. 3 is a diagram showing one of the construction of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 4 is a second block diagram of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 5 is a third block diagram of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 6 is a diagram showing a structure of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 7 is a diagram showing a structure of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 8 is a diagram showing a structure of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 9 is a diagram showing a structure of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 10 is a diagram illustrating a structure of an intelligent recommendation device for financial products according to an embodiment of the present application;
FIG. 11 is a block diagram of a financial product intelligent recommendation system in accordance with an embodiment of the present application;
FIG. 12 is a flow chart of user portrayal construction in an embodiment of the application;
fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
According to the technical scheme, the data are acquired, stored, used and processed according with relevant regulations of laws and regulations.
In consideration of the problems existing in the prior art, the application provides an intelligent recommendation method and device for financial products, which are characterized in that label information, track information and interaction information generated by a user in a virtual business hall are collected, and the label information, the track information and the interaction information are used as key elements of intelligent recommendation of the products; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
In order to effectively improve marketing efficiency and accuracy of financial products, the application provides an embodiment of an intelligent financial product recommendation method, referring to fig. 1, which specifically comprises the following steps:
step S101: and collecting label information, track information and interaction information generated by a user in the virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation.
Step S102: and carrying out data matching according to the attribute content of the basic tag and a setting database, determining the user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation.
Step S103: inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user.
As can be seen from the above description, the intelligent recommendation method for financial products provided by the embodiment of the present application can collect tag information, track information and interaction information generated by a user in a virtual business hall, and use the tag information, track information and interaction information as key elements for intelligent recommendation of products; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
In an embodiment of the intelligent recommendation method for financial products of the present application, the step S101 may further specifically include the following:
and acquiring basic tag attribute contents selected by a user at an entrance of the virtual business hall, and determining the basic tag attribute contents as a first key element of intelligent product recommendation.
Optionally, the application can set a label information acquisition module at the login entrance of the created banking meta-universe virtual business hall, and after the user logs in the virtual business hall, the label system acquisition module acquires basic label attribute contents selected by the user at the entrance of the virtual business hall, wherein the basic label attribute contents comprise personal identity information (mobile phone number, name and the like) of the user and the label contents selected by the login, and the basic label attribute contents are used as first key elements of intelligent product recommendation. The label content of the customer login selection comprises customer identity selection, such as social people, students in school, financial workers, internet IT (information technology) staff and the like; customer points of interest such as product awareness, business handling, others, etc.
In an embodiment of the intelligent recommendation method for financial products of the present application, the step S101 may further specifically include the following:
and acquiring access point position data and click point position data of the user in the operation process of the virtual business hall, and taking the access point position data and the click point position data as second key elements of intelligent product recommendation.
Optionally, in the process that the client logs in the meta-universe virtual business hall, the track information generation module of the virtual business hall periodically acquires the data of the access point position and the clicking point position of the user, and the access point position information comprises the action track of the client in the virtual business hall, such as the arrived scene name, the accessed product propaganda material name and the like, and is used as a second key element of intelligent product recommendation. The user uses meta-universe to continuously update the point location acquisition data.
In an embodiment of the intelligent recommendation method for financial products of the present application, the step S101 may further specifically include the following:
and acquiring text content of the user in the interaction process of the virtual digital person, acquiring key financial information in the text content, and taking the key financial information as a third key element of intelligent product recommendation.
Optionally, the application can collect all text contents in the process of interaction between the client and the virtual digital person in the virtual business hall, realize information preprocessing based on the text contents, filter the interaction information irrelevant to the financial requirement, acquire the key financial information of the interaction, such as business of consultation and the like, and know products and the like, and serve as a third key element of intelligent recommendation of the products. Each virtual digital person access operation of the user triggers the acquisition module to update data.
The database comprises an enterprise internal database, an Internet and partner database and a meta universe database.
And the intelligent recommendation information acquisition module is used for uploading acquired data to the meta space database.
In an embodiment of the intelligent recommendation method for financial products of the present application, the step S101 may further specifically include the following:
and carrying out information preprocessing operation on the text content, wherein the information preprocessing operation comprises filtering interaction information irrelevant to financial requirements.
In an embodiment of the intelligent recommendation method for financial products of the present application, the step S102 may further specifically include the following:
and carrying out data matching according to the attribute content of the basic tag and a set user asset database, determining the financial user portrait of the user, and taking the financial user portrait as a fourth key element of intelligent product recommendation.
Optionally, the application can acquire the data content of the mobile phone number, the name, the label and the like of the target client collected by the meta universe database, match with the enterprise internal database, acquire the financial information of the client, such as the information of the asset scale (including the loan deposit funds in the client line, the information of the held product information in the bank, the transacted financial service, the consumption condition, the bank account funds, etc.), the definition of the client group label and the like, analyze the asset grading, the financial requirement, the channel preference, the transaction preference, the product preference, the investment preference and the like of the client through deep mining and data analysis by means of the personal information and the basic label information of the client based on the collected line data, and perfect the financial digital label of the client as the fourth key element of intelligent product recommendation. Wherein the asset rating is high, medium, low; the channel preference comprises off-line channels (off-line counter, off-self-service terminal, off-manual handling), off-line channels (off-line channels such as off-line mobile banking, off-line WeChat applet, etc.); investment preferences include financial products, annual regular deposits, long term regular deposits, demand deposits, and the like.
In an embodiment of the intelligent recommendation method for financial products of the present application, the step S102 may further specifically include the following:
and carrying out data matching according to the attribute content of the basic tag and a set user social relation database, determining a non-financial user portrait of the user, and taking the non-financial user portrait as a fifth key element of intelligent product recommendation.
Optionally, the application can utilize big data analysis technology, based on the acquired customer group label definition and customer personal information, match the internet and other data asset information outside the line, extract the customer non-financial digital label, including the contents of customer work, relation demand, life demand, etc., perfect the customer non-financial digital label and portrait, as the fifth key element of intelligent recommendation of the product. The living demands include buying a car, buying a house, renting a house, etc.
In an embodiment of the intelligent recommendation method for financial products of the present application, referring to fig. 2, the step S103 may further specifically include the following:
step S201: and inputting the first key element, the second key element, the third key element, the fourth key element and the fifth key element into a preset marketing sensitivity rule model.
Step S202: and determining financial products corresponding to the user through the business recommendation strategy and recommendation rules of the marketing sensitive rule model and recommending the financial products to the user.
Optionally, the application can extract the access point position, click point position data and language interaction data stored in the meta space database from the meta space database on time, namely, the first key element (client basic information and selection label), the second key element (client meta space point position access data), the third key element (client meta space interaction text data), the fourth key element (client financial digital label) and the fifth key element (client non-financial digital label) are used as the input of the marketing sensitive rule model together, and the strategy in the model is judged and predicted through a certain service recommendation strategy and formulated recommendation logic and recommendation rule. The service recommendation strategy and recommendation rule can be obtained by combining manual experience and adopting machine learning, federal learning and other technical training.
Optionally, the application can output the introduction and the handling flow prompt of the proper business products and services to the user through the user preference analysis module as the final recommendation result, thereby realizing the intelligent recommendation of the financial products facing the target clients in the meta-universe scene.
In order to effectively improve marketing efficiency and accuracy of financial products, the application provides an embodiment of a financial product intelligent recommendation device for implementing all or part of contents of the financial product intelligent recommendation method, referring to fig. 3, the financial product intelligent recommendation device specifically comprises the following contents:
the information collection module 10 is configured to collect tag information, track information and interaction information generated by a user in the virtual business hall, and take the tag information, the track information and the interaction information as key elements of intelligent product recommendation.
And the data matching module 20 is used for carrying out data matching according to the attribute content of the basic tag and a setting database, determining the user portrait of the user and taking the user portrait as another key element of intelligent product recommendation.
And the product recommending module 30 is used for inputting all key elements of the intelligent product recommendation into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user.
As can be seen from the above description, the intelligent recommendation device for financial products provided by the embodiment of the application can collect the label information, the track information and the interaction information generated by the user in the virtual business hall, and take the label information, the track information and the interaction information as key elements of intelligent recommendation of the products; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 4, the information collecting module 10 includes:
the first key element determining unit 11 is configured to obtain basic tag attribute content selected by a user at an entrance of the virtual business hall, and determine the basic tag attribute content as a first key element of intelligent product recommendation.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 5, the information collecting module 10 further includes:
and the second key element determining unit 12 is used for collecting the access point position data and the click point position data of the user in the operation process of the virtual business hall, and taking the access point position data and the click point position data as second key elements for intelligent product recommendation.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 6, the information collecting module 10 further includes:
and the third key element determining unit 13 is used for acquiring text contents of the user in the interaction process of the virtual digital person, acquiring key financial information in the text contents, and taking the key financial information as a third key element for intelligent product recommendation.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 7, the information collecting module 10 further includes:
an information preprocessing unit 14, configured to perform an information preprocessing operation on the text content, where the information preprocessing operation includes filtering interactive information irrelevant to financial requirements.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 8, the data matching module 20 includes:
and the fourth key element determining unit 21 is used for performing data matching with a set user asset database according to the attribute content of the basic tag, determining the financial user portrait of the user, and taking the financial user portrait as a fourth key element for intelligent product recommendation.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 9, the data matching module 20 further includes:
and the fifth key element determining unit 22 is configured to determine a non-financial user portrait of the user according to the basic tag attribute content and perform data matching with a social relationship database of the set user, and use the non-financial user portrait as a fifth key element for intelligent product recommendation.
In an embodiment of the intelligent recommendation apparatus for financial products of the present application, referring to fig. 10, the product recommendation module 30 includes:
the model input unit 31 is configured to input the first key element, the second key element, the third key element, the fourth key element, and the fifth key element into a preset marketing sensitivity rule model.
And the model recommending unit 32 is used for determining the financial products corresponding to the user through the business recommending strategies and recommending rules of the marketing sensitive rule model and recommending the financial products to the user.
In order to further explain the present solution, the present application further provides a specific application example of a system for implementing the intelligent recommendation method for financial products by using the intelligent recommendation device for financial products, referring to fig. 11, which specifically includes the following contents:
the intelligent recommendation information acquisition module comprises a label information acquisition module, a virtual business hall track information generation module and a virtual digital human-language interaction acquisition module.
a) The method comprises the steps that a label information acquisition module is arranged at a login entrance of a created banking element universe virtual business hall, after a user logs in the virtual business hall, a label system acquisition module acquires basic label attribute contents selected by the user at the entrance of the virtual business hall, wherein the basic label attribute contents comprise personal identity information (mobile phone number, name and the like) of the user and the label contents selected by the login, and the basic label attribute contents are used as first key elements of intelligent product recommendation. The label content of the customer login selection comprises customer identity selection, such as social people, students in school, financial workers, internet IT (information technology) staff and the like; customer points of interest such as product awareness, business handling, others, etc.
b) In the process that the customer logs in the meta-universe virtual business hall, the virtual business hall track information generation module collects data of access points and clicking points of the user at regular time, and the access point information comprises the action tracks of the customer in the virtual business hall, such as the arrived scene names, the accessed product propaganda material names and the like, and is used as a second key element of intelligent product recommendation. The user uses meta-universe to continuously update the point location acquisition data.
c) The language interaction acquisition module acquires all text contents in the process of interaction between a client and a virtual digital person in a virtual business hall, and performs information preprocessing based on the text contents, filters interaction information irrelevant to financial requirements, and acquires key interaction financial information such as business of consultation and known products and the like as a third key element of intelligent product recommendation. Each virtual digital person access operation of the user triggers the acquisition module to update data.
The databases include an enterprise internal database, an internet and partner database, and a meta-universe database.
And the intelligent recommendation information acquisition module is used for uploading acquired data to the meta space database.
Referring to fig. 12, after the target client logs into the meta-universe virtual business hall, the data processing module performs the following operations:
a) The method comprises the steps of acquiring data contents such as a target customer mobile phone number, a name, a label and the like acquired by a meta universe database, matching the data contents with an enterprise internal database, acquiring financial information of a customer such as in-line asset scale (including information of loan deposit funds in a customer line, information of processed financial services, consumption conditions, bank account funds, and the like) and customer group label definition and the like, analyzing asset grading, financial requirements, channel preferences, transaction preferences, product preferences, investment preferences and the like of the customer by deep mining and data analysis based on the acquired in-line data and by means of customer personal information and basic label information, and perfecting the customer financial digital label as a fourth key element of intelligent product recommendation. Wherein the asset rating is high, medium, low; the channel preference comprises off-line channels (off-line counter, off-self-service terminal, off-manual handling), off-line channels (off-line channels such as off-line mobile banking, off-line WeChat applet, etc.); investment preferences include financial products, annual regular deposits, long term regular deposits, demand deposits, and the like.
b) And (3) extracting a customer non-financial digital label based on the acquired customer group label definition and customer personal information, matching with the Internet and other data asset information outside the line by utilizing a big data analysis technology, wherein the customer non-financial digital label comprises contents such as customer work, relation demands, life demands and the like, and perfecting the customer non-financial digital label and portrait to be used as a fifth key element of intelligent product recommendation. The living demands include buying a car, buying a house, renting a house, etc.
The user preference analysis module extracts access point position and click point position data and language interaction data stored in the metauniverse database from the metauniverse database on time, namely, a first key element (client basic information and selection label), a second key element (client metauniverse point position access data), a third key element (client metauniverse interaction text data), a fourth key element (client financial digital label) and a fifth key element (client non-financial digital label) are used as inputs of a marketing sensitive rule model together, and a strategy in the model is judged and predicted through a certain service recommendation strategy and formulated recommendation logic and recommendation rules. The service recommendation strategy and recommendation rule can be obtained by combining manual experience and adopting machine learning, federal learning and other technical training.
The user preference analysis module outputs the introduction and handling flow prompt of the proper business products and services to the user as the final recommendation result, and intelligent recommendation of the financial products facing the target client in the meta-universe scene is realized.
As can be seen from the above, the present application can achieve at least the following technical effects:
1) Compared with the traditional single recommendation mode of only tag positioning and content matching, the method is more accurate in product marketing and intelligent recommendation.
2) The defect that the online network points of the meta-universe virtual business hall can display mass products for all users but cannot accurately recommend marketing is overcome.
3) The label, the virtual business hall track information, the language interaction information, the in-line database and the out-of-line database are used as the input of the marketing recommendation model, the logic rule is more comprehensive and complex, and effective recommendation can be made for new clients and inactive clients.
4) The method is exploration of the metauniverse scene, expansion and attempt of the metauniverse service ecology, expands the financial service channel, can realize diversification of scene contents, and enables product marketing service and customer service.
In order to effectively improve marketing efficiency and accuracy of financial products from a hardware level, the application provides an embodiment of an electronic device for implementing all or part of contents in an intelligent recommendation method of financial products, 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 communication with each other through the bus; the communication interface is used for realizing information transmission between the intelligent financial product recommending device and related equipment such as a core service system, a user terminal, a related database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may refer to an embodiment of the intelligent recommendation method for financial products in the embodiment and an embodiment of the intelligent recommendation device for financial products, and the contents thereof are incorporated herein, and are not repeated here.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical application, part of the intelligent recommendation method for the financial products can be executed on the side of the electronic equipment as described in the above description, or all operations can be completed in the client equipment. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Fig. 13 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. 13, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 13 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the financial product intelligent recommendation method functionality may be integrated into the central processor 9100.
The central processor 9100 may be configured to perform the following control:
step S101: and collecting label information, track information and interaction information generated by a user in the virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation.
Step S102: and carrying out data matching according to the attribute content of the basic tag and a setting database, determining the user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation.
Step S103: inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user.
As can be seen from the above description, in the electronic device provided by the embodiment of the present application, tag information, track information and interaction information generated by a user in a virtual business hall are collected, and the tag information, the track information and the interaction information are used as key elements for intelligent product recommendation; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
In another embodiment, the intelligent financial product recommendation device may be configured separately from the central processor 9100, for example, the intelligent financial product recommendation device may be configured as a chip connected to the central processor 9100, and the intelligent financial product recommendation method function is implemented under the control of the central processor.
As shown in fig. 13, 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 need not include all of the components shown in fig. 13; in addition, the electronic device 9600 may further include components not shown in fig. 13, and reference may be made to the related art.
As shown in fig. 13, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may 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 about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and 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. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The 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 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing 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 of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
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, etc., 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 to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiment of the present application further provides a computer readable storage medium capable of implementing all the steps in the intelligent recommendation method for financial products in which the execution subject is a server or a client in the above embodiment, the computer readable storage medium storing a computer program thereon, the computer program implementing all the steps in the intelligent recommendation method for financial products in which the execution subject is a server or a client in the above embodiment when executed by a processor, for example, the processor implementing the following steps when executing the computer program:
step S101: and collecting label information, track information and interaction information generated by a user in the virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation.
Step S102: and carrying out data matching according to the attribute content of the basic tag and a setting database, determining the user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation.
Step S103: inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user.
As can be seen from the above description, the computer readable storage medium provided by the embodiments of the present application collects tag information, track information and interaction information generated by a user in a virtual business hall, and uses the tag information, track information and interaction information as key elements for intelligent product recommendation; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
The embodiment of the present application further provides a computer program product capable of implementing all the steps in the intelligent recommendation method for financial products in which the execution subject is a server or a client, and the computer program/instructions implement the steps of the intelligent recommendation method for financial products when executed by a processor, for example, the computer program/instructions implement the steps of:
Step S101: and collecting label information, track information and interaction information generated by a user in the virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation.
Step S102: and carrying out data matching according to the attribute content of the basic tag and a setting database, determining the user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation.
Step S103: inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user.
As can be seen from the above description, the computer program product provided by the embodiment of the present application collects the label information, the track information and the interaction information generated by the user in the virtual business hall, and uses the label information, the track information and the interaction information as key elements of intelligent product recommendation; according to the attribute content of the basic tag, carrying out data matching with a setting database, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation; and inputting all key elements of intelligent recommendation of the product into a preset marketing sensitivity rule model, determining financial products corresponding to the user and recommending the financial products to the user, so that the marketing efficiency and accuracy of the financial products can be effectively improved.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention 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 (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (19)

1. An intelligent recommendation method for financial products, which is characterized by comprising the following steps:
acquiring label information, track information and interaction information generated by a user in a virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation;
according to the basic tag attribute content and the setting database, carrying out data matching, determining a user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation;
inputting all key elements of intelligent recommendation of the product into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending the financial products to the user.
2. The intelligent recommendation method for financial products according to claim 1, wherein the collecting label information, track information and interaction information generated by a user in a virtual business hall and using the label information, track information and interaction information as key elements of intelligent recommendation for the products comprises:
and acquiring basic tag attribute contents selected by a user at an entrance of the virtual business hall, and determining the basic tag attribute contents as a first key element of intelligent product recommendation.
3. The intelligent recommendation method for financial products according to claim 2, wherein the collecting label information, track information and interaction information generated by a user in a virtual business hall and using the label information, track information and interaction information as key elements of intelligent recommendation for the products comprises:
and acquiring access point position data and click point position data of the user in the operation process of the virtual business hall, and taking the access point position data and the click point position data as second key elements of intelligent product recommendation.
4. The intelligent recommendation method for financial products according to claim 3, wherein the collecting tag information, track information and interaction information generated by a user in a virtual business hall and using the tag information, track information and interaction information as key elements of intelligent recommendation for the products comprises:
and acquiring text content of the user in the interaction process of the virtual digital person, acquiring key financial information in the text content, and taking the key financial information as a third key element of intelligent product recommendation.
5. The intelligent recommendation method for financial products according to claim 4, further comprising:
And carrying out information preprocessing operation on the text content, wherein the information preprocessing operation comprises filtering interaction information irrelevant to financial requirements.
6. The intelligent recommendation method for financial products according to claim 5, wherein the data matching is performed with a setting database according to the attribute contents of the basic tag, the user representation of the user is determined, and the user representation is used as another key element of intelligent recommendation for the products, and the method comprises the steps of:
and carrying out data matching according to the attribute content of the basic tag and a set user asset database, determining the financial user portrait of the user, and taking the financial user portrait as a fourth key element of intelligent product recommendation.
7. The intelligent recommendation method for financial products according to claim 6, wherein the data matching is performed with a setting database according to the attribute contents of the basic tag, the user representation of the user is determined, and the user representation is used as another key element of intelligent recommendation for the products, and the method comprises the steps of:
and carrying out data matching according to the attribute content of the basic tag and a set user social relation database, determining a non-financial user portrait of the user, and taking the non-financial user portrait as a fifth key element of intelligent product recommendation.
8. The intelligent recommendation method for financial products according to claim 7, wherein inputting all key elements of the intelligent recommendation for the products into a preset marketing-sensitive rule model, determining financial products corresponding to the user and recommending to the user comprises:
inputting the first key element, the second key element, the third key element, the fourth key element and the fifth key element into a preset marketing sensitivity rule model;
and determining financial products corresponding to the user through the business recommendation strategy and recommendation rules of the marketing sensitive rule model and recommending the financial products to the user.
9. An intelligent financial product recommendation device, which is characterized by comprising:
the information acquisition module is used for acquiring label information, track information and interaction information generated by a user in the virtual business hall, and taking the label information, the track information and the interaction information as key elements of intelligent product recommendation;
the data matching module is used for carrying out data matching according to the attribute content of the basic tag and the setting database, determining the user portrait of the user, and taking the user portrait as another key element of intelligent product recommendation;
And the product recommending module is used for inputting all key elements of intelligent product recommending into a preset marketing sensitive rule model, determining financial products corresponding to the user and recommending to the user.
10. The intelligent recommendation apparatus for financial products according to claim 9, wherein the information collection module comprises:
and the first key element determining unit is used for acquiring basic tag attribute contents selected by a user at the entrance of the virtual business hall and determining the basic tag attribute contents as first key elements of intelligent product recommendation.
11. The intelligent recommendation apparatus for financial products of claim 9, wherein the information acquisition module further comprises:
the second key element determining unit is used for collecting the access point position data and the click point position data of the user in the operation process of the virtual business hall, and taking the access point position data and the click point position data as second key elements for intelligent product recommendation.
12. The intelligent recommendation apparatus for financial products of claim 9, wherein the information acquisition module further comprises:
and the third key element determining unit is used for acquiring text contents of the user in the interaction process of the virtual digital person, acquiring key financial information in the text contents and taking the key financial information as a third key element for intelligent product recommendation.
13. The intelligent recommendation apparatus for financial products of claim 12, wherein the information acquisition module further comprises:
and the information preprocessing unit is used for carrying out information preprocessing operation on the text content, wherein the information preprocessing operation comprises filtering interaction information irrelevant to financial requirements.
14. The intelligent recommendation apparatus for financial products of claim 9, wherein the data matching module comprises:
and the fourth key element determining unit is used for performing data matching with a set user asset database according to the attribute content of the basic tag, determining the financial user portrait of the user, and taking the financial user portrait as a fourth key element for intelligent product recommendation.
15. The intelligent recommendation apparatus for financial products of claim 9, wherein the data matching module further comprises:
and the fifth key element determining unit is used for performing data matching with a set user social relation database according to the attribute content of the basic tag, determining the non-financial user portrait of the user, and taking the non-financial user portrait as a fifth key element for intelligent product recommendation.
16. The intelligent recommendation apparatus for financial products according to claim 9, wherein the product recommendation module comprises:
the model input unit is used for inputting the first key element, the second key element, the third key element, the fourth key element and the fifth key element into a preset marketing sensitivity rule model;
and the model recommending unit is used for determining the financial products corresponding to the user through the business recommending strategy and recommending rules of the marketing sensitive rule model and recommending the financial products to the user.
17. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the intelligent recommendation method for financial products of any one of claims 1 to 8 when the program is executed by the processor.
18. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the intelligent recommendation method for a financial product according to any of claims 1 to 8.
19. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the intelligent recommendation method for financial products of any one of claims 1 to 8.
CN202310545227.3A 2023-05-15 2023-05-15 Intelligent recommendation method and device for financial products Pending CN116664227A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117808562A (en) * 2024-02-29 2024-04-02 南京特沃斯高科技有限公司 Network marketing method and platform based on metaspace

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117808562A (en) * 2024-02-29 2024-04-02 南京特沃斯高科技有限公司 Network marketing method and platform based on metaspace
CN117808562B (en) * 2024-02-29 2024-05-10 南京特沃斯高科技有限公司 Network marketing method and platform based on metaspace

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