CN115049456A - Recommendation method and device for financial product combination and electronic equipment - Google Patents
Recommendation method and device for financial product combination and electronic equipment Download PDFInfo
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Abstract
The invention discloses a recommendation method and device for financial product combinations and electronic equipment, and relates to the field of artificial intelligence, wherein the recommendation method comprises the following steps: acquiring asset information of a target user, pushing the asset information to a data center, and then establishing a user behavior model by adopting a preset Hall three-dimensional structure, wherein the user behavior model comprises the following steps: the first sub-model is used for calculating the investment scale stability of a target user, the second sub-model is used for determining a first financial product set to be pushed to the target user, the third sub-model is used for determining a second financial product set to be pushed to the target user, a target financial product combination is determined from the first financial product set and/or the second financial product set based on the investment scale stability, and the target financial product combination is recommended to the target user. The method and the device solve the technical problem that the user experience is reduced due to the fact that reasonable financial product combinations cannot be recommended to the user in the related technology.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a recommendation method and device for a financial product combination and electronic equipment.
Background
In the related art, financial institutions classify financial product services into money type financial products, bond type financial products, and the like, and a user can individually select financial products to be purchased only through various interface option buttons (e.g., "choose you" and "all financial products"). However, in the current 'carefully-chosen financial products for you' push, only two to three financial products are provided, the selectable content is less, the user cannot perform personalized push, and the user selects financial products by pushing 'all financial products', and because the purchasing proportion of various financial products does not have a reasonable model for the user to refer to, the user cannot determine whether the purchased financial products are required by the financial management scheme of the user, and cannot reasonably distribute the purchased financial products, so that the purchasing blindness is high, and the user experience is reduced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a recommendation method and device for a financial product combination and electronic equipment, and aims to at least solve the technical problem that the user experience is reduced because a reasonable financial product combination cannot be recommended for a user in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a method for recommending a financial product portfolio, including: acquiring asset information of a target user, and pushing the asset information to a data center; based on the data center, a preset Hall three-dimensional structure is adopted to establish a user behavior model, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of the target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user; determining a target financial product portfolio from the first set of financial products and/or the second set of financial products based on the investment scale stability; and recommending the target financial product combination to the target user.
Optionally, the step of obtaining asset information of the target user includes: generating an information acquisition instruction under the condition that the target user triggers product purchase operation and/or product interface browsing operation; and acquiring the asset information of the target user based on the information acquisition instruction.
Optionally, after the asset information is pushed to the data center, the method further includes: extracting keywords of the asset information based on a preset data lake technology; classifying the asset information based on the keywords to obtain a classification result, wherein the data type in the classification result at least comprises: transaction data, first type financial product data, second type financial product data.
Optionally, the step of establishing a user behavior model by using a preset hall three-dimensional structure based on the data center comprises: acquiring the transaction data, the first type of financial product data and the second type of financial product data based on the data center; representing the investment scale stability as a time dimension standard of the preset Hall three-dimensional structure; representing a first financial product set in a first preset time period as a logic dimension standard of the preset Hall three-dimensional structure; characterizing a second set of financial products in a second preset time period as a knowledge dimension standard of the preset Hall three-dimensional structure; and establishing the user behavior model by adopting the transaction data, the first type of financial product data and the second type of financial product data based on the time dimension standard, the logic dimension standard and the knowledge dimension standard.
Optionally, the step of calculating the investment scale stability of the target user includes: setting a preset period duration; analyzing the transaction data based on the preset period duration to obtain a transaction data value in each period; and calculating the stability of the investment scale by adopting a preset logistic regression strategy based on the transaction data value.
Optionally, the step of determining a first set of financial products to be pushed to the target user comprises: analyzing the first type of financial product data to obtain first product types of financial products purchased by the target user within the first preset time period and the purchase quantity of each first product type; calculating the preference degree of the target user for each first product category based on the purchase quantity of each first product category; and determining the target product type indicated by the preference degree larger than a first preset threshold value to obtain the first financial product set.
Optionally, the step of determining a second set of financial products to be pushed to the target user comprises: analyzing the second type financial product data to obtain a first type set of financial products searched by the target user in the second preset time period, a second product type of purchased financial products and the purchase quantity of each second product type; calculating the preference degree of the target user for each second product category based on the purchase quantity of each second product category; determining the target product type indicated by the preference degree larger than a second preset threshold value to obtain a second type set; obtaining the second set of financial products based on the first set of categories and the second set of categories.
Optionally, the step of determining a target financial product portfolio from the first set of financial products, and/or the second set of financial products, based on the investment scale stability, comprises: determining the target financial product portfolio from the first set of financial products if the investment scale stability is a first stability; determining the target financial product portfolio from the second set of financial products if the investment scale stability is a second stability, wherein the second stability is greater than the first stability; determining the target financial product portfolio from the first set of financial products and the second set of financial products if the investment scale stability is a third stability, the third stability being greater than the first stability and less than the second stability.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for recommending a financial product portfolio, including: the system comprises an acquisition unit, a data center station and a data processing unit, wherein the acquisition unit is used for acquiring asset information of a target user and pushing the asset information to the data center station; the establishing unit is used for establishing a user behavior model by adopting a preset Hall three-dimensional structure based on the data center, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of the target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user; a determining unit for determining a target financial product combination from the first financial product set and/or the second financial product set based on the investment scale stability; and the recommending unit is used for recommending the target financial product combination to the target user.
Optionally, the obtaining unit includes: the first generation module is used for generating an information acquisition instruction under the condition that the target user triggers product purchase operation and/or product interface browsing operation; and the first acquisition module is used for acquiring the asset information of the target user based on the information acquisition instruction.
Optionally, the recommendation device further includes: the first extraction module is used for extracting keywords of the asset information based on a preset data lake technology after the asset information is pushed to a data center; a first classification module, configured to classify the asset information based on the keyword to obtain a classification result, where a data type in the classification result at least includes: transaction data, first type financial product data, second type financial product data.
Optionally, the establishing unit includes: the second acquisition module is used for acquiring the transaction data, the first type of financial product data and the second type of financial product data based on the data center; the first characterization module is used for characterizing the investment scale stability as a time dimension standard of the preset Hall three-dimensional structure; the second characterization module is used for characterizing the first financial product set in a first preset time period as a logic dimension standard of the preset Hall three-dimensional structure; the third characterization module is used for characterizing a second financial product set in a second preset time period as the knowledge dimension standard of the preset Hall three-dimensional structure; a first establishing module, configured to establish the user behavior model by using the transaction data, the first type of financial product data, and the second type of financial product data based on the time dimension standard, the logic dimension standard, and the knowledge dimension standard.
Optionally, the first calculation module comprises: the first setting submodule is used for setting a preset period duration; the first analysis submodule is used for analyzing the transaction data based on the preset period duration to obtain a transaction data value in each period; and the first calculation submodule is used for calculating the investment scale stability by adopting a preset logistic regression strategy based on the transaction data value.
Optionally, the first determining module includes: the second analysis submodule is used for analyzing the first type of financial product data to obtain first product types of financial products purchased by the target user in the first preset time period and the purchase quantity of each first product type; a second calculating sub-module, configured to calculate, based on the purchase amount of each of the first product categories, a preference degree of the target user for each of the first product categories; and the first determining submodule is used for determining the target product type indicated by the preference degree greater than a first preset threshold value to obtain the first financial product set.
Optionally, the second determining module includes: a third analysis sub-module, configured to analyze the second-class financial product data to obtain a first class set of financial products, a second product class of purchased financial products, and a purchase quantity of each second product class, which are searched by the target user within the second preset time period; a third calculation submodule, configured to calculate a preference degree of the target user for each of the second product categories based on a purchase amount of each of the second product categories; the second determining submodule is used for determining the target product type indicated by the preference degree larger than a second preset threshold value to obtain a second type set; a first output submodule, configured to obtain the second set of financial products based on the first set of categories and the second set of categories.
Optionally, the determining unit includes: a third determining module for determining the target financial product portfolio from the first set of financial products if the investment scale stability is a first stability; a fourth determining module for determining the target financial product portfolio from the second set of financial products if the investment scale stability is a second stability, wherein the second stability is greater than the first stability; a fifth determining module for determining the target financial product portfolio from the first set of financial products and the second set of financial products if the investment scale stability is a third stability, the third stability being greater than the first stability and less than the second stability.
According to another aspect of embodiments of the present invention, there is also provided an electronic device, including one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method for recommending a financial product portfolio described above.
In the disclosure, asset information of a target user is acquired, the asset information is pushed to a data center, a user behavior model is established by adopting a preset hall three-dimensional structure based on the data center, and the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, the third submodel is used for determining a second financial product set to be pushed to the target user, a target financial product combination is determined from the first financial product set and/or the second financial product set based on the investment scale stability, and the target financial product combination is recommended to the target user. In the application, a user behavior model can be established by adopting a preset Hall three-dimensional structure based on asset information on a data center, so that the investment scale stability of a user, a first financial product set and a second financial product set are obtained, and a reasonable target financial product combination can be selected from the first financial product set and the second financial product set according to the investment scale stability and recommended to the user, so that the personalized delivery of the financial product combination is realized, the purchasing time of the user is saved, the accuracy of financial product selection is improved, the user experience is improved, and the technical problem that the user experience is reduced due to the fact that the reasonable financial product combination cannot be recommended to the user in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for recommending alternative combinations of financial products, according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of an alternative fund recommendation setup in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of an alternative pushing of a smart fund combination according to an embodiment of the invention;
FIG. 4 is a schematic diagram of an alternative financial product portfolio recommendation device, according to an embodiment of the present invention;
fig. 5 is a block diagram of a hardware configuration of an electronic device (or mobile device) for a recommendation method of a financial product portfolio according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
To facilitate understanding of the invention by those skilled in the art, some terms or nouns referred to in the embodiments of the invention are explained below:
the data center station: the system can acquire, process, store and calculate massive, multi-source and various data, simultaneously unify standards and calibers, and store the data in a standard form after the data are unified to form a big data asset layer so as to meet the requirements of foreground data analysis and application. The data center station emphasizes application, is closer to business, emphasizes the capability of serving a foreground, can realize the precipitation and reuse of logic, algorithm, label, model and data asset, can quickly adapt to the requirements of business and application development, and has the characteristics of traceability and higher accuracy.
Quantifying financing risk: the possible result range of the financing project is estimated through risks and the interaction of the risks. Common risk quantification methods include expectation methods, statistical sum methods, simulation methods, decision trees, and the like.
User portrait: namely, the user role is an effective tool for drawing a target user and contacting user appeal and design direction, and user behaviors, attributes and expected data conversion can be combined.
A Hall three-dimensional structure: the three-dimensional space structure model is composed of a time dimension, a logic dimension and a knowledge dimension.
And (3) a logistic regression algorithm: also known as regression analysis, is a generalized linear regression analysis model commonly used in data mining.
The combined pushing of the intelligent financial products: and personalized pushing is carried out according to the information such as financial objects, client assets, transaction data and the like of the clients.
It should be noted that the method and apparatus for recommending a financial product portfolio in the present disclosure may be used in the artificial intelligence field for recommending a financial product portfolio, or in any field other than the artificial intelligence field for recommending a financial product portfolio, and the application fields of the method and apparatus for recommending a financial product portfolio in the present disclosure are not limited.
It should be noted that relevant information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for presentation, analyzed data, etc.) referred to in the present disclosure are information and data that are authorized by the user or sufficiently authorized by various parties. For example, an interface is provided between the system and the relevant user or organization, before obtaining the relevant information, an obtaining request needs to be sent to the user or organization through the interface, and after receiving the consent information fed back by the user or organization, the relevant information is obtained.
The embodiments of the invention described below may be applied to systems/applications/devices that recommend combinations of financial products. The invention can utilize the data center to provide personalized financial product combination push for users (including enterprise users), when the users buy financial products, the users can evaluate according to the directions of user behavior models (including enterprise operation modes), user image preference, financial management risks and the like, and push a plurality of financial product combinations meeting requirements for the users, the financial product combinations can comprise financial products of various types, and suitable financial product purchase proportion and purchase period are provided for the users to refer, thereby not only saving the purchasing time of the users, but also improving the accuracy of financial product selection, and further improving the user experience.
The present invention will be described in detail with reference to examples.
Example one
In accordance with an embodiment of the present invention, there is provided a method for recommending a financial product portfolio, where the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and where a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
FIG. 1 is a flow chart of a method for recommending alternative combinations of financial products, according to an embodiment of the present invention, as shown in FIG. 1, the method comprising the steps of:
and step S101, acquiring the asset information of the target user and pushing the asset information to a data center.
Step S102, based on the data center, a user behavior model is established by adopting a preset Hall three-dimensional structure, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user.
And step S103, determining a target financial product combination from the first financial product set and/or the second financial product set based on the investment scale stability.
And step S104, recommending the target financial product combination to the target user.
Through the steps, the asset information of the target user can be obtained, the asset information is pushed to the data center, based on the data center, a user behavior model is established by adopting a preset Hall three-dimensional structure, and the user behavior model comprises the following steps: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, the third submodel is used for determining a second financial product set to be pushed to the target user, a target financial product combination is determined from the first financial product set and/or the second financial product set based on the investment scale stability, and the target financial product combination is recommended to the target user. In the embodiment of the invention, a user behavior model can be established by adopting a preset Hall three-dimensional structure based on asset information on a data center, so that the investment scale stability of a user, a first financial product set and a second financial product set are obtained, and a reasonable target financial product combination can be selected from the first financial product set and the second financial product set according to the investment scale stability and recommended to the user, so that the personalized delivery of the financial product combination is realized, the purchasing time of the user is saved, the accuracy of financial product selection is improved, the user experience is improved, and the technical problem that the user experience is reduced because the reasonable financial product combination cannot be recommended to the user in the related technology is solved.
The following will explain the embodiments of the present invention in detail with reference to the above steps.
And step S101, acquiring the asset information of the target user and pushing the asset information to a data center.
Optionally, the step of obtaining asset information of the target user includes: generating an information acquisition instruction under the condition that a target user triggers a product purchasing operation and/or a product interface browsing operation; and acquiring the asset information of the target user based on the information acquisition instruction.
In the embodiment of the present invention, when a user purchases a financial product and/or views a flow of the financial product, an information acquisition operation may be entered (that is, an information acquisition instruction is generated when a target user triggers a product purchase operation and/or a product interface browsing operation), asset information of the user is acquired (that is, asset information of the target user is acquired based on the information acquisition instruction), and then the acquired asset information is collectively pushed to a data center.
Optionally, after the asset information is pushed to the data console, the method further includes: extracting keywords of asset information based on a preset data lake technology; based on the keywords, the asset information is classified to obtain a classification result, wherein the data types in the classification result at least comprise: transaction data, first type financial product data, second type financial product data.
In the embodiment of the invention, the keywords of the asset information can be extracted through a preset data lake technology, then the asset information can be labeled according to the keywords, and statistical classification is carried out to obtain the classification result (the data types in the classification result at least comprise transaction data, first-class financial product data, second-class financial product data and the like).
FIG. 2 is a schematic diagram of an alternative fund recommendation setting mode according to an embodiment of the present invention, and as shown in FIG. 2, the data sources of the client include: the method comprises the following steps that a plurality of sources such as customer information, product information, buried point data, transaction monitoring logs and transaction data can be used for setting recommendation elements, wherein the recommendation elements comprise that the purchase process enters information collection and my fund-investor information collection (namely a user purchases a financial product and the user checks the financial product) is used as a trigger condition for data collection: customers (customer groups) (comprising user behavior data, user preference data and user transaction data), time (comprising period, time, timing and real-time), labels (comprising fact labels, model labels and forecast labels), strategies (comprising frequency strategies, blacklist filtering and wind control filtering) and the like, and the set recommendation types can be: statistical analysis recommendation (including data lake and big data analysis), intelligent recommendation (including knowledge graph and recommendation model), and the like, and the Hall three-dimensional structure adopted by the embodiment belongs to the statistical analysis recommendation type.
Step S102, based on the data center, a user behavior model is established by adopting a preset Hall three-dimensional structure, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user.
Optionally, the step of establishing a user behavior model by using a preset hall three-dimensional structure based on the data center comprises: acquiring transaction data, first type financial product data and second type financial product data based on a data center; representing the investment scale stability as a time dimension standard of a preset Hall three-dimensional structure; representing a first financial product set in a first preset time period as a logic dimension standard of a preset Hall three-dimensional structure; representing a second financial product set in a second preset time period as a knowledge dimension standard of a preset Hall three-dimensional structure; and establishing a user behavior model by adopting transaction data, first-class financial product data and second-class financial product data based on the time dimension standard, the logic dimension standard and the knowledge dimension standard.
In the embodiment of the invention, transaction data (including transaction amount and the like in each period), first-class financial product data (including financial product types purchased by a user in a long time (such as within one year), financial product quantity purchased in different types and the like) and second-class financial product data (including financial product types searched and purchased by the user in a short time (such as within one month), financial product quantity purchased and financial product quantity purchased in different types and the like) can be obtained based on the data center, and in addition, as the Hall three-dimensional structure is a three-dimensional space structure model consisting of a time dimension, a logic dimension and a knowledge dimension, the embodiment can take the "investment scale stability of the user" as the "time dimension" standard, the "preference data of the user in a long period" as the "logic dimension" standard, and the "sensitivity of the user in a short time to a certain type of products" as the "knowledge dimension" standard (namely, the investment scale stability is characterized as preset three Hall three The method comprises the steps of establishing a user behavior model by using transaction data, first-class financial product data and second-class financial product data based on time dimension standards, logic dimension standards and knowledge dimension standards, wherein the user behavior model comprises the following steps: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set (namely a user preference financial product set in a long period) to be pushed to the target user, and the third submodel is used for determining a second financial product set (namely a sensitivity financial product set of a user in a short period) to be pushed to the target user.
Optionally, the step of calculating the investment scale stability of the target user includes: setting a preset period duration; analyzing the transaction data based on the preset period duration to obtain a transaction data value in each period; and calculating the stability of the investment scale by adopting a preset logistic regression strategy based on the transaction data value.
In the embodiment of the present invention, a preset period duration T may be set (for example, if three months are taken as a period, T1 is 1-3 months, T2 is 2-4 months, and the like), and then, through the transaction data, the transaction data value X in each period may be obtained through analysis, for example, the transaction data value of the user in the first period T is X 1 The transaction data value in the second period T is X 2 ,., the transaction data value in the nth period T is X n Then, a preset logistic regression strategy (e.g., logistic regression algorithm) may be employed to calculate the investment scale stability P by the following equation (1):
wherein e is a preset base constant, and z is a multivariate variable linear function with X as a variable:
n denotes n periods.
Finally, the "time dimension" criterion in the hall three-dimensional structure can be found: the value of "stability of investment scale of user".
Optionally, the step of determining a first set of financial products to be pushed to the target user includes: analyzing the first type of financial product data to obtain first product types of financial products purchased by a target user in a first preset time period and the purchase quantity of each first product type; calculating the preference degree of the target user to each first product type based on the purchase quantity of each first product type; and determining the target product type indicated by the preference degree larger than a first preset threshold value to obtain a first financial product set.
In the embodiment of the present invention, the financial product types X purchased by the user for a long time may be analyzed and obtained through the first type of financial product data, the number a of financial products purchased in different types (i.e., the first product type of the financial product purchased by the target user in the first preset time period and the purchase number of each first product type) is obtained, and then, the user preference for the financial products in different types is calculated by using the following formula (2) (i.e., the preference of the target user for each first product type is calculated based on the purchase number of each first product type):
wherein, the types of the financial products are defined to be X types, a 1 To purchase quantities of type 1 financial products, a 2 A, to purchase quantities of type 2 financial products x To purchase quantities, Q, corresponding to category x financial products x Indicating the user's preference for different types of financial products.
The present embodiment may determine a preference greater than a first preset threshold (e.g.,
) And obtaining a first financial product set Q according to the indicated target product type, namely a 'logic dimension' standard in a Hall three-dimensional structure: "user preference data over a long period".
Optionally, the step of determining a second set of financial products to be pushed to the target user includes: analyzing the data of the second type of financial products to obtain a first type set of financial products searched by the target user in a second preset time period, second product types of the purchased financial products and the purchase quantity of each second product type; calculating the preference degree of the target user for each second product type based on the purchase quantity of each second product type; determining the target product type indicated by the preference degree larger than a second preset threshold value to obtain a second type set; a second set of financial products is derived based on the first set of categories and the second set of categories.
In the embodiment of the invention, the financial product category set R searched by the user in a short term can be analyzed and obtained through the second type financial product data 1 (i.e., the first kind of set of financial products searched by the target user during the second preset time period), the second product kind of financial products purchased by the target user during the second preset time period, and the purchase quantity of each second product kind, and then, the user-preferred financial product kind set R for a short period of time is calculated using the following formula (3) 2 ：
Wherein, the financial product types purchased in a short period are defined to be X types, a 1 To purchase quantities of type 1 financial products, a 2 A, to purchase quantities of type 2 financial products x To purchase quantities of corresponding type X financial products, r x Indicating the user's preference for different types of financial products.
The present embodiment may determine a preference greater than a second preset threshold (e.g.,
) The indicated target product category, resulting in a second set of categories (i.e., a set of user-preferred financial product categories R in the short term) 2 ) Then, based on the first kind set and the second kind set, obtainingThe second financial product set R is the "knowledge dimension" standard in the hall three-dimensional structure: the set of "sensitivity of users to a certain type of product in the short term" is: r ═ R 1 ∪R 2 。
And step S103, determining a target financial product combination from the first financial product set and/or the second financial product set based on the investment scale stability.
Optionally, the step of determining a target financial product portfolio from the first set of financial products, and/or the second set of financial products, based on the investment scale stability, comprises: determining a target financial product portfolio from the first set of financial products if the investment scale stability is a first stability; determining a target financial product portfolio from a second set of financial products where the investment scale stability is a second stability, wherein the second stability is greater than the first stability; in the case where the investment-scale stability is a third stability, the target financial product portfolio is determined from the first set of financial products and the second set of financial products, the third stability being greater than the first stability and less than the second stability.
In the embodiment of the present invention, based on the "stability of user investment scale", the user group with the "stability of user investment scale in long period" and the "sensitivity of the user to a certain type of product in short period" may be guided to perform the recommendation of financial product combinations to the target user, specifically, the user group with the "stability of user investment scale" in first stability (e.g., [0,0.6) may be set as the low stability user, the user group with the "stability of user investment scale" in third stability (e.g., [0.6,0.8) may be set as the medium stability user, and the user group with the "stability of user investment scale" in second stability (e.g., [0.8,1] may be set as the high stability user.
For a user with low stability, the financial product combination recommendation of the "stock maintenance system" can be performed, that is, the financial product combination in the "user preference data in a long period" set Q is pushed by the user (that is, under the condition that the stability of the investment scale is the first stability, the target financial product combination is determined from the first financial product set), so as to achieve the purpose of stably maintaining the target user.
For a user with high stability, the financial product combination recommendation of innovation and push can be carried out, namely, the user pushes the financial product combination in the financial product set R of the sensitivity of the user to certain products in a short period (namely, under the condition that the stability of investment scale is the second stability, the target financial product combination is determined from the second financial product set), so as to achieve the purposes of innovation and recommendation and increment maintenance of the customer base.
For the financial product combination recommendation which is mainly based on innovation pushing and assisted by a user with medium stability and assisted by an inventory maintenance system, the financial product combination recommendation is that the user pushes a financial product combination of a user preference data set Q in a long period and a user sensitivity to a certain product set R in a short period (namely, under the condition that the investment scale stability is the third stability, a target financial product combination is determined from a first financial product set and a second financial product set), and the incremental service can be performed on the premise that the user stably purchases the financial product combination recommendation is ensured.
And step S104, recommending the target financial product combination to the target user.
In the embodiment of the invention, the problems that the selectable content is too limited, the investment content ratio does not always meet the self development requirement and the autonomous selection is always blindness when the user purchases the financial products in the past can be solved, the financial product combination meeting the financing target of the user can be accurately provided for the user through 'personalized financial product combination pushing', so that the user can reasonably distribute resources, a good and referable guidance scheme is provided for the user investment, the financing income of the user can be improved, the user experience is greatly improved, the user viscosity is higher, and the development of financial institutions is facilitated.
The following detailed description is to be read in connection with alternative embodiments. In the present embodiment, a fund combination is taken as an example, and an application scenario may be fund combination pushing.
Fig. 3 is a schematic diagram of an alternative smart fund combination pushing according to an embodiment of the present invention, and as shown in fig. 3, the smart fund combination pushing includes: the channel client (such as a mobile phone client, a computer client, etc.), a user interface, a channel server (such as a mobile phone server, a computer server, etc.), and a data center, specifically, the pushing process is as follows:
the method comprises the steps of setting an individualized fund combination purchasing interface on a user interface, judging whether individualized evaluation is performed or not after a client enters the individualized fund combination purchasing interface, if so, directly providing fund combinations to the client, otherwise, acquiring client asset information through a channel client, evaluating the financial risk undertaking degree of the client, storing the information through a channel server, transmitting centralized data to a data center, processing, storing, calculating and unifying the standard through the data center to form a data asset layer, providing data assets to a channel server, screening out fund combinations meeting requirements according to data asset layer conclusions by the channel server, and then providing fund combinations to the client through the user interface.
In this embodiment, if the customer wants to change the fund purchase preference information, the data needs to be retrieved and personalized push is performed.
In the embodiment of the invention, the acquired data in multiple aspects, such as asset information, user portrait, financial risk quantification and the like, can be calculated and processed through the data center, and the financial product combination recommended to the user is finally determined. For example, whether a certain enterprise user needs stable financing in a medium-long term, whether fast flow turnover of funds is needed, whether bearing capacity is provided for short-term asset value reduction and the like is determined, the selection ratio of past investment financial products is compared, and the enterprise user with higher similarity purchases financial product contents to carry out comprehensive personalized recommendation.
Example two
The recommendation device for a financial product combination provided in this embodiment includes a plurality of implementation units, and each implementation unit corresponds to a respective implementation step in the first embodiment.
Fig. 4 is a schematic diagram of a recommendation apparatus for an alternative financial product portfolio according to an embodiment of the present invention, as shown in fig. 4, the recommendation apparatus may include: an obtaining unit 40, a creating unit 41, a determining unit 42, a recommending unit 43, wherein,
the acquisition unit 40 is configured to acquire asset information of a target user and push the asset information to a data center;
the establishing unit 41 is configured to establish a user behavior model by using a preset hall three-dimensional structure based on a data center, where the user behavior model includes: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user;
a determination unit 42 for determining a target financial product portfolio from the first financial product portfolio and/or the second financial product portfolio based on the investment scale stability;
and a recommending unit 43 for recommending the target financial product combination to the target user.
The recommendation device can acquire the asset information of the target user and push the asset information to the data center station, and establishes a user behavior model by adopting a preset Hall three-dimensional structure based on the data center station, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, the third submodel is used for determining a second financial product set to be pushed to the target user, a target financial product combination is determined from the first financial product set and/or the second financial product set based on the investment scale stability, and the target financial product combination is recommended to the target user. In the embodiment of the invention, a user behavior model can be established by adopting a preset Hall three-dimensional structure based on asset information on a data center, so that the investment scale stability of a user, a first financial product set and a second financial product set are obtained, and a reasonable target financial product combination can be selected from the first financial product set and the second financial product set according to the investment scale stability and recommended to the user, so that the personalized delivery of the financial product combination is realized, the purchasing time of the user is saved, the accuracy of financial product selection is improved, the user experience is improved, and the technical problem that the user experience is reduced because the reasonable financial product combination cannot be recommended to the user in the related technology is solved.
Optionally, the obtaining unit includes: the first generation module is used for generating an information acquisition instruction under the condition that a target user triggers product purchase operation and/or product interface browsing operation; and the first acquisition module is used for acquiring the asset information of the target user based on the information acquisition instruction.
Optionally, the recommendation device further includes: the first extraction module is used for extracting keywords of the asset information based on a preset data lake technology after the asset information is pushed to the data center; the first classification module is used for classifying the asset information based on the keywords to obtain a classification result, wherein the data type in the classification result at least comprises: transaction data, first type financial product data, second type financial product data.
Optionally, the establishing unit includes: the second acquisition module is used for acquiring transaction data, first type financial product data and second type financial product data based on the data console; the first characterization module is used for characterizing the investment scale stability as a time dimension standard of a preset Hall three-dimensional structure; the second characterization module is used for characterizing the first financial product set in the first preset time period into a logic dimension standard of a preset Hall three-dimensional structure; the third characterization module is used for characterizing the second financial product set in a second preset time period into a knowledge dimension standard of a preset Hall three-dimensional structure; the first establishing module is used for establishing a user behavior model by adopting transaction data, first-class financial product data and second-class financial product data based on the time dimension standard, the logic dimension standard and the knowledge dimension standard.
Optionally, the first calculation module includes: the first setting submodule is used for setting a preset period duration; the first analysis submodule is used for analyzing the transaction data based on the preset period duration to obtain a transaction data value in each period; and the first calculation submodule is used for calculating the investment scale stability by adopting a preset logistic regression strategy based on the transaction data value.
Optionally, the first determining module includes: the second analysis submodule is used for analyzing the first type of financial product data to obtain first product types of financial products purchased by a target user in a first preset time period and the purchase quantity of each first product type; the second calculation submodule is used for calculating the preference degree of the target user for each first product type based on the purchase quantity of each first product type; and the first determining submodule is used for determining the target product type indicated by the preference degree greater than the first preset threshold value to obtain a first financial product set.
Optionally, the second determining module includes: the third analysis submodule is used for analyzing the data of the second-class financial products to obtain a first-class set of financial products searched by the target user in a second preset time period, second product types of the purchased financial products and the purchase quantity of each second product type; the third calculation submodule is used for calculating the preference degree of the target user to each second product type based on the purchase quantity of each second product type; the second determining submodule is used for determining the target product type indicated by the preference degree larger than a second preset threshold value to obtain a second type set; and the first output submodule is used for obtaining a second financial product set based on the first category set and the second category set.
Optionally, the determining unit includes: a third determining module for determining a target financial product portfolio from the first financial product portfolio under the condition that the investment scale stability is the first stability; a fourth determining module for determining a target financial product portfolio from the second set of financial products if the investment scale stability is a second stability, wherein the second stability is greater than the first stability; and a fifth determining module for determining the target financial product combination from the first and second financial product sets if the investment scale stability is a third stability, the third stability being greater than the first stability and less than the second stability.
The recommendation device may further include a processor and a memory, and the acquiring unit 40, the establishing unit 41, the determining unit 42, the recommending unit 43, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can set one or more than one, and the target financial product combination is recommended to the target user by adjusting the parameters of the kernel.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: the method comprises the steps of obtaining asset information of a target user, pushing the asset information to a data center, and establishing a user behavior model by adopting a preset Hall three-dimensional structure based on the data center, wherein the user behavior model comprises the following steps: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of a target user, the second submodel is used for determining a first financial product set to be pushed to the target user, the third submodel is used for determining a second financial product set to be pushed to the target user, a target financial product combination is determined from the first financial product set and/or the second financial product set based on the investment scale stability, and the target financial product combination is recommended to the target user.
According to another aspect of embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the above-described recommendation method for a combination of financial products.
Fig. 5 is a block diagram of a hardware configuration of an electronic device (or mobile device) for a recommendation method of a financial product portfolio according to an embodiment of the present invention. As shown in fig. 5, the electronic device may include one or more processors 502 (shown as 502a, 502b, … …, 502 n) (the processors 502 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and memory 504 for storing data. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a keyboard, a power supply, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 5 is only an illustration and is not intended to limit the structure of the electronic device. For example, the electronic device may also include more or fewer components than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for recommending a financial product portfolio, comprising:
acquiring asset information of a target user, and pushing the asset information to a data center;
based on the data center, a preset Hall three-dimensional structure is adopted to establish a user behavior model, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of the target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user;
determining a target financial product portfolio from the first set of financial products and/or the second set of financial products based on the investment scale stability;
and recommending the target financial product combination to the target user.
2. The recommendation method according to claim 1, wherein the step of obtaining asset information of the target user comprises:
generating an information acquisition instruction under the condition that the target user triggers product purchase operation and/or product interface browsing operation;
and acquiring the asset information of the target user based on the information acquisition instruction.
3. The recommendation method according to claim 1, further comprising, after pushing the asset information to a data middlebox:
extracting keywords of the asset information based on a preset data lake technology;
classifying the asset information based on the keywords to obtain a classification result, wherein the data type in the classification result at least comprises: transaction data, first type financial product data, second type financial product data.
4. The recommendation method according to claim 3, wherein the step of establishing a user behavior model based on the data center by adopting a preset Hall three-dimensional structure comprises:
acquiring the transaction data, the first type of financial product data and the second type of financial product data based on the data center;
representing the investment scale stability as a time dimension standard of the preset Hall three-dimensional structure;
representing a first financial product set in a first preset time period as a logic dimension standard of the preset Hall three-dimensional structure;
characterizing a second set of financial products in a second preset time period as a knowledge dimension standard of the preset Hall three-dimensional structure;
and establishing the user behavior model by adopting the transaction data, the first type of financial product data and the second type of financial product data based on the time dimension standard, the logic dimension standard and the knowledge dimension standard.
5. The recommendation method according to claim 4, wherein the step of calculating the investment scale stability of the target user comprises:
setting a preset period duration;
analyzing the transaction data based on the preset period duration to obtain a transaction data value in each period;
and calculating the stability of the investment scale by adopting a preset logistic regression strategy based on the transaction data value.
6. The recommendation method of claim 4, wherein the step of determining a first set of financial products to be pushed to the target user comprises:
analyzing the first type of financial product data to obtain first product types of financial products purchased by the target user within the first preset time period and the purchase quantity of each first product type;
calculating the preference degree of the target user for each first product category based on the purchase quantity of each first product category;
and determining the target product type indicated by the preference degree larger than a first preset threshold value to obtain the first financial product set.
7. The recommendation method of claim 4, wherein the step of determining a second set of financial products to be pushed to the target user comprises:
analyzing the second type financial product data to obtain a first type set of financial products searched by the target user in the second preset time period, a second product type of purchased financial products and the purchase quantity of each second product type;
calculating the preference degree of the target user for each second product category based on the purchase quantity of each second product category;
determining the target product type indicated by the preference degree larger than a second preset threshold value to obtain a second type set;
and obtaining the second financial product set based on the first category set and the second category set.
8. The recommendation method according to claim 1, wherein the step of determining a target financial product portfolio from the first set of financial products, and/or the second set of financial products, based on the investment scale stability comprises:
determining the target financial product portfolio from the first set of financial products if the investment scale stability is a first stability;
determining the target financial product portfolio from the second set of financial products if the investment scale stability is a second stability, wherein the second stability is greater than the first stability;
determining the target financial product portfolio from the first set of financial products and the second set of financial products if the investment scale stability is a third stability, the third stability being greater than the first stability and less than the second stability.
9. An apparatus for recommending a financial product portfolio, comprising:
the system comprises an acquisition unit, a data center station and a data processing unit, wherein the acquisition unit is used for acquiring asset information of a target user and pushing the asset information to the data center station;
the establishing unit is used for establishing a user behavior model by adopting a preset Hall three-dimensional structure based on the data center, wherein the user behavior model comprises: the system comprises a first submodel, a second submodel and a third submodel, wherein the first submodel is used for calculating the investment scale stability of the target user, the second submodel is used for determining a first financial product set to be pushed to the target user, and the third submodel is used for determining a second financial product set to be pushed to the target user;
a determining unit for determining a target financial product combination from the first financial product set and/or the second financial product set based on the investment scale stability;
and the recommending unit is used for recommending the target financial product combination to the target user.
10. An electronic device comprising one or more processors and memory storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method for recommending a combination of financial products of any of claims 1 through 8.
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| CN115239442B (en) * | 2022-09-22 | 2023-01-06 | 湖南快乐通宝小额贷款有限公司 | Method and system for popularizing internet financial products and storage medium |
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