CN112150293A - Product recommendation method and device based on user personal information - Google Patents
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Abstract
The disclosure provides a product recommendation method and device based on user personal information, which includes: the method comprises the steps of profiling a user, collecting personal information data of the user in the forms of a terminal questionnaire and the like, and constructing an evaluation information set; simulating future stock trends and expected income of users to be evaluated by using a geometric Brownian motion model, preliminarily processing an evaluation information set, and obtaining model parameters by using a moment estimation method; and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user by using a dynamic planning method, and sending the recommended financial products to the corresponding terminals. The method overcomes the defect that the investment proportion of each stock in the stock fund is fixed for different users, customizes the optimal investment proportion for the investors according to the self condition of the investors, provides corresponding investment satisfaction indexes, and has more flexibility and accuracy.
Description
Technical Field
The disclosure belongs to the technical field of computer information processing, and particularly relates to a product recommendation method and device based on user personal information.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The current recommendation of financial products is generally based on the preliminary information acquisition in the form of a questionnaire of a terminal, then the types of the users are judged after analysis, including conservative type, robust type or active type, and corresponding products are recommended according to the investment types of the users.
The elderly are a more specific class of users. The user is characterized in that: the risk aversion degree is high; the investment period is medium-short term; the risk of ending investment due to the fact that the investor is away from the world exists; the main economic income of the old is retirement funds, and the investment strategy of the old is influenced by the condition of the retirement funds.
The conventional product recommendation method is a general product recommendation method for common users, and when aiming at the special users, the user information is not comprehensive enough, the accuracy is not enough when the financial products are recommended for specific retired users, and the evaluation efficiency is low.
BRIEF SUMMARY OF THE PRESENT DISCLOSURE
In order to overcome the defects of the prior art, the product recommendation method based on the user personal information is provided, so that the accuracy and the evaluation efficiency are improved when financial products are recommended for a specific retired user.
In order to achieve the above object, one or more embodiments of the present disclosure provide the following technical solutions:
in a first aspect, a product recommendation method based on user personal information is disclosed, which at least comprises the following steps:
the method comprises the steps of profiling a user, collecting personal information data of the user in the forms of a terminal questionnaire and the like, and constructing an evaluation information set;
simulating future stock trends and expected income of users to be evaluated by using a geometric Brownian motion model, preliminarily processing an evaluation information set, and obtaining model parameters by using a moment estimation method;
and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user to be evaluated by using a dynamic planning method, and sending the recommended financial product to the corresponding terminal.
In a second aspect, a product recommendation system based on personal information of a user is disclosed, which at least comprises:
an information collection unit configured to: the method comprises the steps of profiling a user, collecting personal information data of the user in the forms of a terminal questionnaire and the like, and constructing an evaluation information set;
an information processing unit configured to: simulating future stock trends and expected income of users to be evaluated by using a geometric Brownian motion model, preliminarily processing an evaluation information set, and obtaining model parameters by using a moment estimation method;
an investment proportion generation and evaluation unit configured to: and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user to be evaluated by using a dynamic planning method, and sending the recommended financial product to the corresponding terminal.
The above one or more technical solutions have the following beneficial effects:
the technical scheme of the method includes the steps of constructing a random optimal control model based on an evaluation information set of a user to be evaluated, generating an optimal investment proportion and a corresponding investment satisfaction index of the user to be evaluated by a dynamic planning method, sending a recommended financial product to a corresponding terminal, overcoming the defect that the investment proportion of each stock in a stock fund is fixed for different users, customizing the optimal investment proportion for the investors according to the conditions of the investors, providing the corresponding investment satisfaction index, and having flexibility and accuracy.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flowchart illustrating an implementation of a product recommendation method based on user personal information according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating an implementation details of S4 in a method for recommending products based on personal information of a user according to an embodiment of the present disclosure;
fig. 3 is a flowchart illustrating an implementation details of S5 in a method for recommending products based on personal information of a user according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a product recommendation device based on user personal information according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device for recommending products based on personal information of a user according to an embodiment of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
A product recommendation method based on user personal information is disclosed, the overall concept is as follows:
the method comprises the steps of profiling a user, collecting personal information data of the user in the forms of a terminal questionnaire and the like, and constructing an evaluation information set;
simulating future stock trends and expected income of users to be evaluated by using a geometric Brownian motion model, preliminarily processing an evaluation information set, and obtaining model parameters by using a moment estimation method;
and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user to be evaluated by using a dynamic planning method, and sending the recommended financial product to the corresponding terminal.
Example one
The embodiment discloses a product recommendation method based on user personal information, please refer to fig. 1, and fig. 1 is a flowchart illustrating an implementation of the product recommendation method based on user personal information according to the embodiment of the present disclosure. A product recommendation method based on user personal information as shown in fig. 1 at least comprises the following steps:
s1: the fund company issues optional portfolio information.
The fund company carries out stock selection in the stock market and constructs various investment portfolios. The fund company releases specific stocks included in the investment portfolio and provides price information of each stock in each investment portfolio and information of risk-free interest rate in the financial market for the user to refer to.
S2: the user selects a target portfolio among the selectable portfolios.
The user selects at least 1 investment portfolio to invest by referring to the investment portfolio information given by the fund company, the selected investment portfolio is called the target investment portfolio of the user, and the number of stocks contained in the target investment portfolio is N.
S3: and collecting personal information of the user, wherein the personal information comprises information such as investment amount, investment period, retirement income, physical health condition, risk aversion degree and the like.
Personal information of the user is collected through on-line questionnaires and the like, wherein the personal information comprises age, gender, history of serious diseases, current physical health condition, aversion degree to risks, investment amount, investment period and retirement income. The obtained investor personal information and the target investment portfolio information issued by the fund company jointly form an evaluation information set of the user, and the evaluation information set is stored in a database in a memory.
The information in the user evaluation information set is annotated as follows:
recording the investment amount of the user as x;
recording the investment period of the user as T;
recording the age of the user as a;
recording the expected life of the user as b;
recording the relative risk aversion index of the user as 1-R;
recording the income of the retirement fund of the current month of investment of the user as e;
the risk free interest rate in the market is recorded as r.
And preprocessing the information in the user evaluation information set, and completing missing information or deleting repeated information.
S4: and performing primary processing on the evaluation information set of the user, processing the stock price information in the target investment portfolio and the retired fund income information of the user, and estimating model parameters such as expected income rate and expected fluctuation rate of the stock price in the target investment portfolio, expected income growth rate and expected income fluctuation rate of the retired fund income of the user, and the correlation coefficient between the retired fund income of the user and the stock price by using a statistical method. And a foundation is laid for completely constructing a random optimal control model later.
The present disclosure uses a geometric brownian motion model to fit stock price trends, i.e.
dPi(t)＝Pi(t)[uidt+σidBi(t)],
Wherein, Pi(t) closing price of ith stock in target investment portfolio at time t, uiExpected profitability, σ, for the ith stock priceiExpected volatility of the ith stock price, Bi(t) is the i-th component of the N-dimensional standard Brownian motion B (t).
The present disclosure employs a geometric brownian motion model to fit the user's retirement income trend, i.e.
de(t)＝e(t)[vdt+dW(t)],
Wherein e (t) is the retirement fund income of the user at the time t, v is the expected income increase rate of the retirement fund income of the user, and v is the retirement fund income of the userExpected revenue volatility, w (t), is 1-dimensional standard brownian motion. B isiThe correlation coefficient between (t) and W (t) is ρi。
The present disclosure uses a moment estimation method in statistics to predict the expected profitability u in a model using stock price data and user retirement income data in a target portfolio over the past T/2 timeiExpected fluctuation ratio σiExpected revenue growth rate v, expected revenue fluctuation rate, correlation coefficient ρi。
Referring to fig. 2, S4 can be specifically realized through S41-S43 shown in fig. 2, and the specific steps are as follows:
s41: the number of stock trading days in the past T/2 time is recorded as M1The average number of the annual stock trading days in the T/2 recorded time is M2. Estimating expected rate of return uiExpected fluctuation ratio σiThe specific method comprises the following steps:
calculating the logarithmic rate of return for each day over the past T/2 time: LPi(j)＝lnPi(j+1)-lnPi(j)；
Calculating the mean of the logarithmic yield over the past T/2 time:
calculate the variance of the log rate of return over the past T/2 time:
calculating the expected profitability of the ith stock: u. ofi＝M2[E(LPi)+0.5Var(LPi)]；
Calculating the expected fluctuation rate of the ith stock:
s42: recording the number of months in the past T/2 time as M3. The specific method for estimating the expected income growth rate v and the expected income fluctuation rate is as follows:
calculate the monthly logarithmic growth rate over the past T/2 time: le (j) ═ lne (j +1) -lne (j);
calculate the mean of log growth rate over the past T/2 time:
calculate the variance of the log growth rate over the past T/2 time:
calculating an expected revenue growth rate for retirement fund revenue: v ═ 12[ e (le) +0.5var (le) ];
calculating an expected revenue fluctuation rate for retirement fund revenue:
s43: recording the closing price of the ith stock on the first trading day of each month in the past T/2 time as
Estimating the correlation coefficient piThe specific method comprises the following steps:
calculate the average of the log stock price and the log retirement income over the past T/2 time:
calculate the variance of the log stock price and log retirement income over the past T/2 time:
calculate the covariance between the logarithmic stock price and the logarithmic retirement income:
calculation of BiCorrelation coefficient between (t) and w (t):
s5: and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user to be evaluated by using a dynamic planning method, and sending the recommended financial product to the corresponding terminal.
And constructing a random optimal control model. The model comprises a system equation, a functional equation and a random optimal control problem, wherein:
the system equation is:
where τ is the transpose of the matrix, 1 is the N-dimensional column vector with all 1 elements, and u is the ith component and uiIs the ith diagonal element ofiIs the ith component is rhoiY (t) is the investment wealth-income ratio of the user at the time t, and the N-dimensional column vector pitThe investment ratio of the N stocks in the investment portfolio at time t. The initial investment wealth-income ratio of the user is
The functional equation is:
where α is a random variable that follows an exponential distribution with a mean value b-a, and min (T, α) takes the value of the smallest of T and α.
The random optimal control problem is: finding optimal controls
And according to a system equation, maximizing a functional equation.
Optimal control for maximizing functional equations
The optimal investment proportion of the user is obtained, and the maximum value of the corresponding functional equation is the investment satisfaction index of the user.
The specific steps for generating the optimal investment proportion of the user and the corresponding investment satisfaction index by using the dynamic programming method are described below.
Firstly, the dynamic programming method is used to derive the HJB equation of the random optimal control problem as follows:
where V (t, y) is a function of the value of the random optimal control problem.
Further, solving the HJB equation to obtain the value function and optimal control of the random optimal control problem are respectively:
wherein the content of the first and second substances,
σστ-1 is the inverse of the matrix σ σ τ, and σ τ -1 is the inverse of the matrix σ τ.
Finally, referring to fig. 3, S5 can be specifically realized through S51-S53 shown in fig. 3, and the specific steps are as follows:
s51: calculating pi*；
S52: judging pi*τWhether 1 is less than or equal to 1 or not;
s53: according to pi*τWhether 1 is less than or equal to 1 or not, generating the optimal investment proportion of the user and the corresponding investment satisfaction index according to two conditions, wherein the specific generation mode is as follows:
π*τless than or equal to 1, and the optimal investment proportion of the user in the target investment combination is pi*The proportion of investment to non-risk assets is 1-pi*τ1, the corresponding investment satisfaction index is V (0, y);
π*τless than or equal to 1 is not true, and the optimal investment proportion of the user in the target investment combination is
No investment is made on the risk-free assets, and the corresponding investment satisfaction index is
In addition, technical obstacles may exist in using electronic products for the old, or information receiving is inaccurate, and the evaluation result is subjected to voice broadcasting. The user only needs to select a target investment combination and input personal basic information, the background executes a related algorithm, and then final product recommendation and evaluation results are output for the customer to confirm.
It can be seen from the above that, in the product recommendation method based on the user personal information provided by the present embodiment, firstly, the evaluation information set of the user is formed by collecting the user personal information and the stock price information in the user target investment portfolio; then, processing data in the user evaluation information set by using a moment estimation method, and estimating parameters in the model by using stock price data and user retirement fund income data in a target investment portfolio within the past T/2 time; finally, constructA random optimal control model, using a dynamic programming method according to pi*τIf 1 is less than or equal to 1, the optimal investment proportion of the user and the corresponding investment satisfaction index are automatically generated according to two conditions. The method fully considers the risk of terminating the investment caused by the fact that the user leaves, and if the investment is due and the investor leaves, the investment is terminated immediately; the method can customize the optimal investment proportion for the investor according to the self condition of the investor and provide corresponding investment satisfaction index; the method is implemented mainly by a computer program, does not need labor cost, and can objectively and accurately calculate the optimal investment proportion of each user and the corresponding investment satisfaction index.
Example two
Referring to fig. 4, fig. 4 is a schematic structural diagram of a product recommendation device based on user personal information according to an embodiment of the present disclosure. The product recommendation device 100 includes units for performing the steps in the embodiments corresponding to fig. 1 to 3. Please refer to fig. 1 to 3 and fig. 1 to 3 for the corresponding embodiments. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 4, the product recommendation device 100 includes: an information issuing unit 101, an investment portfolio selection unit 102, an information collection unit 103, an information processing unit 104, and an investment proportion generation and evaluation unit 105. Wherein:
the information issuing unit 101 is configured to issue information of the optional investment portfolio. The alternative portfolio is constructed by a fund company that has selected shares in the stock market. The fund company publishes specific stocks included in the portfolio and gives information such as price information of each stock in each portfolio and risk-free interest rate in the financial market.
The portfolio selection element 102 is operable to select a target portfolio of users.
The information collection unit 103 is used for collecting personal information of the user, including information of investment amount, investment term, retirement income, physical health condition, risk aversion degree, and the like.
The information processing unit 104 is configured to process the stock price information in the target portfolio and the personal information of the user to estimate model parameters including an expected profitability of the stock price in the target portfolio, an expected volatility and an income growth rate of the user's retirement income, an income volatility and a correlation coefficient between the user's retirement income and the stock price.
The investment proportion generating and evaluating unit 105 is used for generating an optimal investment proportion of the user and a corresponding investment satisfaction index.
EXAMPLE III
Referring to fig. 5, fig. 5 is a schematic structural diagram of a product recommendation electronic device based on user personal information according to an embodiment of the present disclosure. The electronic device may be a smart phone, a computer, or the like, which is not limited herein. The electronic apparatus 200 includes: processor 201, memory controller 202, memory 203, input device 204, output device 205. Wherein:
the processor 201 may be a central processing unit, a microprocessor, any conventional processor, etc. The processor 201 is used for processing information in a database and executing computer programs. The processor 201 is connected to other structures in the device via a system bus.
The storage controller 202 is configured to receive an instruction from the processor 201, and write, modify, and delete database data in the memory 203.
The storage 203 may be an internal storage unit of the electronic device 200, such as a hard disk or a memory of the electronic device 200. The memory 203 may also be an external storage device of the electronic device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 200. The memory 203 has stored thereon a database and a computer program. The database at least comprises an evaluation information set of a user, and the computer program calls data in the database when running so as to realize steps S4-S5.
The input device 204 may be a mouse, keyboard, tablet, voice input, touch screen input, etc. The input device 204 is used for inputting the target investment portfolio of the user and the evaluation information set into the database in the memory 203.
The output device 205 may be a display, printer, plotter, etc. The output means 205 is used to output the information of the selectable investment portfolio and the optimal investment proportions generated by the computer program and the corresponding investment satisfaction index to the user.
Example four
The object of the present embodiment is to provide a computing device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the specific steps of the method in the first embodiment.
EXAMPLE five
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, performs the specific steps of the method in the first embodiment.
The steps involved in the apparatus of the above embodiment correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present disclosure.
The technical scheme fully considers the risk of terminating the investment of the user due to the fact that the user leaves, if the user is unfortunate before the investment is due, the investment is stopped immediately, and the method has higher safety.
The implementation of the technical scheme disclosed by the invention mainly depends on a computer program, does not need labor cost, can objectively and accurately calculate the optimal investment proportion of each user and the corresponding investment satisfaction index, and has practical value.
Those skilled in the art will appreciate that the modules or steps of the present disclosure described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code executable by computing means, whereby the modules or steps may be stored in memory means for execution by the computing means, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A product recommendation method based on user personal information is characterized by at least comprising the following steps:
the method comprises the steps of profiling a user, collecting personal information data of the user, and constructing an evaluation information set;
simulating future stock trends and expected income of users to be evaluated by using a geometric Brownian motion model, preliminarily processing an evaluation information set, and obtaining model parameters by using a moment estimation method;
and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user to be evaluated by using a dynamic planning method, and sending the recommended financial product to the corresponding terminal.
2. The product recommendation method according to claim 1, wherein the personal information of the user is collected, including age, sex, history of major diseases, current health status, aversion degree to risks, investment amount, investment period, retirement income condition;
the obtained investor personal information and the target investment portfolio information issued by the fund company jointly form an evaluation information set of the user, and the evaluation information set is stored in a database in a memory.
3. The method as claimed in claim 2, wherein the information in the user evaluation information set is preprocessed to complement missing information or delete duplicate information.
4. The method of claim 1, wherein a geometric brownian motion model is used to fit a stock price trend and a retirement income trend of the user.
5. The method as claimed in claim 4, wherein the expected profitability of the stock price, the expected fluctuation rate and the expected income increase rate of the retirement income, the expected income fluctuation rate and the correlation coefficient therebetween are estimated in the model by using the stock price data and the user retirement income data in the target portfolio for the past set time by using a moment estimation method in statistics.
6. The method as claimed in claim 1, wherein a stochastic optimal control model is constructed, and the optimal investment proportion and corresponding investment satisfaction index of the user are generated by a dynamic programming method.
7. The method as claimed in claim 6, wherein the product recommendation method based on the user's personal information is based on pi*τIf 1 is not more than 1, generating the optimal investment proportion and the corresponding investment satisfaction index of the user according to two conditions, and sending the recommended financial products to the corresponding terminals to ensure that the functional equation is satisfiedOptimum control for maximum value
I.e. the optimal investment proportion of the user, and tau is a matrix transposition symbol.
8. A product recommendation device based on personal information of a user, comprising at least:
an information collection unit configured to: the method comprises the steps of profiling a user, collecting personal information data of the user in the forms of a terminal questionnaire and the like, and constructing an evaluation information set;
an information processing unit configured to: simulating future stock trends and expected income of users to be evaluated by using a geometric Brownian motion model, preliminarily processing an evaluation information set, and obtaining model parameters by using a moment estimation method;
an investment proportion generation and evaluation unit configured to: and further processing the evaluation information set, constructing a random optimal control model, generating the optimal investment proportion and the corresponding investment satisfaction index of the user by using a dynamic planning method, and sending the recommended financial products to the corresponding terminals.
9. An electronic device, such as a smart phone, a computer, etc., comprising a processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the program.
10. A computing 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 method of any of the preceding claims 1 to 7 when executing the program.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112927087A (en) * | 2021-02-03 | 2021-06-08 | 泛华普益基金销售有限公司 | Financing information processing system, financing information processing method, computer device, and storage medium |
| CN114581249A (en) * | 2022-03-22 | 2022-06-03 | 山东大学 | Financial product recommendation method and system based on investment risk bearing capacity assessment |
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| WO2018233301A1 (en) * | 2017-06-20 | 2018-12-27 | 平安科技（深圳）有限公司 | Product recommendation method, apparatus, and device, and computer readable storage medium |
| CN111445088A (en) * | 2020-04-16 | 2020-07-24 | 北京智慧丰泰科技有限公司 | Family financial planning method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2018233301A1 (en) * | 2017-06-20 | 2018-12-27 | 平安科技（深圳）有限公司 | Product recommendation method, apparatus, and device, and computer readable storage medium |
| CN111445088A (en) * | 2020-04-16 | 2020-07-24 | 北京智慧丰泰科技有限公司 | Family financial planning method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112927087A (en) * | 2021-02-03 | 2021-06-08 | 泛华普益基金销售有限公司 | Financing information processing system, financing information processing method, computer device, and storage medium |
| CN114581249A (en) * | 2022-03-22 | 2022-06-03 | 山东大学 | Financial product recommendation method and system based on investment risk bearing capacity assessment |
| CN114581249B (en) * | 2022-03-22 | 2024-05-31 | 山东大学 | Financial product recommendation method and system based on investment risk bearing capacity assessment |
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