CN114780859A

CN114780859A - Information recommendation method and device, computer equipment and storage medium

Info

Publication number: CN114780859A
Application number: CN202210583287.XA
Authority: CN
Inventors: 高其成; 陈少杰; 谢义; 杨龙
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-05-25
Filing date: 2022-05-25
Publication date: 2022-07-22

Abstract

The application relates to the technical field of artificial intelligence, and provides an information recommendation method, an information recommendation device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring answer data of a questionnaire submitted by a user; inputting the answer data into a risk evaluation model to obtain a target risk evaluation grade; generating major asset configuration suggestion information based on the target risk evaluation grade, the answer data and the major asset market prediction data; determining a first recommended investment product corresponding to the target risk evaluation level; generating an investment capacity value of a user; screening a second recommended investment product from the first recommended investment product based upon the investment capacity value; screening a target recommended investment product from the second recommended investment product based on the investment product data; and pushing the investment recommendation information to the user. The method and the device can improve the efficiency and accuracy of recommending the product information. The method and the device can also be applied to the field of block chains, and the investment recommendation information can be stored on the block chains.

Description

Information recommendation method and device, computer equipment and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to an information recommendation method, an information recommendation device, computer equipment and a storage medium.

Background

With the development of information technology, more and more users use networks for information interaction, such as purchasing investment products. With the explosive growth of internet information, the difficulty of finding valuable information by users is increasing, for example, when users need to buy investment products, users mainly rely on searching from a plurality of investment products by themselves or a salesman recommends the investment products for the users. However, when searching for an investment product by a user, the user experience is poor because the user has limited knowledge of the investment product and needs to search for an investment product meeting the needs for many times. When investment products are recommended for the user by the salesman, the accuracy of recommendation is low because the salesman cannot know the user, the investment products meeting the personalized requirements of the user cannot be recommended through multiple times of recommendation, a large amount of manpower and material resources are needed, the recommendation cost is high, and the intelligence is low.

Disclosure of Invention

The application mainly aims to provide an information recommendation method, an information recommendation device, computer equipment and a storage medium, and aims to solve the technical problems of low recommendation accuracy and high recommendation cost of the existing product information recommendation method.

The application provides an information recommendation method, which comprises the following steps:

acquiring answer data of a preset questionnaire submitted by a user;

inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user;

generating large-class asset configuration suggestion information corresponding to the user based on the target risk evaluation grade, the answer data and preset large-class asset market prediction data;

determining a first recommended investment product corresponding to the target risk evaluation grade based on the target risk evaluation grade;

acquiring account information and investment product data of the user, and generating an investment capacity value of the user based on the account information and the investment product data;

acquiring standard purchase amount values of all the first recommended investment products, and screening out second recommended investment products from all the first recommended investment products on the basis of the investment capacity values and the standard purchase amount values;

extracting product data characteristics of the investment product data, calculating a matching degree value of the product data characteristics and each second recommended investment product, and screening out a target recommended investment product of which the matching degree value is greater than a preset matching degree threshold value from all the second recommended investment products;

pushing investment recommendation information to the user; wherein the investment recommendation information comprises the broad asset allocation recommendation information and the target recommended investment product.

Optionally, the questionnaire includes evaluation question data of multiple dimensions, each dimension includes at least one choice question, each choice question includes at least two options, and each option corresponds to a score; the step of inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user includes:

inputting the answer data into the risk evaluation model, and obtaining the score of each choice question through the risk evaluation model according to the answer data;

setting corresponding dimension weight for each dimension respectively, and setting corresponding option weight for the choice questions in each dimension in the questionnaire;

based on the option weight, carrying out weighted summation on the score of each choice question in the questionnaire under the same dimensionality to obtain an evaluation score of each dimensionality in the questionnaire;

based on the dimension weight, carrying out weighted summation on the evaluation score of each dimension in the questionnaire to obtain a corresponding comprehensive risk evaluation score;

acquiring a mapping relation between a risk evaluation score and a risk evaluation grade from a preset mapping table;

and determining the target risk evaluation grade corresponding to the comprehensive risk evaluation score from the preset mapping table based on the mapping relation.

Optionally, the step of generating, based on the target risk evaluation level, the answer data, and preset large-category asset market forecast data, large-category asset configuration suggestion information corresponding to the user includes:

calling a preset prediction model;

performing benefit prediction processing and risk prediction processing on each type of assets through the prediction model to obtain corresponding prediction results;

extracting investment deadline information of the user from the answer data;

and determining the large-class asset configuration suggestion information corresponding to the user based on the prediction result, the target risk evaluation level and the investment deadline information.

Optionally, the step of determining a first recommended investment product corresponding to the target risk evaluation level based on the target risk evaluation level includes:

obtaining a target risk type corresponding to the target risk evaluation grade;

screening out a first investment product corresponding to the target risk type from preset investment products; wherein the first investment product is plural in number;

obtaining evaluation data of each of the first investment products;

calculating a product score for each of the first investment products based on the valuation data;

sorting the product scores in the numerical order from large to small to obtain corresponding sorting results;

starting from the first product score in the sorting result, sequentially acquiring the assigned product scores in the assigned quantity;

acquiring second investment products corresponding to the scores of the designated products respectively;

the second investment product is designated as the first recommended investment product.

Optionally, the step of generating the value of the investment capacity of the user based on the account information and the investment product data includes:

extracting account balance information and the holding quantity of investment products from the account information based on a preset first rule expression; and the number of the first and second groups,

extracting product unit price information and product expiration time information from the investment product data based on a preset second regular expression;

and calling a preset calculation formula to calculate the investment capacity value of the user based on the account balance information, the holding quantity of the investment products, the unit price information of the products and the expiration time information of the products.

Optionally, the step of pushing investment recommendation information to the user includes:

acquiring historical investment product purchase information corresponding to the user;

determining an information recommendation time period corresponding to the user based on a preset user attribute library and the historical investment product purchase information;

and pushing the investment recommendation information to the user based on the product recommendation time period.

Optionally, the step of determining an information recommendation time period corresponding to the user based on a preset user attribute library and the historical investment product purchase information includes:

calling the user attribute library, and judging whether user information corresponding to the user exists in the user attribute library or not;

if yes, inquiring attribute data corresponding to the user information from the user attribute library; the attribute data comprises basic attribute data and working attribute data;

analyzing the time trajectory of the attribute data to obtain a time schedule corresponding to the user;

acquiring the purchase time in the historical investment product purchase information;

and determining the information recommendation time period based on the time table and the purchase time.

The present application further provides an information recommendation device, including:

the first acquisition module is used for acquiring answer data of a preset questionnaire submitted by a user;

the first generation module is used for inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user;

the second generation module is used for generating the large-class asset configuration suggestion information corresponding to the user based on the target risk evaluation grade, the answer data and preset large-class asset market prediction data;

the determining module is used for determining a first recommended investment product corresponding to the target risk evaluation level based on the target risk evaluation level;

the second acquisition module is used for acquiring the account information and the investment product data of the user and generating the investment capacity value of the user based on the account information and the investment product data;

the first screening module is used for obtaining the standard purchase amount value of all the first recommended investment products and screening out second recommended investment products from all the first recommended investment products on the basis of the investment capacity value and the standard purchase amount value;

the second screening module is used for extracting the product data characteristics of the investment product data, calculating the matching degree value of the product data characteristics and each second recommended investment product, and screening out the target recommended investment products of which the matching degree value is greater than a preset matching degree threshold value from all the second recommended investment products;

the pushing module is used for pushing investment recommendation information to the user; wherein the investment recommendation information comprises the broad asset allocation recommendation information and the target recommended investment product.

The present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method.

The information recommendation method, the information recommendation device, the computer equipment and the storage medium have the following beneficial effects:

according to the information recommendation method, the device, the computer equipment and the storage medium, after answer data of a preset questionnaire submitted by a user are obtained, a preset risk evaluation model is called first to obtain a corresponding target risk evaluation grade based on the answer data, then, based on the target risk evaluation grade, the answer data and preset large-class asset market prediction data, large-class asset configuration suggestion information corresponding to the user is generated, meanwhile, a first recommended investment product corresponding to the user is determined based on the target risk evaluation grade, then, a corresponding investment capacity value is generated according to account information and investment product data of the user, a second recommended investment product is screened out from the first recommended investment product based on the investment capacity value and a standard purchase amount value, and finally, a target recommended investment product is screened out from all the second recommended investment products based on the investment product data And finally, the investment recommendation information containing the large-class asset configuration recommendation information and the target recommended investment product is pushed to the user, so that the pushing processing of the investment recommendation information to the user can be automatically, intelligently and quickly completed, a large amount of human resources do not need to be occupied, the manual workload is greatly reduced, the cost of investment product information recommendation is effectively reduced, the recommendation efficiency of the investment product information is improved, and the accuracy of the generated investment recommendation information is ensured.

Drawings

Fig. 1 is a schematic flowchart of an information recommendation method according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of an information recommendation device according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.

The implementation, functional features and advantages of the object of the present application will be further explained with reference to the embodiments, and with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Referring to fig. 1, an information recommendation method according to an embodiment of the present application includes:

s10: acquiring answer data of a preset questionnaire submitted by a user;

s20: inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user;

s30: generating large-class asset configuration suggestion information corresponding to the user based on the target risk evaluation grade, the answer data and preset large-class asset market prediction data;

s40: determining a first recommended investment product corresponding to the target risk evaluation grade based on the target risk evaluation grade;

s50: acquiring account information and investment product data of the user, and generating an investment capacity value of the user based on the account information and the investment product data;

s60: acquiring standard purchase amount values of all the first recommended investment products, and screening out second recommended investment products from all the first recommended investment products on the basis of the investment capacity values and the standard purchase amount values;

s70: extracting product data characteristics of the investment product data, calculating matching degree values of the product data characteristics and each second recommended investment product, and screening out target recommended investment products of which the matching degree values are larger than a preset matching degree threshold value from all the second recommended investment products;

s80: pushing investment recommendation information to the user; wherein the investment recommendation information comprises the broad category asset allocation recommendation information and the target recommended investment product.

As described in the above steps S10 to S80, the execution subject of the embodiment of the method is an information recommendation apparatus. In practical applications, the information recommendation device may be implemented by a virtual device, such as a software code, or by an entity device in which a relevant execution code is written or integrated, and may perform human-computer interaction with a user through a keyboard, a mouse, a remote controller, a touch panel, or a voice control device. The information recommendation device in the embodiment can automatically, intelligently and quickly complete the pushing processing of the investment recommendation information to the user, improve the recommendation efficiency of the investment product information and ensure the accuracy of the investment product information recommendation. Specifically, answer data of a preset questionnaire submitted by a user is first acquired. The questionnaire is a questionnaire for acquiring related information of a user portrait, and the user portrait can include user basic information, assets, risk preferences, investment experience and liquidity preferences, namely, various types of dimension information such as acceptable investment period, population attribute dimension and investment purpose. The answer data refers to answers generated after the user answers questions in the questionnaire. And after obtaining answer data, inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user. Wherein the risk evaluation model is a model for counting corresponding target risk evaluation grades based on the answer data. In addition, for the specific implementation process of inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation level corresponding to the user, this will be further described in the following specific embodiments, which is not repeated herein.

And after a target risk evaluation grade is obtained, generating major asset configuration suggestion information corresponding to the user based on the target risk evaluation grade, the answer data and preset major asset market prediction data. The method comprises the steps of calling a preset prediction model to conduct income prediction processing and risk prediction processing on each major asset to obtain a corresponding prediction result, and determining major asset configuration suggestion information corresponding to a user based on the prediction result, the target risk evaluation level and investment deadline information in answer data. And then determining a first recommended investment product corresponding to the target risk evaluation grade based on the target risk evaluation grade. For the specific implementation process of determining the first recommended investment product corresponding to the target risk evaluation level based on the target risk evaluation level, this will be further described in the following specific embodiments, which are not repeated herein. And then acquiring account information and investment product data of the user, and generating an investment capacity numerical value of the user based on the account information and the investment product data. The account balance information and the holding amount of the investment products can be extracted from the account information, the unit price information and the expiration time information of the investment products are extracted from the investment product data, and then a preset calculation formula is called to calculate the investment capacity value of the user based on the obtained information, wherein the calculation formula further describes the investment capacity value in the subsequent specific embodiment.

And subsequently acquiring the standard purchase amount value of all the first recommended investment products, and screening out second recommended investment products from all the first recommended investment products based on the investment capacity value and the standard purchase amount value. Wherein the second recommended investment product refers to a product of which the standard purchase amount value is smaller than the investment capacity value in all the first recommended investment products. The standard purchase amount value may be obtained by querying product data associated with the recommended investment product. The investment capacity value can be understood as the purchasing power of the user, the obtained recommended investment products are primarily screened based on the investment capacity value, the accuracy of the obtained second recommended investment product is ensured, and the second recommended investment product and the investment products of the user only need to be subjected to matching calculation subsequently, so that the data matching processing amount is reduced, the power consumption is reduced, and the intelligence of data processing is improved. After second recommended investment products are obtained, extracting product data characteristics of the investment product data, calculating the matching degree value of the product data characteristics and each second recommended investment product, and screening out target recommended investment products with the matching degree value larger than a preset matching degree threshold value from all the second recommended investment products. Wherein the product data characteristics include, but are not limited to, product type, product unit price information, product expiration time information. Specifically, the formula for calculating the matching degree value is as follows:

p is a matching degree value, x is the product data characteristic, y_nA digitized representation of data corresponding to the product data characteristic for an nth product of the second recommended investment products. After the obtained first recommended investment product is primarily screened based on the purchasing power of the user, namely the investment capacity value, the product data characteristics of the investment product data of the user are analyzed subsequently, so that the obtained second recommended investment product is secondarily screened according to the product data characteristics, the investment product recommendation of the user is pertinently realized, the accuracy of the investment product recommendation is further improved, and the problem of low accuracy in the investment product recommendation of the user can be effectively solved. And finally, pushing investment recommendation information to the user. Wherein the investment recommendation information comprises the broad asset allocation recommendation information and the target recommended investment product. Specifically, the major asset allocation recommendation information and the target recommended investment product may be filled into a preset investment recommendation information template to generate the investment recommendation information. The investment recommendation information template can be generated in advance according to actual use requirements and stored in the device.

Different from the existing method of manually recommending investment products, in this embodiment, after answer data of a preset questionnaire submitted by a user is obtained, a preset risk evaluation model is called first to obtain a corresponding target risk evaluation level based on the answer data, then, based on the target risk evaluation level, the answer data and preset large-class asset market prediction data, large-class asset configuration suggestion information corresponding to the user is generated, a first recommended investment product corresponding to the user is determined based on the target risk evaluation level, then, a corresponding investment capacity value is generated according to account information of the user and investment product data, a second recommended investment product is screened from the first recommended investment product based on the investment capacity value and a standard purchase amount value, and then, based on the investment product data, a final target recommended investment product is screened from all the second recommended investment products, and finally, the investment recommendation information containing the large-class asset configuration recommendation information and the target recommended investment product is pushed to the user, so that the pushing processing of the investment recommendation information to the user is automatically, intelligently and quickly completed, a large amount of human resources do not need to be occupied, the manual workload is greatly reduced, the cost of investment product information recommendation is effectively reduced, the recommendation efficiency of the investment product information is improved, and the accuracy of the generated investment recommendation information is also ensured.

Further, in an embodiment of the present application, the questionnaire includes evaluation question data of multiple dimensions, each dimension includes at least one choice question, each choice question includes at least two options, and each option corresponds to one score; the step S20 includes:

s200: inputting the answer data into the risk evaluation model, and obtaining the score of each choice question through the risk evaluation model according to the answer data;

s201: setting corresponding dimension weight for each dimension respectively, and setting corresponding option weight for the choice questions under each dimension in the questionnaire;

s202: based on the option weight, carrying out weighted summation on the score of each choice question in the questionnaire under the same dimensionality to obtain an evaluation score of each dimensionality in the questionnaire;

s203: based on the dimension weight, carrying out weighted summation on the evaluation score of each dimension in the questionnaire to obtain a corresponding comprehensive risk evaluation score;

s204: acquiring a mapping relation between a risk evaluation score and a risk evaluation grade from a preset mapping table;

s205: and determining the target risk evaluation grade corresponding to the comprehensive risk evaluation score from the preset mapping table based on the mapping relation.

As described in the above steps S200 to S205, the step of inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation level corresponding to the user may specifically include: firstly, inputting the answer data into the risk evaluation model, and obtaining the score of each choice question through the risk evaluation model according to the answer data. The questionnaire can comprise various types of dimension information such as user basic information, assets, risk preferences, investment experience, liquidity preferences (namely acceptable investment duration), population attribute dimension, investment purpose and the like; indicators of a user's basic information dimension may include gender, age, marital status, occupation, and academic history. Each question in the questionnaire corresponds to at least two options, and each option has a corresponding score; the scoring basis of the asset dimension is the investment amount, for example, the asset dimension is divided into 10 sections from 1 ten thousand yuan to 100 ten thousand yuan, and each section corresponds to a corresponding score; the scoring of the risk preference dimension is based on a corresponding preset specified problem, which can be set according to actual requirements, for example, the specified problem may be: "to what extent do you feel uneasy when your total investment assets are devalued? ", each designated question corresponds to a plurality of options, and each option has a corresponding score; the investment experience dimension can be divided into five types of investment experience without investment, conservative financial products with investment, securities with investment under the guidance of others, independent investment securities and high-risk investment, and each investment type has a corresponding score; the liquidity preference dimension can be divided into a plurality of types according to the investment period acceptable to the user, and for example, can include one year or less, one year to two years, two years to five years, five years or more, and a random access type, each type having a corresponding score. And then setting corresponding dimension weight for each dimension respectively, and setting corresponding option weight for the selected questions in each dimension in the questionnaire. And setting a dimension weight for each dimension of the information, preferably setting the sum of all the dimension weights to be 1, setting an option weight for the choice questions in each dimension of the questionnaire, preferably setting the sum of the option weights of the choice questions in the same dimension to be 1. And then, based on the option weight, carrying out weighted summation on the score of each choice question in the questionnaire under the same dimensionality to obtain an evaluation score of each dimensionality in the questionnaire. The scores of the choices in the questionnaire under the same dimension can be multiplied by the corresponding option weights, and then summed, so as to obtain the evaluation score of each dimension of the questionnaire. And then based on the dimension weight, carrying out weighted summation on the evaluation score of each dimension in the questionnaire to obtain a corresponding comprehensive risk evaluation score. And multiplying the evaluation score of each dimension of the questionnaire by the corresponding dimension weight to obtain a sum value corresponding to each dimension information of the questionnaire, namely the comprehensive risk evaluation score of the user. And subsequently, obtaining a mapping relation between the risk evaluation score and the risk evaluation grade from a preset mapping table. Wherein the risk evaluation grade comprises conservative type, robust type, balanced type, growth type and aggressive type. The risk level of the product corresponding to the evaluation level of risk includes a low risk level, a medium and high risk level, and a high risk level. In addition, the specific interval of the risk evaluation score corresponding to each risk evaluation level in the preset mapping table can be set according to actual requirements. For example, the interval with the risk evaluation score smaller than 3 corresponds to a conservative type, the interval with the risk evaluation score between 4 and 5 corresponds to a robust type, the interval with the risk evaluation score between 5 and 7 corresponds to a balanced type, the interval with the risk evaluation score between 7 and 8 corresponds to a growing type, and the interval with the risk evaluation score larger than 8 corresponds to an access type. And finally, determining the target risk evaluation grade corresponding to the comprehensive risk evaluation score from the preset mapping table based on the mapping relation. In the embodiment, after answer data which are submitted by a user and correspond to a preset questionnaire are obtained, the user can be quickly and accurately divided into corresponding risk evaluation grades through a risk evaluation model, the division basis is objective and the method is simple, and the method is favorable for intelligently and accurately determining the major asset configuration suggestion information corresponding to the user and the target recommended investment product corresponding to the user based on the target risk evaluation grade in the follow-up process, so that the personalized investment recommendation for the user is accurately realized, and the use experience of the user is improved.

Further, in an embodiment of the application, the step S30 includes:

s300: calling a preset prediction model;

s301: performing benefit prediction processing and risk prediction processing on each type of assets through the prediction model to obtain corresponding prediction results;

s302: extracting investment period information of the user from the answer data;

s303: and determining the large-class asset configuration suggestion information corresponding to the user based on the prediction result, the target risk evaluation level and the investment deadline information.

As described in the foregoing steps S300 to S303, the step of generating the major asset allocation advice information corresponding to the user based on the target risk evaluation level, the answer data, and preset major asset market prediction data may specifically include: first, a preset prediction model is called. The preset prediction model is a time series model, and specifically, a Markov model can be adopted. And then performing benefit prediction processing and risk prediction processing on each type of assets through the prediction model to obtain a corresponding prediction result. The market prediction of the large-class assets can be risk prediction and benefit prediction of various large-class assets existing in the current market, such as cash, guarantee, stock, currency, solid income, rights and interests and the like. And then extracting the investment deadline information of the user from the answer data. And finally, determining the large-class asset configuration suggestion information corresponding to the user based on the prediction result, the target risk evaluation level and the investment deadline information. And the recommendation information of the large-class asset allocation is the ratio of each large-class asset in the asset allocation scheme recommended by the user. Specifically, the assets may be first classified, for example, the assets that the current user may hold are classified into 5 major categories, i.e., cash, guarantee, stock, currency, solid income, and equity, as the basis for market forecasting and major asset allocation. And then analyzing more than 400 types of explained economic factors on a macro scale and a micro scale to obtain factors which are effective on the 6 types of assets by taking the year as a unit, providing predicted values and correlation matrixes of the factors monthly, and calculating the exposure to the factors by combining historical profits of the assets, such as profits within 1 year to obtain the factor exposure and the correlation matrixes of the assets. And then, obtaining the configuration suggestion information of the large-class assets aiming at the user by adopting a Markov model and taking the obtained factor exposure and correlation matrix of each asset as the input of the model and combining the target risk evaluation grade of the user and the investment deadline information. For example, if the target risk evaluation level of the user is a balanced type (the risk level corresponding to the investment product is a medium risk level), and the investment term information is medium, according to the prediction data of the large-class market of the recent market, specifically, after determining each large-class market risk factor of the recent market according to members of the investment team, combining a markov model to predict the future factor, and performing regression on the large-class market to obtain the prediction of the future market, corresponding large-class asset configuration suggestion information is generated for the user according to the prediction result, the target risk evaluation level and the investment term information, such as 50% cash, 10% security, 10% stock, 10% currency, 10% solid income, and 10% benefit class. In the embodiment, the optimal large-class asset configuration suggestion information can be recommended for the user according to the user target risk evaluation level and the investment deadline information and the market prediction results of the income and the risk of each large-class asset, and the generated large-class asset configuration suggestion information has higher accuracy, pertinence and reliability because the large-class asset configuration suggestion information is generated by comprehensively considering the user figures with multiple dimensions.

Further, in an embodiment of the present application, the step S40 includes:

s400: obtaining a target risk type corresponding to the target risk evaluation grade;

s401: screening out a first investment product corresponding to the target risk type from preset investment products; wherein the first investment product is plural in number;

s402: obtaining evaluation data of each of the first investment products;

s403: calculating a product score for each of said first investment products based upon said valuation data;

s404: sorting the product scores in the numerical order from large to small to obtain corresponding sorting results;

s405: starting from the first product score in the sorting result, sequentially acquiring the assigned product scores in the assigned quantity;

s406: acquiring second investment products corresponding to the scores of the specified products respectively;

s407: the second investment product is considered the first recommended investment product.

As described in the foregoing steps S400 to S407, the step of determining, based on the target risk assessment level, a first recommended investment product corresponding to the target risk assessment level may specifically include: firstly, a target risk type corresponding to the target risk evaluation grade is obtained. The risk evaluation level can comprise a conservative type, a robust type, a balanced type, a growth type and an aggressive type. The risk grades of the product corresponding to the risk evaluation grades one by one comprise a low risk grade, a medium and high risk grade, and a high risk grade. And then screening out a first investment product corresponding to the target risk type from preset investment products. Wherein the first investment product is plural in number. In addition, the investment product may be all investment products available on the market. And then obtaining valuation data for each of said first investment products. Wherein the evaluation data may include at least a relative profit score, a maximum withdrawal score, and a manageability score. Specifically, the calculation method of the relative profit score includes: inquiring a performance comparison benchmark table, acquiring performance comparison benchmarks corresponding to the three preset time periods respectively, and subtracting the corresponding performance comparison benchmarks from absolute profits corresponding to the three preset time periods respectively to obtain corresponding relative profits of the three preset time periods respectively; and multiplying the corresponding relative gains of the three preset time periods by the corresponding preset weight coefficients to obtain the corresponding weight relative gains of the three preset time periods, and accumulating the corresponding weight relative gains of the three preset time periods to obtain the final relative gains of the investment product. Wherein the performance comparison benchmark for investment running is to define a suitable benchmark portfolio for the investment, and the performance of the investment products can be measured by comparing the profitability of the investment with the profitability of the performance comparison benchmark. The maximum backoff score is calculated by the following steps: inquiring a preset historical net value table of the complex weights, acquiring net values of the complex weights corresponding to three preset time periods respectively, dividing the net values of the complex weights in one of the preset time periods into two groups according to the sequence of dates, wherein one group comprises a first net value of the complex weights with a big net value date in one of the preset time periods, the other group comprises a second net value of the complex weights with a small net value date in one of the preset time periods, then calculating the maximum withdrawal of the fund product in the other two preset time periods according to max ((the second net value of the complex weights-the first net value of the complex weights)/the first net value of the complex weights), and multiplying the maximum withdrawal corresponding to the three preset time periods by the corresponding preset weight coefficients respectively after obtaining the maximum withdrawals corresponding to the three preset time periods, and obtaining the maximum withdrawal of the weight corresponding to each of the three preset time periods, and accumulating the maximum withdrawal of the weight corresponding to each of the three preset time periods to obtain the final maximum withdrawal of the investment product. Wherein the maximum withdrawal is the maximum value of the rate of return withdrawal amplitude when the net value of the product moves to the lowest point, pushed backwards at any historical time point in the selected period. The calculation mode of the management ability score is specifically as follows: inquiring an investment manager table, acquiring a current investment manager of any one investment product, acquiring all the investment products managed by the current investment manager and an appointed starting date of each investment product, then acquiring the actual appointed days of each investment product managed by the current investment manager, acquiring the net value of a reweigh unit of a newest net worth date and the net value of a reweigh unit of a trading day before the appointed starting date, finally calculating the average daily profitability of each investment product according to a formula (the net value of the reweigh unit of a trading day before the appointed starting date) ^ 365/actual appointed days) -1, accumulating the average daily profitability of all the investment products to obtain the total average daily profitability, dividing the total average daily profitability by the total investment, calculating the respective corresponding management capacities of the investment products in three preset time periods according to the month difference between the net value date of the newest reweigh unit and the appointed starting date, and then multiplying the management capacities corresponding to the three preset time periods by the preset weight coefficients corresponding to the three preset time periods to obtain the weight management capacities corresponding to the three preset time periods, and accumulating the weight management capacity scores corresponding to the three preset time periods to obtain the final management capacity score of the investment product. The manageability score is a management situation of a manager of the investment product. And calculating a product score for each of the first investment products based on the evaluation data. Wherein calculating a product score for each of the first investment products based upon the valuation data can include: and acquiring weighted values corresponding to the relative income score, the maximum withdrawal score and the management ability score respectively, and carrying out weighted summation processing on the relative income score, the maximum withdrawal score and the management ability score based on the acquired weighted values, wherein the acquired sum value is the corresponding product score. In addition, the value of the weight of the evaluation data is not particularly limited, and may be set based on the actual situation. And subsequently, sequencing the product scores according to the numerical order from large to small to obtain a corresponding sequencing result. And after the sorting result is obtained, sequentially obtaining the specified product scores in specified quantity from the first product score sorted in the sorting result. The value of the designated number is not limited, and may be set according to actual requirements, for example, may be set to 3. And finally, acquiring second investment products respectively corresponding to the assigned product scores, and taking the second investment products as the first recommended investment products. In the embodiment, the first investment product corresponding to the target risk type is screened from the preset investment products, so that only the product score of the first investment product needs to be calculated subsequently, and the data processing amount is effectively reduced. In addition, the product score of each first investment product can be rapidly and accurately calculated by using the evaluation data of each first investment product, so that the first recommended investment product can be accurately determined based on the obtained product score, and the method is favorable for subsequently screening the target recommended investment product from the first recommended investment product to recommend the target recommended investment product to the user, so that the personalized investment recommendation of the user is realized, and the use experience of the user is improved.

Further, in an embodiment of the application, the step S50 includes:

s500: extracting account balance information and the holding quantity of investment products from the account information based on a preset first rule expression; and (c) a second step of,

s501: extracting product unit price information and product expiration time information from the investment product data based on a preset second regular expression;

s502: and calling a preset calculation formula to calculate the investment capacity value of the user based on the account balance information, the holding quantity of the investment products, the unit price information of the products and the expiration time information of the products.

As described in the foregoing steps S500 to S502, the step of generating the numerical value of the investment ability of the user based on the account information and the investment product data may specifically include: firstly, extracting account balance information and holding quantity of investment products from the account information based on a preset first rule expression. The account information is information of an account used by the user when generating transaction data, and the account information includes but is not limited to an account name, an account balance, and a held product quantity of the account. In addition, the account information which can be obtained by the authorization of the user can be captured through a data buried point which is constructed in advance in an interface for transaction of the user, and the real-time performance of capturing the account information can be improved by capturing the user data through the data buried point. And extracting product unit price information and product expiration time information from the investment product data based on a preset second regular expression. The investment product data of the user refers to data such as product name, product content description, product price, product expiration time and the like of each product in the investment product. In addition, the pre-stored investment product of the user can be grabbed from the pre-constructed storage area through a computer sentence with a data grabbing function, such as a java sentence, a python sentence and the likeThe storage area of the product data can be a database, a block chain node and a network cache. Specifically, since the account balance information, the holding amount of the investment product, the unit price of the product, and the product expiration date are often stored in a fixed format in the account information and the investment product data, the above-mentioned account information and the investment product data can be extracted by using a regular expression. For example, the holding amount of the investment product exists in the investment product data in a form of a fixed "holding amount of the product xx", so that the investment product data can be extracted by using a regular expression to obtain all data existing in the investment product data in the form of "holding amount of the product xx", and then product expiration time information of each investment product corresponding to the user is obtained. The regular expression is a function which is compiled by a plurality of preset characters and has a specific field extraction function, and the regular expression can be generated by writing in advance according to actual use requirements. And then, based on the account balance information, the holding quantity of the investment products, the unit price information of the products and the expiration time information of the products, calling a preset calculation formula to calculate the investment capacity value of the user. Specifically, the calculation formula is as follows:

wherein S is the value of the user' S investment capacity at the expiration time of the kth investment product, U is the account balance at the expiration time of the kth investment product,

holding amounts of investment products for the ith investment product in said account information for the expiration time of the tth investment product, D_iα, β are preset weighting coefficients for the product unit price of the ith investment product in the account information at the expiration time of the tth investment product. For example, the account information is balance information of 1000 for 1 month and 1 day of the user account, the account holds 5 investment futures products a and holds 5 investment futures products B, and the investment product data includes the investment futures products a and the investment futures products B purchased by the userItem B, wherein the product unit price information of the investment futures product a is 200, the expiration time is 3 months and 1 day, the unit price of the futures product B is 500, the expiration time is 5 months and 1 day, the weight coefficient corresponding to the balance information is 1, and the weight coefficient corresponding to the product unit price information is 1.5; the value of the investment capacity of the user may be calculated by using the calculation formula based on the account information and the investment product data, and is 1 × 1000+1.5 × 5 × 200+1.5 × 5 × 500 ═ 6250. In this embodiment, the required account balance information, investment product holding quantity, product unit price information and product expiration time information can be quickly extracted from the account information of the user and the investment product data of the user by using a regular expression, so that a preset calculation formula can be called to quickly and accurately generate the investment capacity value of the user, and the method is beneficial to subsequently performing targeted investment product recommendation on the user based on the obtained investment capacity value, so as to improve the intelligence and accuracy of investment product recommendation.

Further, in an embodiment of the application, the step S80 includes:

s800: acquiring historical investment product purchase information corresponding to the user;

s801: determining an information recommendation time period corresponding to the user based on a preset user attribute library and the historical investment product purchase information;

s802: and pushing the investment recommendation information to the user based on the product recommendation time period.

As described in the foregoing steps S800 to S802, the step of pushing the investment recommendation information to the user may specifically include: firstly, historical investment product purchase information corresponding to the user is obtained. Wherein the historical investment product purchase information includes at least a purchase time of the historical investment product by the user. And then determining an information recommendation time period corresponding to the user based on a preset user attribute library and the historical investment product purchase information. For the specific implementation process of determining the information recommendation time period corresponding to the user based on the preset user attribute library and the historical investment product purchase information, this will be further described in the subsequent specific embodiments, which is not described herein again. And finally, pushing investment recommendation information to the user based on the product recommendation time period. Specifically, the process of pushing investment recommendation information to the user based on the product recommendation time period may include: acquiring preset mail login information; acquiring a target mail address corresponding to the user; logging in a corresponding mail server according to the mail login information; judging whether the current time is within the product recommendation time period or not; if so, generating corresponding investment recommendation information based on the large-class asset configuration recommendation information and the target investment product; and sending the investment recommendation information to the target mail address through the mail server. In this embodiment, after the large-class asset allocation suggestion information and the target investment product are obtained, corresponding investment recommendation information is intelligently generated based on the obtained large-class asset allocation suggestion information and the target investment product, and the investment recommendation information is sent to a target mail address corresponding to a user, so that the user can timely know investment recommendation content suitable for the user based on the investment recommendation information, and then, required investment behavior is executed based on the investment recommendation content, and the use experience of the user is improved.

Further, in an embodiment of the application, the step S801 includes:

s8010: calling the user attribute library, and judging whether user information corresponding to the user exists in the user attribute library or not;

s8011: if yes, inquiring attribute data corresponding to the user information from the user attribute library; the attribute data comprises basic attribute data and working attribute data;

s8012: analyzing the time trajectory of the attribute data to obtain a time table corresponding to the user;

s8013: acquiring the purchase time in the historical investment product purchase information;

s8014: and determining the information recommendation time period based on the time table and the purchase time.

As described in the foregoing steps S8010 to S8014, the step of determining the information recommendation time period corresponding to the user based on the preset user attribute library and the historical investment product purchase information may specifically include: firstly, calling the user attribute library, and judging whether the user attribute library has user information corresponding to the user. The user attribute library stores attribute data of each user, and the attribute data is associated with user information of each user, such as a user identification code or a user name, the attribute data is data related to basic attribute data, work attribute data and the like of the user, the basic attribute data comprises information related to basic characteristics of the user, such as sex, age, marital situation, family situation and the like of the user, and the work attribute data comprises information of social nature of the user, such as occupation, income situation, company, call number list and the like. And if so, inquiring attribute data corresponding to the user information from the user attribute library. The attribute data comprises basic attribute data and working attribute data. And then, carrying out time trajectory analysis on the attribute data to obtain a time schedule corresponding to the user. The time trajectory analysis is a process of extracting time dimensions in the attribute data, performing time period marking analysis to obtain tags of each time period, and outputting a time schedule of the user. The schedule is an estimated time distribution form for the user for one day. And then acquiring the purchase time in the historical investment product purchase information. And finally, determining the information recommendation time period based on the time table and the purchase time. Specifically, the process of determining the information recommendation time period may include: screening out a first idle time period from the time schedule; judging whether a first purchase time out of all the purchase times is out of the range of all the time periods contained in the time table; if the first purchasing time exists, screening out a second purchasing time with the minimum value from all the first purchasing times and screening out a third purchasing time with the maximum value; generating a corresponding second idle period based on the second purchase time and the third purchase time; performing union set processing on the first idle time period and the second idle time period to obtain a corresponding third idle time period; and taking the third idle time period as the information recommendation time period. The idle time periods can be marked according to the schedule, for example, the route time, the working time and the sleeping time of the user are removed from the schedule, so that the first idle time period is obtained through analysis. For example, the schedule is a working time of 9: 00 to 12: 30 and 14: 00 to 18: 00, the road time is 8: 00 to 8: 59 and 18: 01 to 19: 00, thereby marking a 12: 31 to 13: 59, 19: 01 to 23: the time period corresponding to 00 is an idle time period, and is determined as a first idle time period. In addition, the second purchase time is used as a left end point of the second idle time period, and the third purchase time is used as a right end point of the second idle time period. In this embodiment, a first idle time period is screened from a schedule, a second idle time period is determined from all the purchase times, and the first idle time period and the second idle time period are further subjected to union processing to obtain a corresponding third idle time period and used as an information recommendation time period. The generated information recommendation time period is generated by comprehensively considering the time schedule and the purchase time of the user, so that the information recommendation time period is ensured to be a time period suitable for recommending the product information to the user, the product information is recommended to the user in the information recommendation time period intelligently in the follow-up process, the use experience of the user can be improved, and the intelligence of the product information recommendation is improved.

The information recommendation method in the embodiment of the application can also be applied to the field of block chains, for example, the data such as the target recommended investment product and the like are stored on the block chain. By using the block chain to store and manage the target recommended investment product, the safety and the non-tamper property of the target recommended investment product can be effectively ensured.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The block chain underlying platform can comprise processing modules such as user management, basic service, intelligent contract and operation monitoring. The user management module is responsible for identity information management of all blockchain participants, and comprises the steps of maintaining public and private key generation (account management), key management, user real identity and blockchain address corresponding relation maintenance (authority management) and the like, and under the authorized condition, supervising and auditing the transaction condition of some real identities, and providing rule configuration (wind control audit) of risk control; the basic service module is deployed on all block chain node equipment and used for verifying the validity of the service request, recording the service request to storage after consensus on the valid request is completed, for a new service request, the basic service firstly performs interface adaptation analysis and authentication processing (interface adaptation), then encrypts service information (consensus management) through a consensus algorithm, transmits the service information to a shared account (network communication) completely and consistently after encryption, and performs recording and storage; the intelligent contract module is responsible for registering and issuing contracts, triggering the contracts and executing the contracts, developers can define contract logics through a certain programming language, issue the contract logics to a block chain (contract registration), call keys or other event triggering and executing according to the logics of contract clauses, complete the contract logics and simultaneously provide the function of upgrading and canceling the contracts; the operation monitoring module is mainly responsible for deployment, configuration modification, contract setting, cloud adaptation in the product release process, and visual output of real-time status in product operation, for example: alarm, monitoring network conditions, monitoring node equipment health status, and the like.

Referring to fig. 2, an embodiment of the present application further provides an information recommendation apparatus, including:

the first acquisition module 1 is used for acquiring answer data of a preset questionnaire submitted by a user;

the first generation module 2 is used for inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user;

the second generation module 3 is used for generating the major asset configuration suggestion information corresponding to the user based on the target risk evaluation level, the answer data and preset major asset market prediction data;

the determining module 4 is used for determining a first recommended investment product corresponding to the target risk evaluation level based on the target risk evaluation level;

a second obtaining module 5, configured to obtain account information and investment product data of the user, and generate an investment capacity value of the user based on the account information and the investment product data;

the first screening module 6 is used for obtaining the standard purchase amount value of all the first recommended investment products and screening out second recommended investment products from all the first recommended investment products based on the investment capacity value and the standard purchase amount value;

the second screening module 7 is used for extracting the product data characteristics of the investment product data, calculating the matching degree value of the product data characteristics and each second recommended investment product, and screening out the target recommended investment products of which the matching degree value is greater than a preset matching degree threshold value from all the second recommended investment products;

the pushing module 8 is used for pushing investment recommendation information to the user; wherein the investment recommendation information comprises the broad category asset allocation recommendation information and the target recommended investment product.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the information recommendation method in the foregoing embodiment one by one, and are not described herein again.

Further, in an embodiment of the present application, the questionnaire includes evaluation question data of multiple dimensions, each dimension includes at least one choice question, each choice question includes at least two options, and each option corresponds to one score; the first generating module 2 includes:

the input unit is used for inputting the answer data into the risk evaluation model and obtaining the score of each choice question according to the answer data through the risk evaluation model;

the setting unit is used for setting corresponding dimension weight for each dimension and setting corresponding option weight for the choice questions in each dimension in the questionnaire;

the first calculation unit is used for carrying out weighted summation on the score of each choice question in the questionnaire under the same dimensionality based on the option weight to obtain an evaluation score of each dimensionality in the questionnaire;

the second calculation unit is used for carrying out weighted summation on the evaluation score of each dimension in the questionnaire based on the dimension weight to obtain a corresponding comprehensive risk evaluation score;

the first obtaining unit is used for obtaining the mapping relation between the risk evaluation score and the risk evaluation grade from a preset mapping table;

and the first determining unit is used for determining the target risk evaluation grade corresponding to the comprehensive risk evaluation score from the preset mapping table based on the mapping relation.

In this embodiment, the operations performed by the modules or units correspond to the steps of the information recommendation method in the foregoing embodiment one by one, and are not described herein again.

Further, in an embodiment of the present application, the second generating module 3 includes:

the calling unit is used for calling a preset prediction model;

the processing unit is used for carrying out benefit prediction processing and risk prediction processing on each type of assets through the prediction model to obtain a corresponding prediction result;

a first extraction unit for extracting investment term information of the user from the answer data;

and the second determination unit is used for determining the major asset configuration suggestion information corresponding to the user based on the prediction result, the target risk evaluation level and the investment deadline information.

Further, in an embodiment of the present application, the determining module 4 includes:

the second obtaining unit is used for obtaining a target risk type corresponding to the target risk evaluation level;

the screening unit is used for screening out a first investment product corresponding to the target risk type from preset investment products; wherein the first investment product is plural in number;

a third acquisition unit configured to acquire evaluation data of each of the first investment products;

a third calculating unit for calculating a product score for each of the first investment products based on the evaluation data;

the sorting unit is used for sorting the product scores according to the numerical sequence from large to small to obtain a corresponding sorting result;

the fourth acquisition unit is used for sequentially acquiring the assigned product scores of the assigned quantity from the first product score in the sequencing result;

a fifth acquiring unit configured to acquire second investment products corresponding to the respective assigned product scores;

a third determination unit for determining the second investment product as the first recommended investment product.

Further, in an embodiment of the present application, the second obtaining module 5 includes:

the second extraction unit is used for extracting account balance information and the holding quantity of the investment products from the account information based on a preset first rule expression; and (c) a second step of,

a third extraction unit, configured to extract product unit price information and product expiration time information from the investment product data based on a preset second rule expression;

and the fourth calculating unit is used for calling a preset calculating formula to calculate the investment capacity value of the user based on the account balance information, the holding quantity of the investment products, the unit price information of the products and the expiration time information of the products.

Further, in an embodiment of the application, the pushing module 8 includes:

a sixth acquiring unit, configured to acquire historical investment product purchase information corresponding to the user;

a fourth determining unit, configured to determine, based on a preset user attribute library and the historical investment product purchase information, an information recommendation time period corresponding to the user;

and the pushing unit is used for pushing the investment recommendation information to the user based on the product recommendation time period.

Further, in an embodiment of the application, the fourth determining unit includes:

a calling subunit, configured to call the user attribute library, and determine whether user information corresponding to the user exists in the user attribute library;

the query subunit is used for querying attribute data corresponding to the user information from the user attribute library if the user information is in the attribute data; the attribute data comprises basic attribute data and working attribute data;

the analysis subunit is used for carrying out time trajectory analysis on the attribute data to obtain a time schedule corresponding to the user;

the acquisition subunit is used for acquiring the purchase time in the historical investment product purchase information;

and the determining subunit is used for determining the information recommendation time period based on the time table and the purchase time.

Referring to fig. 3, an embodiment of the present application further provides a computer device, where the computer device may be a server, and an internal structure of the computer device may be as shown in fig. 3. The computer device comprises a processor, a memory, a network interface, a display screen, an input device and a database which are connected through a system bus. Wherein the computer device is designed with a processor for providing computing and control capabilities. The memory of the computer device comprises a storage medium and an internal memory. The storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and computer programs in the storage medium to run. The database of the computer equipment is used for storing answer data, a target risk evaluation grade, major asset allocation suggestion information, an investment capacity value, a matching degree value and a target recommended investment product. The network interface of the computer device is used for communicating with an external terminal through a network connection. The display screen of the computer equipment is an indispensable graphic output equipment in the computer and is used for converting digital signals into optical signals so that characters and graphics are displayed on the screen of the display screen. The input device of the computer equipment is the main device for information exchange between the computer and the user or other equipment, and is used for transmitting data, instructions, some mark information and the like to the computer. The computer program is executed by a processor to implement an information recommendation method.

The processor executes the information recommendation method and comprises the following steps:

acquiring answer data of a preset questionnaire submitted by a user;

acquiring account information and investment product data of the user, and generating an investment capacity numerical value of the user based on the account information and the investment product data;

extracting product data characteristics of the investment product data, calculating matching degree values of the product data characteristics and each second recommended investment product, and screening out target recommended investment products of which the matching degree values are larger than a preset matching degree threshold value from all the second recommended investment products;

It will be understood by those skilled in the art that the structure shown in fig. 3 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the apparatus and the computer device to which the present application is applied.

An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an information recommendation method, and specifically:

acquiring answer data of a preset questionnaire submitted by a user;

generating major asset configuration suggestion information corresponding to the user based on the target risk evaluation grade, the answer data and preset major asset market prediction data;

pushing investment recommendation information to the user; wherein the investment recommendation information comprises the broad category asset allocation recommendation information and the target recommended investment product.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. Any reference to memory, storage, database or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, apparatus, article or method that comprises the element.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.

Claims

1. An information recommendation method, comprising:

acquiring answer data of a preset questionnaire submitted by a user;

acquiring standard purchase amount values of all the first recommended investment products, and screening out second recommended investment products from all the first recommended investment products based on the investment capacity values and the standard purchase amount values;

2. The information recommendation method according to claim 1, wherein the questionnaire comprises evaluation question data of multiple dimensions, each dimension comprises at least one choice, each choice comprises at least two options, and each option corresponds to a score; the step of inputting the answer data into a preset risk evaluation model to obtain a target risk evaluation grade corresponding to the user includes:

3. The information recommendation method according to claim 1, wherein the step of generating the major asset allocation recommendation information corresponding to the user based on the target risk evaluation level, the answer data and preset major asset market prediction data comprises:

calling a preset prediction model;

performing income prediction processing and risk prediction processing on each large asset through the prediction model to obtain a corresponding prediction result;

extracting investment deadline information of the user from the answer data;

4. The information recommendation method according to claim 1, wherein the step of determining a first recommended investment product corresponding to the target risk assessment level based on the target risk assessment level comprises:

obtaining a target risk type corresponding to the target risk evaluation grade;

obtaining evaluation data of each of the first investment products;

calculating a product score for each of said first investment products based upon said valuation data;

sorting the product scores according to the numerical order from large to small to obtain corresponding sorting results;

5. The information recommendation method according to claim 1, wherein said step of generating an investment ability value for said user based on said account information and said investment product data comprises:

extracting account balance information and the holding quantity of investment products from the account information based on a preset first rule expression; and (c) a second step of,

6. The information recommendation method according to claim 1, wherein the step of pushing investment recommendation information to the user comprises:

7. The information recommendation method according to claim 6, wherein the step of determining an information recommendation time period corresponding to the user based on a preset user attribute library and the historical investment product purchase information comprises:

analyzing the time trajectory of the attribute data to obtain a time table corresponding to the user;

8. An information recommendation apparatus, comprising:

the determining module is used for determining a first recommended investment product corresponding to the target risk evaluation grade based on the target risk evaluation grade;

the pushing module is used for pushing investment recommendation information to the user; wherein the investment recommendation information comprises the broad category asset allocation recommendation information and the target recommended investment product.

9. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.