Financial virtual resource recommendation method and device, computer equipment and storage medium

Abstract

The invention relates to a financial virtual resource recommendation method and device, computer equipment, a storage medium and a computer program product, and can be applied to the field of financial science and technology or other related fields. The method comprises the steps of obtaining virtual resource information of candidate resources, risk assessment information of a to-be-recommended user, user information and user behavior information; determining a corresponding target user group and a virtual resource recommendation index corresponding to the target user group according to the risk evaluation information, the user information and the user behavior information; obtaining virtual resource evaluation information under different virtual resource recommendation indexes by using the virtual resource information; and finally, determining a corresponding target virtual resource according to the virtual resource evaluation information, and sending the target virtual resource to a user terminal for recommendation. According to the method, the target user group and the corresponding target virtual resource are respectively determined by obtaining the resource information and the user related information, personalized virtual resource recommendation is performed, and the user experience and the service satisfaction degree are improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology or other related fields, and in particular to a method for recommending financial virtual resources. Background Art

[0002] With the development of financial technology, virtual resource recommendation technology has emerged. Currently, when mobile banking recommends products to potential users, it usually makes recommendations based on the user's past purchase and browsing behavior, or recommends products that are unanimously recognized by the public, and does not start from the user's essential needs.

[0003] Traditional virtual resource recommendation technology only recommends products based on the user's superficial behavioral information or the behavior information of groups such as the general public.

[0004] However, the current virtual resource recommendation methods cannot truly meet user needs. Summary of the invention

[0005] Based on this, it is necessary to provide a financial virtual resource recommendation method, device, computer equipment, computer-readable storage medium and computer program product that can truly meet user needs in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for recommending financial virtual resources. The method comprises:

[0007] Acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories;

[0008] Based on the risk assessment information, user information and user behavior information, determine the target user group category corresponding to the user to be recommended, and obtain the virtual resource recommendation index corresponding to the target user group category;

[0009] Using the virtual resource information, obtaining virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource;

[0010] According to the virtual resource evaluation information, a target virtual resource is obtained from the candidate virtual resources, and the target virtual resource is recommended to a user terminal of a user to be recommended for display.

[0011] In one embodiment, based on the risk assessment information, user information, and user behavior information, determining the target user group category corresponding to the user to be recommended includes:

[0012] Determine a first degree of association between the user to be recommended and each user group category according to the risk assessment information, determine a second degree of association between the user to be recommended and each user group category according to the user information, and determine a third degree of association between the user to be recommended and each user group category according to the user behavior information;

[0013] Obtaining the association degree weights corresponding to the first association degree, the second association degree, and the third association degree respectively, and performing weighted processing on the first association degree, the second association degree, and the third association degree using the association degree weights to obtain the total association degree between the user to be recommended and each user group category;

[0014] The user group category whose total correlation degree meets the preset conditions is used as the target user group category corresponding to the to-be-recommended user.

[0015] In one of the embodiments, each user group category is pre-set with corresponding associated risk assessment information;

[0016] Determining the first degree of association between the recommended user and each user group category according to the risk assessment information includes:

[0017] When the risk assessment information of the user to be recommended includes associated risk assessment information, obtaining the user group category corresponding to the included associated risk assessment information;

[0018] The first correlation degree of the user group category corresponding to the included associated risk assessment information is set to a first preset value, and the first correlation degree of the remaining user group categories is set to a second preset value; the first preset value is greater than the second preset value.

[0019] In one of the embodiments, each user group category is pre-set with corresponding associated user information;

[0020] Determining the second correlation degree between the to-be-recommended user and each user group category according to the user information includes:

[0021] When the user information of the user to be recommended includes associated user information, obtaining the user group category corresponding to the associated user information;

[0022] The second association degree of the user group category corresponding to the included associated user information is set to a first preset value, and the second association degrees of the remaining user group categories are set to a second preset value; the first preset value is greater than the second preset value.

[0023] In one embodiment, determining the third degree of association between the recommended user and each user group category according to the user behavior information includes:

[0024] Obtain the number of clicks on different user category cards by the recommended users; different user category cards are used to display different user group categories;

[0025] According to the click volume information of each user category card, the third correlation degree between the recommended user and each user group category is obtained; wherein the third correlation degree of each user group category is positively correlated with the click volume of the corresponding user category card.

[0026] In one embodiment, when the virtual resource recommendation indicator is the first indicator, the virtual resource information is used to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator, including:

[0027] According to the virtual resource information, obtain the resource growth rate information corresponding to each candidate virtual resource;

[0028] According to the growth rate information of each resource, virtual resource evaluation information corresponding to the first indicator of each candidate virtual resource is obtained.

[0029] In one embodiment, when the virtual resource recommendation indicator is the second indicator, the virtual resource information is used to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator, including:

[0030] According to the virtual resource information, the risk resistance capability information corresponding to each candidate virtual resource is obtained;

[0031] According to each risk resistance capability information, virtual resource evaluation information corresponding to the second indicator of each candidate virtual resource is obtained.

[0032] In one embodiment, when the virtual resource recommendation indicator is the third indicator, the virtual resource information is used to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator, including:

[0033] According to the virtual resource information, obtain resource conversion rate information corresponding to each candidate virtual resource;

[0034] According to the conversion rate information of each resource, virtual resource evaluation information corresponding to the third indicator of each candidate virtual resource is obtained.

[0035] In one embodiment, when the virtual resource recommendation indicator is the fourth indicator, the virtual resource information is used to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator, including:

[0036] According to the virtual resource information, obtain the resource proliferation success rate information corresponding to each candidate virtual resource;

[0037] Based on the information on the success rate of proliferation of each resource, virtual resource evaluation information corresponding to the fourth indicator of each candidate virtual resource is obtained.

[0038] In an exemplary embodiment, obtaining a target virtual resource from candidate virtual resources according to virtual resource evaluation information includes:

[0039] Obtain risk assessment rating information of the user to be recommended, as well as virtual resource acquisition expectation information of the user to be recommended;

[0040] Based on the risk assessment rating information and the expected information of virtual resource acquisition, the target type of virtual resources are acquired from the candidate virtual resources;

[0041] According to the virtual resource evaluation information of each target type virtual resource, a target virtual resource is acquired from the target type virtual resources.

[0042] In a second aspect, the present application also provides a financial virtual resource recommendation device. The device comprises:

[0043] An information acquisition module is used to acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories;

[0044] A group category determination module is used to determine the target user group category corresponding to the recommended user based on risk assessment information, user information and user behavior information, and obtain virtual resource recommendation indicators corresponding to the target user group category;

[0045] An evaluation information determination module, used to obtain virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource by using the virtual resource information;

[0046] The target virtual resource determination module is used to obtain the target virtual resource from the candidate virtual resources according to the virtual resource evaluation information, and recommend the target virtual resource to the user terminal of the user to be recommended for display.

[0047] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0048] Acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories;

[0049] Based on the risk assessment information, user information and user behavior information, determine the target user group category corresponding to the user to be recommended, and obtain the virtual resource recommendation index corresponding to the target user group category;

[0050] Using the virtual resource information, obtaining virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource;

[0051] According to the virtual resource evaluation information, a target virtual resource is obtained from the candidate virtual resources, and the target virtual resource is recommended to a user terminal of a user to be recommended for display.

[0052] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0053] Acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories;

[0054] Based on the risk assessment information, user information and user behavior information, determine the target user group category corresponding to the user to be recommended, and obtain the virtual resource recommendation index corresponding to the target user group category;

[0055] Using the virtual resource information, obtaining virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource;

[0056] According to the virtual resource evaluation information, a target virtual resource is obtained from the candidate virtual resources, and the target virtual resource is recommended to a user terminal of a user to be recommended for display.

[0057] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0058] Acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories;

[0059] Based on the risk assessment information, user information and user behavior information, determine the target user group category corresponding to the user to be recommended, and obtain the virtual resource recommendation index corresponding to the target user group category;

[0060] Using the virtual resource information, obtaining virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource;

[0061] According to the virtual resource evaluation information, a target virtual resource is obtained from the candidate virtual resources, and the target virtual resource is recommended to a user terminal of a user to be recommended for display.

[0062] The above-mentioned financial virtual resource recommendation method, device, computer equipment, storage medium and computer program product obtain virtual resource information of candidate virtual resources and risk assessment of the user to be recommended, user, and user behavior information for different user category cards; based on the above information, determine the target user group category corresponding to the user to be recommended, and obtain the corresponding virtual resource recommendation index; use the virtual resource information to obtain evaluation information of the corresponding virtual resource recommendation index; determine the target virtual resource based on the evaluation information, and recommend the target virtual resource to the user terminal of the user to be recommended for display. By obtaining user-related information of multiple dimensions, the corresponding customer group information is determined, different customer groups correspond to different virtual resource recommendation indicators, and the corresponding target resource is determined based on the information after evaluation, which greatly improves the adaptability between users and virtual resources, thereby improving the accuracy of recommendations and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 An application environment diagram of a financial virtual resource recommendation method in one embodiment;

[0065] Figure 2 A flowchart of a financial virtual resource recommendation method in one embodiment;

[0066] Figure 3 A schematic diagram of a process for determining a target user group category in one embodiment;

[0067] Figure 4 A detailed flowchart of a financial virtual resource recommendation method in another embodiment;

[0068] Figure 5 It is a general scheme diagram of a financial virtual resource recommendation method in one embodiment;

[0069] Figure 6 is a structural block diagram of a financial virtual resource recommendation device in one embodiment;

[0070] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0072] The financial virtual resource recommendation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The terminal 102 exchanges information with the user, obtains the risk assessment of the user to be recommended, user information and user behavior information, determines the corresponding matching customer group based on the aforementioned information, and determines the corresponding recommended virtual resources to recommend to the user. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc.

[0073] In one embodiment, Figure 2 As shown, a financial virtual resource recommendation method is provided, and the method is applied to Figure 1 The terminal 102 in the example is used for explanation, and the steps include the following steps S201 to S204.

[0074] in:

[0075] Step S201, obtaining virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories.

[0076] Among them, virtual resource information can be understood as some non-physical products in the bank, such as equity funds or fixed income funds; risk assessment information can be understood as the results input by users in risk assessment, and the risk assessment input items are disclosed in this application but are not limited to investment goals, income sources, annual disposable household income, age range and investment experience, etc.; user information is disclosed in this application but is not limited to the information originally stored by the user in the bank; user behavior information for different user category cards is disclosed in this application but is not limited to the user's click rate for different user category cards; different user group categories can be understood as a collection of users who are similar in some aspects, which are disclosed in this application but are not limited to the first user group, the second user group, the third user group, the fourth user group and the fifth user group.

[0077] Optionally, the user performs risk assessment on the terminal 102, and the terminal 102 retains the result of the risk assessment of the user, and then obtains the previously stored user information, and simultaneously obtains the click rate information of the user for different user category cards last month.

[0078] Step S202: determining the target user group category corresponding to the to-be-recommended user based on the risk assessment information, the user information and the user behavior information, and obtaining the virtual resource recommendation index corresponding to the target user group category.

[0079] Among them, the virtual resource recommendation index can be understood as an index for evaluating and selecting appropriate virtual resources for recommendation based on user needs and preferences, as well as the attributes and characteristics of virtual resources, which is disclosed in this application but not limited to resource conversion rate, resource growth rate, risk resistance information and resource proliferation rate. The correspondence between the target user group category and the virtual resource recommendation index is disclosed in this application but not limited, as shown in the following table.

[0080] User group categories Recommended indicators for corresponding virtual resources The first user group Resource conversion rate Second user group Resource conversion rate The third user group Resource growth rate The fourth user group Risk resistance information Fifth user group Resource growth rate

[0081] For example, a corresponding user group is determined based on the risk assessment result, user information and user behavior information, and a virtual resource recommendation index of a target user group category is determined according to a corresponding relationship between the group and the virtual resource recommendation index.

[0082] Step S203: using the virtual resource information, obtain virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource.

[0083] Among them, the virtual resource evaluation information can be understood as a parameter for measuring the performance of a virtual resource under different virtual resource recommended indicators; in this application, it is disclosed but not limited to calculating the percentage of each type of virtual resource surpassing the same type of virtual resources in the same type, which is used as a measure of the superiority of the performance of the virtual resource under this indicator, that is, the larger the value, the better the performance of this indicator.

[0084] Optionally, scores under different virtual resource recommendation indicators are calculated according to the information of each virtual resource, and virtual resource recommendation lists under different recommendation standards are made according to the scores, so as to finally obtain virtual resource evaluation information.

[0085] Step S204: acquiring a target virtual resource from candidate virtual resources according to the virtual resource evaluation information, and recommending the target virtual resource to a user terminal of a user to be recommended for display.

[0086] For example, the terminal 102 selects a target virtual resource from the candidate virtual resources according to the previously obtained virtual resource recommendation list, and displays the selected target virtual resource on the terminal for selection by the user.

[0087] In the above financial virtual resource recommendation method, the terminal 102 obtains the virtual resource information of the candidate resources, the risk assessment information of the user to be recommended, the user information and the user behavior information respectively; then determines the corresponding target user group and the virtual resource recommendation index corresponding to the target user group according to the risk assessment information, the user information and the user behavior information; obtains the virtual resource evaluation information under different virtual resource recommendation indexes using the virtual resource information; finally determines the corresponding target virtual resource according to the virtual resource evaluation information, and sends it to the user terminal for recommendation. By obtaining resource information and user-related information, the target user group and the corresponding target virtual resource are determined respectively, matching recommendations are made to the user, and personalized virtual resource recommendations are made, thereby improving the user experience and satisfaction with the service.

[0088] In one embodiment, Figure 3 As shown, based on the risk assessment information, user information and user behavior information, determining the target user group category corresponding to the recommended user includes steps S301 to S303:

[0089] Step S301, determining a first degree of association between the user to be recommended and each user group category based on risk assessment information, determining a second degree of association between the user to be recommended and each user group category based on user information, and determining a third degree of association between the user to be recommended and each user group category based on user behavior information.

[0090] Among them, the risk assessment information can be understood as a questionnaire filled out by the user in association with the user group category and investment scenario. The information filled out in the questionnaire is used as the risk assessment information, which is disclosed in this application but not limited to:

[0091] (1) Virtual resource acquisition target: first virtual resource acquisition target; second virtual resource acquisition target; third virtual resource acquisition target.

[0092] (2) Sources of asset acquisition: the first source of asset acquisition; the second source of asset acquisition; the third source of asset acquisition; the fourth source of asset acquisition; the fifth source of asset acquisition.

[0093] (3) Your family's annual disposable asset acquisition is: the first asset acquisition range; the second asset acquisition range; the third asset acquisition range; the fourth asset acquisition range; the fifth asset acquisition range.

[0094] (4) Age range: first age range; second age range; third age range; fourth age range; fifth age range; sixth age range.

[0095] (5) Your experience in acquiring virtual resources can be described as: zero experience; first experience; second experience; third experience.

[0096] The first degree of association can be understood as relevant information that simultaneously establishes the connection between risk assessment information and user group category information. The second degree of association and the third degree of association can be obtained in the same way.

[0097] For example, the terminal 102 determines the first correlation degree, the second correlation degree and the third correlation degree with each user group category according to the risk assessment information, the user information and the user behavior information.

[0098] Step S302, obtaining the association degree weights corresponding to the first association degree, the second association degree and the third association degree respectively, and weighting the first association degree, the second association degree and the third association degree using the association degree weights to obtain the total association degree between the user to be recommended and each user group category.

[0099] Among them, the association degree weights corresponding to the first association degree, the second association degree and the third association degree can be understood as the weights assigned to the importance of determining the user group category based on each association degree before determining the total association degree, and the appropriate weights are assigned according to the degree of influence on determining the target user group category, among which the third association degree determined by the user behavior information has the greatest influence, so the weight obtained by the third association degree is greater than the first association degree and the second association degree at the same time. The weighted calculation equation disclosed in this application but not limited to is:

[0100] V=20%*X+20%*Y+60%*Z

[0101] Among them, V represents the total correlation degree, X represents the first correlation degree, 20% represents the weight corresponding to the first correlation degree, Y represents the second correlation degree, 20% represents the weight corresponding to the second correlation degree, Z represents the third correlation degree, and 60% represents the weight corresponding to the third correlation degree.

[0102] Optionally, the terminal 102 obtains weights corresponding to the first, second and third association degrees respectively, and brings them into corresponding calculation formulas to perform weighted calculations, and finally obtains a total association degree.

[0103] Step S303: taking the user group category whose total correlation degree meets the preset condition as the target user group category corresponding to the to-be-recommended user.

[0104] Among them, the preset condition can be understood as the total correlation degree corresponding to different user group categories, and the larger the V value, the more it fits the user group category to which the current recommended user belongs.

[0105] For example, the terminal 102 determines the target user group category corresponding to the to-be-recommended user according to the total association degree V.

[0106] In this embodiment, the first, second and third association degrees with each user group category are determined respectively through risk assessment information, user information and user behavior information, and the weights corresponding to the association degrees are further obtained, and the total association degree is obtained by weighted calculation. The target user group category is finally determined based on the total association degree, which can comprehensively consider the risk assessment information, user information and user behavior information, as well as the association degree and importance between them and the user group category to determine the target user group category to which the user belongs. The total association degree obtained by weighted calculation can improve the accuracy and reliability of the target user group category.

[0107] In one of the embodiments, each user group category is pre-set with corresponding associated risk assessment information; the first degree of association between the user to be recommended and each user group category is determined based on the risk assessment information, including: when the risk assessment information of the user to be recommended contains associated risk assessment information, obtaining the user group category corresponding to the contained associated risk assessment information; setting the first degree of association of the user group category corresponding to the contained associated risk assessment information to a first preset value, and setting the first degree of association of the remaining user group categories to a second preset value; the first preset value is greater than the second preset value.

[0108] Among them, the associated risk assessment information can be understood as information related to the user group category obtained by the user after filling out the risk assessment questionnaire. The first preset value is disclosed in this application but is not limited to 100, and the second preset value is disclosed in this application but is not limited to 0. The associated risk assessment information, the corresponding user group category and the corresponding preset value in this application are disclosed but not limited as shown in the following table:

[0109]

[0110] For example, the terminal 102 determines that the risk assessment information contains associated risk assessment information, determines the user group category corresponding to the associated risk assessment information, and sets the first association degree of the user group category corresponding to the associated risk assessment information in the risk assessment information to a first preset value, and sets the first association degree of the remaining user group categories to a second preset value, and the first preset value is greater than the second preset value. By being able to obtain from the risk assessment information that the first association degree of the corresponding user group category is set to a preset value greater than the first association degree of the remaining user group categories, and distinguishing the association degree between the recommended user and different user group categories, the group category described by the user can be judged more accurately, and personalized risk management and decision-making can be further performed according to the user's specific needs and risk conditions.

[0111] In one exemplary embodiment, each user group category is pre-set with corresponding associated user information; determining the second degree of association between the user to be recommended and each user group category based on the user information includes: when the user information of the user to be recommended includes associated user information, obtaining the user group category corresponding to the included associated user information; setting the second degree of association of the user group category corresponding to the included associated user information to a first preset value, and setting the second degree of association of the remaining user group categories to a second preset value; the first preset value is greater than the second preset value.

[0112] The associated user information may be understood as stored user information that can determine the category of the user group, which is disclosed in this application but is not limited to the following table:

[0113] Stored user information Corresponding user group category Corresponding Y score First Information The first user group 100 Second information Second user group 100 Third Information The third user group 100 Fourth Information The fourth user group 100 Fifth Message Fifth user group 100

[0114] Optionally, the terminal 102 determines whether the user information to be recommended contains related user information, determines the user group category corresponding to the related user information, and sets the second association degree of the user group category to be greater than the preset value of the other user group categories. The user group category of the user to be recommended is determined by the pre-stored related user information, and then the corresponding second association degree is determined. By obtaining user information, the user group category to which the user may belong is further accurately captured, and more personalized resource recommendations are achieved, which is conducive to improving user experience.

[0115] In one of the embodiments, the third degree of association between the user to be recommended and each user group category is determined based on user behavior information, including: obtaining the number of clicks on different user category cards by the user to be recommended; different user category cards are used to display different user group categories; based on the click volume information of each user category card, the third degree of association between the user to be recommended and each user group category is obtained; wherein the third degree of association of each user group category is positively correlated with the number of clicks on the corresponding user category card.

[0116] Among them, the number of clicks is disclosed in this application but is not limited to the user's mobile banking display of the five major customer groups and the corresponding investment scenario cards. If the user is interested in a customer group and the corresponding scenario card, clicking on the picture will access the next recommended fund content, and the number of times the user clicks on the picture is regarded as the number of clicks.

[0117] Optionally, the present application discloses but is not limited to the positive correlation between the number of clicks and the third degree of correlation. For example, the number of clicks on pictures of the five major customer groups by users last month is taken. For those with clicks greater than 0, they are sorted in descending order according to the number of clicks. The Z score of the ranking 1 is 100 points, the Z score of the ranking 2 is 80 points, the Z score of the ranking 3 is 60 points, the Z score of the ranking 4 is 40 points, and the Z score of the ranking 5 is 20 points.

[0118] In an exemplary embodiment, when the virtual resource recommendation indicator is the first indicator, virtual resource information is used to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator, including: obtaining resource growth rate information corresponding to each candidate virtual resource based on the virtual resource information; and obtaining virtual resource evaluation information of each candidate virtual resource corresponding to the first indicator based on each resource growth rate information.

[0119] According to the virtual resource information, the risk resistance capability information corresponding to each candidate virtual resource is obtained; according to each risk resistance capability information, the virtual resource evaluation information corresponding to the second indicator of each candidate virtual resource is obtained.

[0120] According to the virtual resource information, resource conversion rate information corresponding to each candidate virtual resource is obtained; according to each resource conversion rate information, virtual resource evaluation information corresponding to the third indicator of each candidate virtual resource is obtained.

[0121] According to the virtual resource information, the resource proliferation success rate information corresponding to each candidate virtual resource is obtained; according to the resource proliferation success rate information, the virtual resource evaluation information corresponding to the fourth indicator of each candidate virtual resource is obtained.

[0122] Among them, virtual resources are disclosed in this application but are not limited to the secondary classification of virtual resources. For type 1 virtual resources, virtual resources are divided into: type I, type II, type III, type IV, and type 2 virtual resources. Virtual resources are divided into: type I, type II, type III, type IV, and type V. The above virtual resource classification is mainly divided according to the type of virtual resources. Under the above secondary classification, there are a total of 9 types of virtual resources.

[0123] For the above 9 types of virtual resources, some indicators are proposed to measure the resource growth rate, risk resistance information, resource conversion rate and resource proliferation rate of each virtual resource. Since the resource growth rate, risk resistance information, resource conversion rate and resource proliferation rate of virtual resources have comparative value in the same type of virtual resources, in calculating these scoring indicators, the percentage of each virtual resource exceeding the same type in the same type of virtual resources (secondary classification) is mainly calculated as the score value of the corresponding indicator of each virtual resource. The scoring table is updated daily, assuming that the nearest trading day of the calculation day is T day, and the calculation period is the nearest year from T day.

[0124] For example, the present application discloses but is not limited to the following methods for obtaining corresponding virtual resource evaluation information, such as:

[0125] (1) Calculate resource growth rate Y(R)

[0126] Profitability is calculated using the interval rate of return R (RETURN). Calculate the growth rate of the net value of each virtual resource in the past year. Assume that the net value of virtual resources is represented by P, PT represents the net value of virtual resources on the most recent trading day T before the calculation date, and P (T-1Y) represents the net value of virtual resources on the trading day one year before T.

[0127] R=(PT-P(T-1Y)) / P(T-1Y)

[0128] For each virtual resource under each secondary classification, calculate the net value growth rate R of each virtual resource in the category in the past year, and then rank the virtual resources under each secondary category, and rank them in order from large to small according to the net value growth rate of the virtual resources in the secondary classification. Assuming that the ranking of the virtual resource is represented by i, the total statistical number of the secondary classification is n, calculate the percentage of the virtual resource's profitability exceeding that of the same category Y(R) = (ni) / (n-1)*100. (The higher the ranking, the stronger this ability)

[0129] This can yield a score of each virtual resource outperforming its peers, and Y(R) is used to measure the resource growth rate of each virtual resource.

[0130] (2) Calculate risk resistance information Y(D)

[0131] Risk resistance information is measured using the maximum drawdown. The maximum drawdown is the decline in the net value of virtual resources when it reaches the lowest point in the selected period, indicating the maximum loss of virtual resources in a period of time. The smaller the maximum drawdown indicator, the better. The larger the maximum drawdown, the worse the risk resistance. The calculated value of the maximum drawdown (DOWNSIDE) is represented by D.

[0132] Assume that PMAX is the maximum net value from T-1Y to T, the date of the maximum net value is M, and then calculate the minimum net value from M to T, which is represented by PMIN(M).

[0133] PMIN is the minimum net value from T-1Y to T day, and the date of the minimum net value is N day

[0134] Then calculate the maximum net value from T-1Y to N days, represented by PMAX(N)

[0135] Maximum Drawdown

[0136] D=MAX[(PMAX-PMIN(M)) / PMIN(M)*100%,(PMAX(N)-PMIN) / PMIN*100%], MAX means taking the maximum value of the two values, which is the maximum drawdown of a certain virtual resource.

[0137] For each virtual resource under each secondary classification, calculate the maximum drawdown D of each virtual resource under the category, and then rank the virtual resources under the secondary category. According to the maximum drawdown, the virtual resources are ranked in order from small to large in the secondary classification. The smaller the maximum drawdown, the higher the ranking of the virtual resource. Assuming that the ranking of the virtual resource is represented by i, the total statistical number of the secondary classification is n, calculate the percentage of the virtual resource exceeding the same category in terms of risk resistance Y(D) = (ni) / (n-1)*100. Thus, the percentage score of each virtual resource exceeding the same category can be obtained, and Y(D) is used to measure the risk resistance of each virtual resource.

[0138] (3) Calculate resource conversion rate Y(S)

[0139] The resource conversion rate is measured using the Sharpe Ratio.

[0140] Sharpe ratio S = (average return in the interval E(RP) - risk-free rate of return E(Rf)) / standard deviation σ, which indicates how much excess return can be expected for each unit of risk. The larger the Sharpe ratio, the better. The larger the Sharpe ratio, the higher the cost-effectiveness of virtual resources.

[0141] When calculating the average rate of return E(RP) within the interval, the average unit net value of all trading days in the most recent year before day T is taken, where d represents the number of trading days in the most recent year before day T, and the virtual resource net value is simply the sum of the unit net value of all trading days. Pk is the unit net value of virtual resources on the kth trading day.

[0142]

[0143] E(Rf) is the one-year government bond interest rate

[0144] σ is the standard deviation of the unit net value of all trading days in the past year on day T

[0145]

[0146] For each virtual resource under each secondary classification, calculate the Sharpe ratio S of each virtual resource under the category, and then rank the virtual resources under the secondary category. According to the Sharpe ratio, the virtual resources are ranked in descending order in the secondary classification of virtual resources. The larger the Sharpe ratio, the higher the ranking of the virtual resource. Assuming that the ranking of the virtual resource is represented by i, the total statistical number of the secondary classification is n, calculate the percentage of the virtual resource that exceeds the same category in terms of investment cost performance Y(S) = (ni) / (n-1)*100. Thus, the percentage score of each virtual resource that exceeds the same category can be obtained, and Y(S) is used to measure the investment cost performance score of each virtual resource.

[0147] (4) Calculate the resource growth rate Y(E)

[0148] Resource multiplication rate is measured using resource win rate.

[0149] Get the daily increase and decrease data of all trading days of all virtual resources in the last 12 months (left closed and right open). If the amount of data within this period is A, the number of daily increase and decrease greater than 0 is B

[0150] Resource winning rate E = B / A. The higher the resource winning rate, the better the resource experience and the higher the virtual resource ranking.

[0151] For the virtual resources under each secondary classification, calculate the resource win rate of each virtual resource under the category, and then rank the virtual resources under the secondary category. Arrange the order of the resource win rate from large to small in the virtual resource secondary classification. The larger the resource win rate value, the higher the virtual resource ranking. Assuming that the virtual resource ranking is represented by i, the total statistical number of the secondary classification is n, calculate the percentage of the virtual resource surpassing the same category in resource experience Y(E) = (ni) / (n-1)*100. In this way, the percentage score of each virtual resource surpassing the same category can be obtained. Y(E) is used to measure the resource experience score of each virtual resource. (The higher the ranking, the stronger this ability)

[0152] By calculating the percentage of different virtual resources that exceed similar resources under the corresponding recommendation indicators after secondary classification, the strength of this resource under this indicator is determined. The higher the ranking, the stronger the ability. Personalized recommendations can be made based on the different indicators corresponding to the user, effectively improving the efficiency and quality of recommendations, thereby improving the user's service experience.

[0153] In one of the embodiments, target virtual resources are obtained from candidate virtual resources based on virtual resource evaluation information, including: obtaining risk assessment rating information matching the user to be recommended, and virtual resource acquisition expectation information of the user to be recommended; obtaining target type virtual resources from candidate virtual resources based on the risk assessment rating information and virtual resource acquisition expectation information; obtaining target virtual resources from target type virtual resources based on virtual resource evaluation information of each target type virtual resource.

[0154] Among them, the risk assessment rating information is disclosed in this application but is not limited to the user's risk assessment level, which is divided into five levels from R1 to R5; the risk level of virtual resources is also five levels from R1 to R5, and the virtual resource acquisition expectation information is disclosed in this application but is not limited to the user's virtual resource acquisition expectation information: the first expectation information corresponds to type one virtual resource product, and the second expectation information corresponds to type two virtual resource product.

[0155] For example, the target virtual resource types determined based on the risk assessment rating information and the expected information are disclosed but not limited in this application, as shown in the following table:

[0156]

[0157]

[0158] Finally, the target virtual resource is selected from the target virtual resource type based on the evaluation information of each virtual resource. By jointly considering multiple influencing factors, the target virtual resource range is continuously narrowed, and the recommended virtual resource is finally determined, which improves the accuracy of resource recommendation and thus improves user satisfaction and service experience.

[0159] In an exemplary embodiment, Figure 4 As shown, the detailed process of implementing a financial virtual resource recommendation method includes steps S401 to S413, which are as follows:

[0160] The overall scheme of the above method is as follows Figure 5 As shown, it mainly includes four parts: analyzing customer groups and recommending investment scenarios, determining fund recommendation indicators, calculating fund scoring indicators and intelligently recommending fund products.

[0161] Step S401, respectively obtain virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of users to be recommended, and user behavior information of users to be recommended for different user category cards; different user category cards correspond to different user group categories.

[0162] Step S402, when the risk assessment information of the user to be recommended contains associated risk assessment information, obtain the user group category corresponding to the associated risk assessment information; set the first correlation degree of the user group category corresponding to the associated risk assessment information to a first preset value, and set the first correlation degree of the remaining user group categories to a second preset value; the first preset value is greater than the second preset value.

[0163] Step S403, when the user information of the user to be recommended includes associated user information, obtain the user group category corresponding to the associated user information; set the second association degree of the user group category corresponding to the associated user information to a first preset value, and set the second association degree of the remaining user group categories to a second preset value; the first preset value is greater than the second preset value.

[0164] Step S404, obtaining the number of clicks on different user category cards by the user to be recommended; different user category cards are used to display different user group categories; based on the click information of each user category card, obtaining the third degree of association between the user to be recommended and each user group category; wherein the third degree of association of each user group category is positively correlated with the number of clicks on the corresponding user category card.

[0165] Step S405, obtaining the association degree weights corresponding to the first association degree, the second association degree and the third association degree respectively, and weighting the first association degree, the second association degree and the third association degree using the association degree weights to obtain the total association degree between the user to be recommended and each user group category.

[0166] Step S406 , taking the user group category whose total correlation degree meets the preset condition as the target user group category corresponding to the to-be-recommended user, and obtaining the corresponding virtual resource recommendation index at the same time.

[0167] Step S407: when the virtual resource recommendation indicator is the first virtual resource recommendation indicator, the corresponding resource growth rate information is determined based on the virtual resource information, and the corresponding virtual resource evaluation information is determined according to the resource growth rate information.

[0168] Step S408: When the virtual resource recommendation indicator is the second virtual resource recommendation indicator, the corresponding risk resistance capability information is determined based on the virtual resource information, and the corresponding virtual resource evaluation information is determined according to the risk resistance capability information.

[0169] Step S409: when the virtual resource recommendation indicator is the third virtual resource recommendation indicator, the corresponding resource conversion rate information is determined based on the virtual resource information, and the corresponding virtual resource evaluation information is determined according to the resource conversion rate information.

[0170] Step S410: When the virtual resource recommendation indicator is the fourth virtual resource recommendation indicator, the corresponding resource proliferation rate information is determined based on the virtual resource information, and the corresponding virtual resource evaluation information is determined according to the resource proliferation rate information.

[0171] Step S411, respectively obtaining risk assessment rating information and virtual resource acquisition expectation information of the user to be recommended.

[0172] Step S412: determining the target type of virtual resources based on the risk assessment rating information and the virtual resource acquisition expectation information.

[0173] Step S413: determine the target virtual resource according to the virtual resource evaluation information of each target type of virtual resource and recommend it to the user terminal for display.

[0174] The above disclosed embodiments have the following advantages compared with the prior art:

[0175] 1. By analyzing the user's risk assessment input, the customer information stored by the bank and the customer's interest in the five major user group categories, the user group category to which the user belongs is determined, and then the characteristics of the user to be recommended can be determined based on the common characteristics of this user group category.

[0176] 2. Different user group categories are associated with corresponding investment scenarios. Different investment scenarios have different recommendation indicators. The corresponding target virtual resources are determined based on the virtual resource evaluation information under different indicators.

[0177] 3. Further determine the corresponding target type virtual resources based on the user's expectation information and risk assessment rating information for obtaining virtual resources, and then determine the target virtual resources from the target type virtual resources based on the virtual resource evaluation information. Provide users with personalized recommendations for virtual resources that meet their own characteristics, thereby improving user experience.

[0178] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0179] Based on the same inventive concept, the embodiment of the present application also provides a financial virtual resource recommendation device for implementing the financial virtual resource recommendation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more financial virtual resource recommendation device embodiments provided below can refer to the limitations of the financial virtual resource recommendation method above, and will not be repeated here.

[0180] In one embodiment, Figure 6 As shown, a financial virtual resource recommendation device is provided, comprising: an information acquisition module 601, a group category determination module 602, an evaluation information determination module 603 and a target virtual resource determination module 604, wherein:

[0181] The information acquisition module 601 is used to obtain virtual resource information of candidate virtual resources, risk assessment information of the user to be recommended, user information of the user to be recommended, and user behavior information of the user to be recommended for different user category cards; different user category cards correspond to different user group categories.

[0182] The group category determination module 602 is used to determine the target user group category corresponding to the to-be-recommended user based on the risk assessment information, user information and user behavior information, and obtain the virtual resource recommendation index corresponding to the target user group category.

[0183] The evaluation information determination module 603 is used to obtain virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource by using the virtual resource information.

[0184] The target virtual resource determination module 604 is used to obtain the target virtual resource from the candidate virtual resources according to the virtual resource evaluation information, and recommend the target virtual resource to the user terminal of the user to be recommended for display.

[0185] In one embodiment, the group category determination module 602 further includes a correlation degree acquisition submodule, a weighted calculation submodule and a group category determination submodule, wherein:

[0186] The association degree acquisition submodule is used to determine the first association degree between the user to be recommended and each user group category based on the risk assessment information, determine the second association degree between the user to be recommended and each user group category based on the user information, and determine the third association degree between the user to be recommended and each user group category based on the user behavior information.

[0187] The weighted calculation submodule is used to obtain the association degree weights corresponding to the first association degree, the second association degree and the third association degree respectively, and use the association degree weights to weight the first association degree, the second association degree and the third association degree to obtain the total association degree between the user to be recommended and each user group category.

[0188] The group category determination submodule is used to take the user group category whose total correlation degree meets the preset conditions as the target user group category corresponding to the to-be-recommended user.

[0189] In one exemplary embodiment, the association degree acquisition submodule is specifically used to pre-set corresponding associated risk assessment information in each user group category; each user group category is pre-set with corresponding associated user information.

[0190] When the risk assessment information of the user to be recommended contains associated risk assessment information, obtain the user group category corresponding to the associated risk assessment information; set the first correlation degree of the user group category corresponding to the associated risk assessment information to a first preset value, and set the first correlation degree of the remaining user group categories to a second preset value; the first preset value is greater than the second preset value.

[0191] In the case where user information of the user to be recommended includes associated user information, a user group category corresponding to the associated user information is obtained; a second association degree of the user group category corresponding to the associated user information is set to a first preset value, and a second association degree of the remaining user group categories is set to a second preset value; the first preset value is greater than the second preset value.

[0192] The number of clicks on different user category cards by the user to be recommended is obtained; different user category cards are used to display different user group categories; based on the click information of each user category card, the third degree of association between the user to be recommended and each user group category is obtained; wherein the third degree of association of each user group category is positively correlated with the number of clicks on the corresponding user category card.

[0193] In one embodiment, the evaluation information determination module 603 is specifically used to obtain resource growth rate information corresponding to each candidate virtual resource based on the virtual resource information; and obtain virtual resource evaluation information corresponding to the first indicator of each candidate virtual resource based on the resource growth rate information.

[0194] According to the virtual resource information, the risk resistance capability information corresponding to each candidate virtual resource is obtained; according to each risk resistance capability information, the virtual resource evaluation information corresponding to the second indicator of each candidate virtual resource is obtained.

[0195] According to the virtual resource information, resource conversion rate information corresponding to each candidate virtual resource is obtained; according to each resource conversion rate information, virtual resource evaluation information corresponding to the third indicator of each candidate virtual resource is obtained.

[0196] According to the virtual resource information, the resource proliferation success rate information corresponding to each candidate virtual resource is obtained; according to the resource proliferation success rate information, the virtual resource evaluation information corresponding to the fourth indicator of each candidate virtual resource is obtained.

[0197] In one embodiment, the target virtual resource determination module 604 is specifically used to obtain risk assessment rating information matching the user to be recommended, as well as the virtual resource acquisition expectation information of the user to be recommended; based on the risk assessment rating information and the virtual resource acquisition expectation information, obtain the target type virtual resource from the candidate virtual resources; according to the virtual resource evaluation information of each target type virtual resource, obtain the target virtual resource from the target type virtual resources.

[0198] Each module in the above-mentioned financial virtual resource recommendation device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0199] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a financial virtual resource recommendation method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0200] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0201] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the financial virtual resource recommendation method of the above embodiment when executing the computer program.

[0202] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the financial virtual resource recommendation method of the above embodiment is implemented.

[0203] In one embodiment, a computer program product is provided, including a computer program, which implements the financial virtual resource recommendation method of the above embodiment when executed by a processor.

[0204] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0205] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0206] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0207] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A financial virtual resource recommendation method, characterized in that: The method comprises: Acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of the users to be recommended, and user behavior information of the users to be recommended for different user category cards; the different user category cards correspond to different user group categories; Based on the risk assessment information, the user information and the user behavior information, determining the target user group category corresponding to the to-be-recommended user, and obtaining the virtual resource recommendation index corresponding to the target user group category; Using the virtual resource information, obtaining virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource; According to the virtual resource evaluation information, a target virtual resource is acquired from the candidate virtual resources, and the target virtual resource is recommended to the user terminal of the to-be-recommended user for display.

2. The method according to claim 1, characterized in that The determining, based on the risk assessment information, the user information, and the user behavior information, the target user group category corresponding to the to-be-recommended user includes: Determining a first degree of association between the user to be recommended and each user group category according to the risk assessment information, determining a second degree of association between the user to be recommended and each user group category according to the user information, and determining a third degree of association between the user to be recommended and each user group category according to the user behavior information; Obtaining the association degree weights corresponding to the first association degree, the second association degree, and the third association degree respectively, and performing weighted processing on the first association degree, the second association degree, and the third association degree using the association degree weights to obtain the total association degree between the to-be-recommended user and each user group category; The user group category whose total correlation degree meets the preset conditions is used as the target user group category corresponding to the to-be-recommended user.

3. The method according to claim 2, characterized in that Each user group category is pre-set with corresponding associated risk assessment information; The determining, according to the risk assessment information, a first correlation degree between the to-be-recommended user and each user group category includes: In a case where the risk assessment information of the to-be-recommended user includes the associated risk assessment information, obtaining a user group category corresponding to the associated risk assessment information; The first correlation degree of the user group category corresponding to the associated risk assessment information is set to a first preset value, and the first correlation degree of the remaining user group categories is set to a second preset value; the first preset value is greater than the second preset value.

4. The method according to claim 2, characterized in that: Each user group category is pre-set with corresponding associated user information; The determining, according to the user information, a second degree of association between the to-be-recommended user and each user group category includes: In a case where the user information of the user to be recommended includes the associated user information, obtaining a user group category corresponding to the associated user information; The second association degree of the user group category corresponding to the associated user information is set to a first preset value, and the second association degrees of the remaining user group categories are set to a second preset value; the first preset value is greater than the second preset value.

5. The method according to claim 2, characterized in that: The determining, according to the user behavior information, a third degree of association between the to-be-recommended user and each user group category comprises: Obtaining the number of clicks of the to-be-recommended user on cards of different user categories; the cards of different user categories are used to display categories of different user groups; According to the click volume information of each user category card, the third correlation degree between the recommended user and each user group category is obtained; wherein the third correlation degree of each user group category is positively correlated with the click volume of the corresponding user category card.

6. The method according to claim 1, characterized in that In the case where the virtual resource recommendation indicator is the first indicator, the using of the virtual resource information to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator includes: According to the virtual resource information, obtaining resource growth rate information corresponding to each of the candidate virtual resources; According to the resource growth rate information, virtual resource evaluation information of each candidate virtual resource corresponding to the first indicator is obtained.

7. The method according to claim 1, characterized in that In the case where the virtual resource recommendation indicator is the second indicator, the using of the virtual resource information to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator includes: According to the virtual resource information, obtaining risk resistance capability information corresponding to each of the candidate virtual resources; According to each of the risk resistance capability information, virtual resource evaluation information corresponding to the second indicator of each of the candidate virtual resources is obtained.

8. The method according to claim 1, characterized in that In the case where the virtual resource recommendation indicator is the third indicator, the using of the virtual resource information to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator includes: According to the virtual resource information, obtaining resource conversion rate information corresponding to each of the candidate virtual resources; According to the resource conversion rate information, virtual resource evaluation information of each candidate virtual resource corresponding to the third indicator is obtained.

9. The method according to claim 1, characterized in that: In the case where the virtual resource recommendation indicator is the fourth indicator, the using of the virtual resource information to obtain virtual resource evaluation information of each candidate virtual resource corresponding to the virtual resource recommendation indicator includes: According to the virtual resource information, obtaining resource proliferation success rate information corresponding to each of the candidate virtual resources; The virtual resource evaluation information of each candidate virtual resource corresponding to the fourth indicator is obtained according to the resource proliferation success rate information.

10. The method according to claim 1, characterized in that The step of acquiring a target virtual resource from the candidate virtual resources according to the virtual resource evaluation information includes: Obtain risk assessment rating information matched with the user to be recommended, and virtual resource acquisition expectation information of the user to be recommended; Based on the risk assessment rating information and the virtual resource acquisition expectation information, acquiring a target type virtual resource from the candidate virtual resources; The target virtual resource is acquired from the target type virtual resources according to the virtual resource evaluation information of each target type virtual resource.

11. A financial virtual resource recommendation device, characterized in that: The device comprises: An information acquisition module, used to acquire virtual resource information of candidate virtual resources, risk assessment information of users to be recommended, user information of the users to be recommended, and user behavior information of the users to be recommended for different user category cards; the different user category cards correspond to different user group categories; A group category determination module, used to determine the target user group category corresponding to the to-be-recommended user based on the risk assessment information, user information and user behavior information, and obtain virtual resource recommendation indicators corresponding to the target user group category; An evaluation information determination module, used to obtain virtual resource evaluation information corresponding to the virtual resource recommendation index of each candidate virtual resource by using the virtual resource information; The target virtual resource determination module is used to obtain the target virtual resource from the candidate virtual resources according to the virtual resource evaluation information, and recommend the target virtual resource to the user terminal of the user to be recommended for display.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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