CN115082204A

CN115082204A - Information processing method, computer device and storage medium in credit product recommendation

Info

Publication number: CN115082204A
Application number: CN202210731820.2A
Authority: CN
Inventors: 张鑫
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-06-25
Filing date: 2022-06-25
Publication date: 2022-09-20

Abstract

The application discloses an information processing method, computer equipment and a storage medium in credit product recommendation, wherein the method comprises the following steps: acquiring historical touch time and historical touch times of a user on a target credit product; determining the product demand degree of the user for the target credit product according to the historical touch duration and the historical touch times; acquiring an expected credit interest rate corresponding to a user; adjusting the current credit interest rate of the target credit product according to the product demand degree and the expected credit interest rate; based on the adjusted current credit interest rate, outputting recommendation information for the target credit product. According to the method and the device, the demand degree of the user for the credit product is calculated, the current credit interest rate of the credit product is adjusted according to the demand degree and the expected credit interest rate of the product, so that the setting of the credit interest rate is more in line with the demand of the user and the expectation of the user, the recommendation success rate of the credit product is improved, and the recommendation cost of the credit product is reduced.

Description

Information processing method, computer device and storage medium in credit product recommendation

Technical Field

The application relates to the technical field of credit products, in particular to an information processing method, computer equipment and a storage medium in credit product recommendation.

Background

Most banks recommend credit products to users, most popular or latest products are recommended to all users, but the extensive recommendation mode is not matched with the actual requirements of the users, so that the recommendation success rate of the credit products is low, and the recommendation cost of the credit products is increased.

Disclosure of Invention

The embodiment of the application provides an information processing method, computer equipment and a storage medium in credit product recommendation, and aims to improve the recommendation success rate of credit products.

In one aspect, the present application provides a software testing method, including:

acquiring historical touch time and historical touch times of a user on a target credit product;

determining the product demand degree of the user for the target credit product according to the historical touch duration and the historical touch times;

acquiring an expected credit interest rate corresponding to a user;

adjusting a current credit interest rate of a target credit product according to the product demand degree and the expected credit interest rate;

and outputting the recommendation information of the target credit product based on the adjusted current credit interest rate.

In some embodiments, the step of determining the product desirability of the user for the target credit product based on the historical length of exposure and the historical number of exposures comprises:

determining a first score corresponding to the historical touch duration, wherein the first score is positively correlated with the historical touch duration;

determining a second score corresponding to the historical touch times, wherein the second score is positively correlated with the historical touch times;

and determining the product demand degree of the user for the target credit product according to the first score and the second score.

In some embodiments, the step of obtaining the corresponding expected credit interest rate of the user comprises:

acquiring asset information of a user and historical credit interest rate corresponding to credit products historically purchased by the user;

and determining the expected credit interest rate corresponding to the user according to the asset information and the historical credit interest rate.

In some embodiments, the step of adjusting the current credit interest rate of the target credit product as a function of the product desirability and the expected credit interest rate comprises:

obtaining an interest rate difference between the expected credit interest rate and a current credit interest rate of a target credit product;

when the interest rate difference value is larger than a preset interest rate difference value, determining a first adjusting value corresponding to the interest rate difference value, and obtaining a second adjusting value corresponding to the product demand degree, wherein the first adjusting value is positively correlated with the interest rate difference value, and the second adjusting value is negatively correlated with the product demand degree;

adjusting a current credit interest rate of a target credit product using the first adjustment value and the second adjustment value.

In some embodiments, the step of adjusting the current credit interest rate of the target credit product using the first adjustment value and the second adjustment value includes:

acquiring a credit channel source to which a user belongs, wherein the credit channel source comprises at least one of a webpage, a client, a manual work and a telephone;

determining a total recommendation success rate of a target credit product at the credit channel source;

correcting the first adjusting value according to the total recommendation success rate;

and adjusting the current credit interest rate of the target credit product using the modified first adjustment value and the second adjustment value.

In some embodiments, the step of modifying the first adjustment value according to the total recommendation success rate includes:

acquiring a historical recommendation success rate of recommending credit products to a user;

carrying out weighted summation on the historical recommendation success rate and the total recommendation success rate to obtain a comprehensive recommendation rate;

and correcting the first adjusting value according to the comprehensive recommendation rate.

In some embodiments, the step of outputting the recommendation information for the target credit product based on the adjusted current credit interest rate further comprises:

obtaining the reach duration and the reach times when the target credit product is recommended based on the adjusted current credit interest rate;

updating the product demand degree of the user for the target credit product according to the reach duration and the reach times when the target credit product is recommended based on the adjusted current credit interest rate;

and returning to the step of adjusting the current credit interest rate of the target credit product according to the product demand degree and the expected credit interest rate based on the updated product demand degree.

In some embodiments, the step of obtaining the expected credit interest rate corresponding to the user further comprises:

determining a third score corresponding to the interest rate difference, wherein the third score is positively correlated with the interest rate difference;

determining a fourth score corresponding to the product demand degree, wherein the fourth score is negatively related to the product demand degree;

and determining a recommendation mode of the target credit product according to the sum of the third score and the fourth score, wherein the recommendation mode comprises at least one of a webpage, a client, a manual work and a telephone, and the target credit product is recommended based on the recommendation mode.

In another aspect, an embodiment of the present application provides an information processing apparatus in credit product recommendation, including:

the acquisition module is used for acquiring the historical touch time and the historical touch times of the user on the target credit product;

the determining module is used for determining the product demand degree of the user for the target credit product according to the historical touch duration and the historical touch times; acquiring an expected credit interest rate corresponding to a user;

an adjustment module for adjusting a current credit interest rate of a target credit product according to the product desirability and the expected credit interest rate;

and the output module is used for outputting the recommendation information of the target credit product based on the adjusted current credit interest rate.

In some embodiments, the determining module is specifically configured to:

determining a product demand for the target credit product by the user based on the first score and the second score.

In some embodiments, the determining module is specifically configured to:

In some embodiments, the adjustment module is specifically configured to:

and adjusting the current credit interest rate of the target credit product by adopting the corrected first adjustment value and the second adjustment value.

In some embodiments, the adjustment module is specifically configured to:

and correcting the first adjustment value according to the comprehensive recommendation rate.

In some embodiments, the adjustment module is further to:

In some embodiments, the determining module is specifically configured to:

In another aspect, the present application further provides a computer device, including:

one or more processors;

a memory; and

one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement steps in any of the information processing methods in credit product recommendations.

In another aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform any of the steps in the method for processing information in a credit product recommendation.

The information processing method, the computer equipment and the storage medium in the credit product recommendation provided by the embodiment of the application comprise the following steps: acquiring historical reaching time and historical reaching times of a user for a target credit product; determining the product demand degree of the user for the target credit product according to the historical touch duration and the historical touch times; acquiring an expected credit interest rate corresponding to a user; adjusting the current credit interest rate of the target credit product according to the product demand degree and the expected credit interest rate; based on the adjusted current credit interest rate, outputting recommendation information for the target credit product. According to the method and the device, the demand degree of the user for the credit product is calculated, and the current credit interest rate of the credit product is adjusted according to the demand degree and the expected credit interest rate of the product, so that the setting of the credit interest rate is more in line with the demand of the user and the expectation of the user, the recommendation success rate of the credit product is improved, and the recommendation cost of the credit product is reduced.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram illustrating one embodiment of a method for processing information in a credit product recommendation as provided in an embodiment of the present application;

FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a method for processing information in a credit product recommendation as provided in an embodiment of the present application;

FIG. 3 is a schematic flow chart diagram illustrating a further embodiment of a method for processing information in a credit product recommendation as provided in an embodiment of the present application;

FIG. 4 is a schematic flow chart diagram of another embodiment of the information processing method in credit product recommendation provided in the embodiments of the present application;

FIG. 5 is a block diagram illustrating an embodiment of an information processing apparatus in a credit product recommendation provided by an embodiment of the present application;

fig. 6 is a schematic terminal structure diagram of an embodiment of a computer device provided in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In the description of the present application, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed in a particular orientation, and be operated, and thus should not be considered as limiting the present application. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes are not set forth in detail in order to avoid obscuring the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

It should be noted that, since the method in the embodiment of the present application is executed in a computer device, processing objects of each computer device all exist in the form of data or information, for example, time, which is substantially time information, and it is understood that, in the subsequent embodiments, if size, number, position, and the like are mentioned, corresponding data exist so as to be processed by the computer device, and details are not described herein.

Embodiments of the present application provide an information processing method, a computer device, and a storage medium in credit product recommendation, which are described in detail below.

Referring to FIG. 1, in one embodiment, a method of information processing in credit product recommendation includes:

101. acquiring historical touch time and historical touch times of a user on a target credit product;

in this embodiment, the reach-to-reach means that the product information of the target credit product reaches the user and is known to be contacted by the user, and the reach-to-reach manner may include web page pushing, client pushing, telephone pushing, manual offline pushing, and the like. The reach duration refers to the duration or total duration that the target credit product reaches the user, e.g., the duration of browsing when the user opens the web page of the target credit product, and also, e.g., the duration of a call when the target credit product is recommended to the user over the phone. The number of hits refers to the number of times the target credit product hits the user, e.g., the number of times the user opens a web page for the target credit product, and, again, the number of times the user connects to a phone recommending the target credit product. After the product information of the target credit product is reached to the user, the corresponding reach data may be recorded and stored. By querying the historical reach data of the user for the target credit product, the historical reach duration and the historical reach times of the user for the target credit product may be determined.

102. Determining the product demand degree of the user for the target credit product according to the historical touch duration and the historical touch times;

in this embodiment, generally speaking, the longer the historical reach period, the higher the user's acceptance of the target credit product and, therefore, the higher the user's product demand for the target credit product. Generally, a greater number of historical hits indicates a greater degree of interest by the user in the target credit product, and thus a greater degree of product demand by the user for the target credit product. For example, the product of the historical length of exposure and the historical number of exposures may be taken as the product demand of the user for the target credit product.

In some embodiments, when the product demand degree is determined according to the historical touch duration and the historical touch times, a first score corresponding to the historical touch duration and a second score corresponding to the historical touch times are determined, and the product demand degree is determined according to the first score and the second score. Wherein the first score is positively correlated with the historical time of arrival, and the second score is positively correlated with the historical time of arrival. For example, a duration interval in which the history touch duration is located may be determined, and a score corresponding to the duration interval may be used as the first score, and a frequency interval in which the history touch frequency is located may be determined, and a score corresponding to the frequency interval may be used as the second score. The time interval with the larger numerical value corresponds to a higher score, and the time interval with the larger numerical value corresponds to a higher score.

In some embodiments, in the step of determining the product demand degree according to the first score and the second score, the sum of the first score and the second score may be used as the product demand degree. Of course, a first weight may also be set for the reach duration dimension, a second weight may also be set for the reach times dimension, and the first and second scores may be weighted and summed by using the first and second weights to obtain the product demand degree, where the second weight is generally greater than the second weight to focus more on the reach times when calculating the product demand degree.

In some embodiments, the historical reach data may further include a historical reach frequency, where the historical reach frequency is a ratio of the historical reach times to a total duration of the historical time period within a preset historical time period. When the product demand degree is determined according to the history touch duration and the history touch frequency, the score corresponding to the history touch frequency can be further determined, and the product demand degree is calculated by integrating the first score, the second score and the score corresponding to the history touch frequency, so that the product demand degree is more in line with the actual demand of the user.

103. Acquiring an expected credit interest rate corresponding to a user;

in this embodiment, the expected credit interest rate is the expected value of the user's credit interest rate for the target credit product. The expected credit interest rate may be set manually by the user or automatically calculated based on the user information. For example, the user's asset level may be evaluated according to the user's asset information, and the corresponding expected credit interest rate may be set according to the asset level, and for example, the user's credit habits may be evaluated according to the user's historical credit data, which may include historical credit interest rates corresponding to various credit products that the user has historically purchased, and the corresponding expected credit interest rate may be set according to the credit interest rate corresponding to the credit habits.

In some embodiments, the expected credit interest rate corresponding to the user may be aggregated based on the user's asset information and historical credit data. The property information of the user may include public property information of the user, for example, company property, and for an organization such as a bank, the property information of the user may also include the deposit amount of the user at the bank. The historical credit data may include historical credit interest rates corresponding to respective credit products that the user has historically purchased to determine the user's expected credit interest rate from the user's historical credit interest rates. Specifically, the asset information of the user can be evaluated to obtain the asset score, the integrated asset score and the historical credit interest rate, and the expected credit interest rate of the user can be determined. Generally, the asset score, as well as the historical credit interest rate, are positively correlated to the expected credit interest rate.

In some embodiments, the historical credit data may further include historical credit amounts, historical credit times, historical overdue times for repayment of the credit, historical overdue durations, etc. for which the user has historically purchased individual credit products, from which information, as well as the composite asset score and historical credit interest rates, the expected credit interest rate is synthetically determined to reduce the credit risk.

104. Adjusting a current credit interest rate of a target credit product according to the product demand degree and the expected credit interest rate;

in this embodiment, it is detected whether the product demand is greater than the demand threshold, and if the product demand is greater than the preset demand, it indicates that the product demand is too high, so that the credit interest rate of the target credit product can be increased correspondingly to increase the income from the credit product. If the product demand degree is less than or equal to the preset demand degree, the credit interest rate of the target credit product can be correspondingly reduced, or the credit interest rate of the target credit product is not changed. It may be determined whether the expected credit interest rate is less than the interest rate threshold, and if the expected credit interest rate is less than the interest rate threshold, the expected credit interest rate may be indicated as being low, and therefore the credit interest rate of the target credit product may be decreased accordingly to increase the user's willingness to purchase the target credit product. If the expected credit interest rate is greater than or equal to the interest rate threshold, then the credit interest rate of the target credit product may be increased or unchanged accordingly.

In some embodiments, the credit interest rate range of the target credit product may be set in advance based on actual conditions, and if the adjusted current credit interest rate is less than the minimum value of the credit interest rate range, the minimum value of the credit interest rate range may be used as the adjusted current credit interest rate. And if the adjusted current credit interest rate is larger than the maximum value of the credit interest rate range, taking the maximum value of the credit interest rate range as the adjusted current credit interest rate. Of course, when the adjusted current credit interest rate exceeds the credit interest rate range, the target credit product may not be recommended to the user, so as to save the recommendation cost of the target credit product. In some embodiments, other credit products with higher or lower credit interest rate ranges may also be recommended when the adjusted current credit interest rate is outside the credit interest rate range.

105. Based on the adjusted current credit interest rate, outputting recommendation information for the target credit product.

In this embodiment, the recommendation information for the target credit product is generated based on the adjusted current credit interest rate, and the recommendation information for the target credit product is output. The output mode may include direct output to the user side to reach the user, output to the marketer side for the marketer to recommend the target credit product to the user based on the adjusted current credit interest rate.

In the technical scheme disclosed in the embodiment, the current credit interest rate of the credit product is adjusted by calculating the demand degree of the user for the credit product and according to the demand degree of the product and the expected credit interest rate, so that the setting of the credit interest rate is more in line with the demand of the user and the expectation of the user, the recommendation success rate of the credit product is improved, and the recommendation cost of the credit product is reduced.

In another embodiment, as shown in fig. 2, based on the embodiment shown in fig. 1, step 104 includes:

201. obtaining an interest rate difference between the expected credit interest rate and a current credit interest rate of a target credit product;

202. when the interest rate difference value is larger than a preset interest rate difference value, determining a first adjusting value corresponding to the interest rate difference value, and obtaining a second adjusting value corresponding to the product demand degree, wherein the first adjusting value is positively correlated with the interest rate difference value, and the second adjusting value is negatively correlated with the product demand degree;

in this embodiment, when the current credit interest rate of the target credit product is adjusted according to the product demand degree and the expected credit interest rate, the interest rate difference between the expected credit interest rate and the current credit interest rate may be acquired. If the interest rate difference is larger than the preset interest rate difference, the difference between the current credit interest rate and the expected credit interest rate is larger, so that a first adjustment value corresponding to the interest rate difference can be determined, and a second adjustment value corresponding to the product demand degree is obtained to adjust the current credit interest rate. If the interest rate difference is less than the preset interest rate difference, the difference between the current credit interest rate and the expected credit interest rate is smaller, so that the current credit interest rate of the target credit product can not be adjusted. Wherein the greater of the expected and current credit interest rates minus the lesser of the expected and current credit interest rates equals the interest rate difference, i.e., the interest rate difference is a positive number.

203. Adjusting the current credit interest rate of the target credit product using the first adjustment value and the second adjustment value.

In this embodiment, if the expected credit interest rate is greater than the current credit interest rate of the target credit product, the current credit interest rate of the target credit product is increased by using the first adjustment value and the second adjustment value when adjusting the current credit interest rate of the target credit product. If the expected credit interest rate is less than the current credit interest rate of the target credit product, then the current credit interest rate of the target credit product is adjusted by reducing the current credit interest rate of the target credit product using the first adjustment value and the second adjustment value.

In some embodiments, in the step of adjusting the current credit interest rate of the target credit product using the first adjustment value and the second adjustment value, a credit channel origin to which the user belongs may also be obtained, wherein the credit channel origin includes at least one of a web page, a client, a human, a telephone. And acquiring the total recommendation success rate of the target credit product in the credit channel source, correcting the first adjustment value according to the total recommendation success rate, and adjusting the current credit interest rate of the target credit product by adopting the corrected first adjustment value and the second adjustment value.

In some embodiments, the credit channel source to which the user belongs may be determined according to a recommendation mode of last credit product recommendation to the user, and the recommendation mode may include webpage recommendation, client recommendation, manual recommendation, telephone recommendation, and the like. Of course, the credit channel source to which the user belongs may also be determined according to the channel in which the user was developed, for example, the user was developed by means of web page recommendation and the user was developed by means of telephone recommendation. Alternatively, the credit channel source to which the user belongs may also be manually selected by the user.

In some embodiments, in the step of counting the total recommendation success rate of the target credit product at the credit channel source, the total recommendation times of the target credit product for all users of the credit channel source and the total purchase times of the target credit product at the credit channel source can be obtained, and the ratio of the total purchase times to the total recommendation times is used as the total recommendation success rate of the target credit product at the credit channel source.

In some embodiments, in correcting the first adjustment value according to the total recommendation success rate, if the expected credit interest rate is greater than the current credit interest rate of the target credit product, the corrected first adjustment value is positively correlated with the total recommendation success rate to increase revenue from the credit product. When the first adjustment value is modified according to the total recommendation success rate, if the expected credit interest rate is less than the current credit interest rate of the target credit product, the modified first adjustment value is negatively correlated with the total recommendation success rate to increase the user's willingness to purchase the target credit product while accounting for revenue from the credit product.

In some embodiments, in order to correct the first adjustment value more accurately, when the first adjustment value is corrected according to the total recommendation success rate, a historical recommendation success rate for recommending credit products to the user may also be obtained, and the historical recommendation success rate and the total recommendation success rate are subjected to weighted summation to obtain a comprehensive recommendation rate, and the first adjustment value is corrected according to the comprehensive recommendation rate, wherein a specific manner of correcting the first adjustment value according to the comprehensive recommendation rate is similar to the specific manner of correcting the first adjustment value according to the total recommendation success rate, and is not described herein again. Generally, the weight corresponding to the historical recommendation success rate of recommending credit products to the user is greater than the weight corresponding to the total recommendation success rate.

In some embodiments, when obtaining the historical recommendation success rate of the credit products recommended to the user, a first total number of times of recommending each credit product to the user and a second total number of times of purchasing the credit products by the user can be obtained, wherein the percentage of the second total number of times to the first total number is the historical recommendation success rate of the credit products recommended to the user.

In the technical scheme disclosed in this embodiment, when the difference between the expected credit interest rate and the current credit interest rate is too large, the current credit interest rate of the target credit product is adjusted according to the first adjustment value corresponding to the interest rate difference and the second adjustment value corresponding to the product demand degree, so that the setting of the credit interest rate is more in line with the user demand and the user expectation.

In yet another embodiment, as shown in fig. 3, on the basis of the embodiment shown in any one of fig. 1 to fig. 2, after step 105, the method further includes:

301. obtaining the reach duration and the reach times when the target credit product is recommended based on the adjusted current credit interest rate;

302. updating the product demand degree of the user for the target credit product according to the reach duration and the reach times when the target credit product is recommended based on the adjusted current credit interest rate;

in this embodiment, in recommending the target credit product based on the adjusted current credit interest rate, the reach duration and the number of reach times for recommending the target credit product based on the adjusted current credit interest rate may be recorded. And according to the reaching duration and the reaching times when the target credit product is recommended based on the adjusted current credit interest rate, synthesizing the historical reaching duration and the historical reaching times, and re-determining the product demand of the user for the target credit product, namely updating the product demand.

303. And returning to the step of adjusting the current credit interest rate of the target credit product according to the product demand degree and the expected credit interest rate based on the updated product demand degree.

In this embodiment, the updated product demand level is used as the latest product demand level, and the process returns to step 104 to implement the feedback adjustment of the current credit interest rate of the target credit product.

In the technical scheme disclosed in this embodiment, after the recommendation information of the target credit product is output based on the adjusted current credit interest rate, the credit interest rate is continuously adjusted according to the reach duration and the reach times when the target credit product is recommended based on the adjusted current credit interest rate, so that the setting of the credit interest rate is more in line with the user requirements and the user expectations.

In another embodiment, as shown in fig. 4, on the basis of the embodiment shown in any one of fig. 1 to fig. 3, after step 103, the method further includes:

401. obtaining an interest rate difference between the expected credit interest rate and a current credit interest rate of a target credit product;

402. determining a third score corresponding to the interest rate difference, wherein the third score is positively correlated with the interest rate difference;

403. determining a fourth score corresponding to the product demand degree, wherein the fourth score is negatively related to the product demand degree;

in this embodiment, an appropriate recommendation mode corresponding to the target credit product may also be selected according to the product demand and the expected credit interest rate. Specifically, a interest rate difference between the expected credit interest rate and the current credit interest rate of the target credit product may be obtained, a third score corresponding to the interest rate difference may be calculated, and a fourth score corresponding to the product desirability may be calculated, wherein the interest rate difference may represent a gap between the expected credit interest rate and the current credit interest rate,

in some embodiments, the third score is positively correlated with interest rate difference and the fourth score is negatively correlated with product desirability. For example, a difference interval where the interest rate difference is located may be determined, a score corresponding to the difference interval may be used as a third score, a demand interval where the product demand is located may be determined, and a score corresponding to the demand interval may be used as a fourth score. The different difference intervals and the scores corresponding to the difference intervals are set in advance, the score corresponding to the difference interval with larger numerical value is higher, the different demand intervals and the scores corresponding to the demand intervals are set in advance, and the score corresponding to the demand interval with larger numerical value is higher.

404. And determining a recommendation mode of the target credit product according to the sum of the third score and the fourth score, wherein the recommendation mode comprises at least one of a webpage, a client, a manual work and a telephone, and the target credit product is recommended based on the recommendation mode.

In this embodiment, the recommendation mode of the target credit product may generally include at least one of a web page, a client, a person, and a phone, for example, the recommendation information of the target credit product may be pushed through the web page, the recommendation information of the target credit product may be pushed through the client, the recommendation information of the target credit product may be pushed through a manual line, and the recommendation information of the target credit product may be pushed through the phone. Generally, the recommendation success rates corresponding to different recommendation manners are different, so corresponding priorities may be set for different recommendation manners, for example, the higher the recommendation success rate is, the higher the priority corresponding to the recommendation manner is, and the ranking from high to low according to the priority may be: manual, telephone, client, web page. In the step of determining the recommendation mode of the target credit product according to the third score and the fourth score, the sum of the third score and the fourth score may be obtained, a score interval in which the sum of the third score and the fourth score is located may be determined, and the recommendation mode of the priority corresponding to the score interval may be used as the recommendation mode of the target credit product. The plurality of scoring intervals are set in advance, and the priorities corresponding to different scoring intervals are different, and generally, the higher the numerical value is, the higher the priority corresponding to the scoring interval is. In this way, the recommendation mode is selected according to the product demand degree and the expected credit interest rate, and the willingness of the user to purchase the target credit product can be further improved through a more appropriate recommendation mode. In addition, the higher the priority of the recommendation mode is, the higher the recommendation cost is, so that unnecessary recommendation cost can be reduced through reasonable selection of the recommendation mode.

In some embodiments, the individual recommendation success rate of each salesman can be acquired for different salesmen, and the salesmen with higher recommendation success rate can be set with higher salesmen priority. In this way, the corresponding target operator priority can be determined according to the sum of the third score and the fourth score, and the recommended tasks of the user for the target credit product are distributed to the operators with the target operator priority, so that the willingness of the user to purchase the target credit product is further improved. The rule for determining the priority of the target operator according to the sum of the third score and the fourth score is similar to the rule for determining the recommendation mode of the target credit product, and is not repeated here.

In the technical scheme disclosed in the embodiment, a more appropriate recommendation mode is selected according to the product demand degree and the expected credit interest rate, so that the recommendation success rate of the credit product can be improved, and the recommendation cost of the credit product can be reduced.

In order to better implement the information processing method in the credit product recommendation in the embodiment of the present application, on the basis of the information processing method in the credit product recommendation, an information processing apparatus in the credit product recommendation in the embodiment of the present application is further provided, as shown in fig. 5, the information processing apparatus 500 in the credit product recommendation includes an obtaining module 501, a determining module 502, an adjusting module 503, and an outputting module 504, and the specific details are as follows:

the acquisition module 501 is used for acquiring the historical reaching time and the historical reaching times of the user for the target credit product;

a determining module 502, configured to determine a product demand degree of the user for the target credit product according to the historical reach duration and the historical reach times; acquiring an expected credit interest rate corresponding to a user;

an adjusting module 503, configured to adjust the current credit interest rate of the target credit product according to the product demand degree and the expected credit interest rate;

an output module 504 for outputting the recommendation information for the target credit product based on the adjusted current credit interest rate.

The embodiment of the application also provides computer equipment which integrates the information processing device in any credit product recommendation provided by the embodiment of the application. As shown in fig. 6, it shows a schematic structural diagram of a computer device according to an embodiment of the present application, specifically:

the computer device may include components such as a processor 601 of one or more processing cores, memory 602 of one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will appreciate that the computer device configuration shown in FIG. 6 is not intended to constitute a limitation of computer devices and may include more or fewer components than those shown, or some of the components may be combined, or a different arrangement of components. Wherein:

the processor 601 is a control center of the computer device, connects various parts of the whole computer device by using various interfaces and lines, and performs various functions of the computer device and processes data by running or executing software programs and/or modules stored in the memory 602 and calling data stored in the memory 602, thereby monitoring the computer device as a whole. Alternatively, processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 601.

The memory 602 may be used to store software programs and modules, and the processor 601 executes various functional applications and data processing by operating the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

The computer device further comprises a power supply 603 for supplying power to the various components, and preferably, the power supply 603 is logically connected to the processor 601 through a power management system, so that functions of managing charging, discharging, and power consumption are realized through the power management system. The power supply 603 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.

The computer device may further include an input unit 604, and the input unit 604 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

Although not shown, the computer device may further include a display unit and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 601 in the computer device loads the executable file corresponding to the process of one or more application programs into the memory 602 according to the following instructions, and the processor 601 runs the application programs stored in the memory 602, thereby implementing various functions as follows:

determining the product demand degree of the user for the target credit product according to the historical reaching time length and the historical reaching times;

acquiring an expected credit interest rate corresponding to a user;

adjusting the current credit interest rate of the target credit product according to the product demand degree and the expected credit interest rate;

based on the adjusted current credit interest rate, outputting recommendation information for the target credit product.

It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.

To this end, an embodiment of the present application provides a computer-readable storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like. The computer program is loaded by a processor to execute the steps of the information processing method in any credit product recommendation provided by the embodiment of the application. For example, the computer program may be loaded by a processor to perform the steps of:

acquiring an expected credit interest rate corresponding to a user;

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed descriptions of other embodiments, and are not described herein again.

In a specific implementation, each unit or structure may be implemented as an independent entity, or may be combined arbitrarily to be implemented as one or several entities, and the specific implementation of each unit or structure may refer to the foregoing method embodiment, which is not described herein again.

The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.

The above detailed description is provided for the information processing method, the computer device and the storage medium in the credit product recommendation provided by the embodiment of the present application, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiment is only used to help understanding the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. An information processing method in credit product recommendation, characterized by comprising:

acquiring an expected credit interest rate corresponding to a user;

2. The method of information processing in credit product recommendations according to claim 1 wherein the step of determining the product desirability of a user for a target credit product based on the historical length of exposure and the historical number of exposures comprises:

3. The method of processing information in credit product recommendations according to claim 1 wherein the step of obtaining the user's corresponding expected credit interest rate comprises:

4. The method of processing information in credit product recommendations according to claim 1 wherein the step of adjusting the current credit interest rate of a target credit product based on the product desirability and the expected credit interest rate comprises:

5. The method of information processing in credit product recommendations according to claim 4 wherein the step of adjusting the current credit interest rate of the target credit product using the first adjustment value and the second adjustment value comprises:

6. The method of information processing in credit product recommendations according to claim 5 wherein the step of modifying the first adjustment value based on the overall recommendation success rate comprises:

7. The method of processing information in credit product recommendations according to claim 1, wherein after the step of outputting the recommendation information for the target credit product based on the adjusted current credit interest rate, further comprising:

8. The method of processing information in credit product recommendations according to claim 1, wherein the step of obtaining the corresponding expected credit interest rate of the user is followed by further comprising:

9. A computer device, characterized in that the computer device comprises:

one or more processors;

a memory; and

one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the steps in the method of information processing in credit product recommendations of any one of claims 1-8.

10. A computer-readable storage medium, having stored thereon a computer program which is loaded by a processor to execute the steps in the information processing method in a credit product recommendation according to any one of claims 1 to 8.