CN116402582A - Personalized intelligent recommendation system for insurance products - Google Patents
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Abstract
The invention discloses an individualized intelligent recommendation system for insurance products, in particular to the technical field of insurance product recommendation, which is used for solving the problems that when the existing insurance products suitable for users are determined manually, the influence of subjective factors such as manual experience is large, and the efficiency and the accuracy are low; the system comprises a data processing module, a user information acquisition module, an insurance product information acquisition module, a recommendation module and a sequencing module, wherein the user information acquisition module, the insurance product information acquisition module, the recommendation module and the sequencing module are in signal connection with the data processing module; the user information and the insurance product information are collected, the user evaluation coefficient and the insurance product comprehensive evaluation coefficient are calculated through the data processing module, the insurance product recommendation coefficient is obtained through a formula by the user evaluation coefficient and the insurance product comprehensive evaluation coefficient, and the insurance product is recommended in a grading manner, so that the most suitable insurance product is recommended for the user, the satisfaction degree of the user is improved, and meanwhile, the sales volume and the market share of an insurance company can be increased.
Description
Technical Field
The invention relates to the technical field of insurance product recommendation, in particular to an insurance product personalized intelligent recommendation system.
Background
Insurance is taken as a risk management mode, generally, an applicant pays a certain fee to an insurance company to obtain a corresponding risk guarantee in order to reduce or transfer own risk, and the mode is more and more accepted by people, and the reduction of social risk through the mode of insurance has important significance for promoting social stability and improving folk life.
When the insurance products suitable for the user are recommended individually for the user in the prior art, a sales manager and the like usually manually determine the insurance products suitable for the user after knowing personal information of the user through communication with the user so as to conduct individual recommendation; however, when the insurance product suitable for the user is determined manually, the influence of subjective factors such as manual experience is large, the situations of imperfect decision and the like are unavoidable, and the problem of low efficiency and accuracy exists.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides an insurance product personalized intelligent recommendation system to solve the above-mentioned problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the personalized intelligent recommendation system for the insurance products comprises a data processing module, and a user information acquisition module, an insurance product information acquisition module, a recommendation module and a sequencing module which are connected with the data processing module through signals;
the user information acquisition module acquires user information, the user information is sent to the data processing module, the data processing module calculates to obtain a user evaluation coefficient, and the state of a user is distinguished according to the user evaluation coefficient;
the insurance product information acquisition module acquires insurance product information of an insurance company, sends the acquired insurance product information of the insurance company to the data processing module, calculates and obtains an insurance product comprehensive evaluation coefficient, and determines the comprehensive guarantee degree of the insurance product according to the insurance product comprehensive evaluation coefficient;
the data processing module calculates the user evaluation coefficient and the comprehensive evaluation coefficient of the insurance product to obtain an insurance product recommendation coefficient, and the recommendation module receives the insurance product recommendation coefficient calculated by the data processing module to conduct classified recommendation on the insurance product;
and the sorting module sorts the insurance products marked as the third-level recommendation and the insurance products marked as the second-level recommendation from large to small respectively according to the insurance product recommendation coefficients calculated by the data processing module.
In a preferred embodiment, the user information includes age, BMI index deviation value, exercise step ratio, drinking frequency ratio, and blood pressure deviation value;
respectively marking the age, the BMI index deviation value, the exercise step ratio, the drinking frequency ratio and the blood pressure deviation value as Ag, bv, hr, dr, bd;
the data processing module normalizes the age, the BMI index deviation value, the exercise step ratio, the drinking frequency ratio and the blood pressure deviation value, calculates a user evaluation coefficient, and has the expression:
wherein H is a user evaluation coefficient, a 1 、a 2 、a 3 、a 4 、a 5 Preset proportional coefficients of age, BMI index deviation value, exercise step ratio, drinking frequency ratio and blood pressure deviation value are respectively obtained;
setting a user evaluation coefficient threshold value, and marking the user evaluation coefficient threshold value as H 0 When the user evaluation coefficient is smaller than the user evaluation coefficient threshold, marking the state of the user as poor; and when the user evaluation coefficient is greater than or equal to the user evaluation coefficient threshold value, marking the state of the user as good.
In a preferred embodiment, the user information acquisition module further acquires user basic information including gender, occupation and user location, wherein the set drinking number, the set exercise step number, the set BMI index and the set blood pressure value are set according to the gender, occupation and age included in the user information.
In a preferred embodiment, the insurance product information includes an applied amount, an insurance period, an insurance rate, a reimbursement amount, and an reimbursement allowance;
marking the applied amount, the insurance period, the insurance rate, the reimbursement amount and the reimbursement allowance as Si, pi, pf, da, li, respectively;
the data processing module normalizes the applied amount, the insurance period, the insurance rate, the claim free amount and the compensation limit, calculates the comprehensive evaluation coefficient of the insurance product, and the expression is as follows:
wherein J is the comprehensive evaluation coefficient of the insurance product, b 1 、b 2 、b 3 、b 4 、b 5 Preset proportional coefficients of the applied amount, the insurance period, the insurance rate, the claim-free amount and the compensation limit respectively;
setting an insurance product comprehensive evaluation coefficient threshold value, and marking the insurance product comprehensive evaluation coefficient threshold value as J 0 。
And when the comprehensive evaluation coefficient of the insurance product is larger than the comprehensive evaluation coefficient threshold value of the insurance product, marking the insurance product as high in comprehensive guarantee degree.
And when the comprehensive evaluation coefficient of the insurance product is smaller than or equal to the threshold value of the comprehensive evaluation coefficient of the insurance product, marking the insurance product as common in comprehensive guarantee degree.
In a preferred embodiment, insurance product recommendation coefficients are calculated, expressed as:
wherein T is an insurance product recommendation coefficient, and alpha and beta are preset proportional coefficients of the ratio of a user evaluation coefficient threshold value to a user evaluation coefficient ratio and the ratio of an insurance product comprehensive evaluation coefficient to an insurance product comprehensive evaluation coefficient threshold value respectively;
setting a first threshold value of the insurance product recommendation coefficient and a second threshold value of the insurance product recommendation coefficient, wherein the first threshold value of the insurance product recommendation coefficient is larger than the second threshold value of the insurance product recommendation coefficient;
when the insurance product recommendation coefficient is greater than the insurance product recommendation coefficient first threshold, marking the insurance product as three-level recommendation;
when the insurance product recommendation coefficient is smaller than or equal to the first threshold value of the insurance product recommendation coefficient, and the insurance product recommendation coefficient is larger than or equal to the second threshold value of the insurance product recommendation coefficient, marking the insurance product as second-level recommendation;
when the insurance product recommendation coefficient is smaller than a second threshold value of the insurance product recommendation coefficient, marking the insurance product as first-level recommendation;
for the insurance products marked as three-level recommendation, sorting the insurance products according to the recommendation coefficients of the insurance products in a user browsing interface from large to small for users to browse;
prompting a user on a user browsing interface whether to present the second-level recommended insurance product on the user browsing interface, and if the user selects to present the second-level recommended insurance product, presenting the insurance product on the user browsing interface according to the descending order of the insurance product recommendation coefficients of the insurance products;
for insurance products marked as first-level recommendations, no user-browsing interface is displayed.
The personalized intelligent recommendation system for the insurance products has the technical effects and advantages that:
through the comparison of the user evaluation coefficient and the user evaluation coefficient threshold value, the insurance company can know the state of the user more accurately, so that a more suitable insurance product is provided for the user according to the state of the user, and the satisfaction degree of the user is improved; the comprehensive evaluation coefficient of the insurance product is calculated, so that the insurance company and the user can evaluate the quality of the insurance product more comprehensively and objectively; and comparing and screening different insurance products, thereby improving the accuracy of insurance product recommendation.
The user evaluation coefficient and the comprehensive insurance product evaluation coefficient are comprehensively analyzed to obtain an insurance product recommendation coefficient through a formula, the insurance products are classified and recommended according to the insurance product recommendation coefficient, so that the most suitable insurance products are recommended to the user, and the insurance products are presented on a user browsing interface according to the descending order of the insurance product recommendation coefficient, so that the user can more conveniently select the most suitable insurance products, the satisfaction degree of the user can be improved through personalized recommendation, the sales volume and market share of an insurance company can be increased, and the experience of the user is improved.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent personalized recommendation system for insurance products according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Fig. 1 shows a schematic structural diagram of an insurance product personalized intelligent recommendation system of the invention, which comprises a data processing module, and a user information acquisition module, an insurance product information acquisition module, a recommendation module and a sequencing module which are connected with the data processing module in a signal manner.
The user information acquisition module acquires user information, the user information is sent to the data processing module, the data processing module calculates to obtain a user evaluation coefficient, and the state of the user is distinguished according to the user evaluation coefficient.
The insurance product information acquisition module acquires insurance product information of an insurance company, sends the acquired insurance product information of the insurance company to the data processing module, calculates and obtains an insurance product comprehensive evaluation coefficient, and determines the comprehensive guarantee degree of the insurance product according to the insurance product comprehensive evaluation coefficient.
The data processing module calculates the user evaluation coefficient and the comprehensive insurance product evaluation coefficient to obtain an insurance product recommendation coefficient, and the recommendation module receives the insurance product recommendation coefficient calculated by the data processing module to conduct classified recommendation on the insurance product.
And the sorting module sorts the insurance products marked as the third-level recommendation and the insurance products marked as the second-level recommendation from large to small respectively according to the insurance product recommendation coefficients calculated by the data processing module.
The user information acquisition module acquires user information including, but not limited to, age, BMI index deviation value, exercise step ratio, drinking frequency ratio and blood pressure deviation value; for a better presentation, the invention is analyzed from age, BMI index bias, exercise step ratio, drinking frequency ratio, and blood pressure bias.
Age, BMI index bias, exercise step ratio, drinking frequency ratio, and blood pressure bias were labeled Ag, bv, hr, dr, bd, respectively.
The data processing module normalizes the age, the BMI index deviation value, the exercise step ratio, the drinking frequency ratio and the blood pressure deviation value, calculates a user evaluation coefficient, and has the expression:
wherein H is a user evaluation coefficient, a 1 、a 2 、a 3 、a 4 、a 5 Preset proportionality coefficients of age, BMI index deviation value, exercise step ratio, drinking frequency ratio and blood pressure deviation value respectively, wherein a 1 ＞a 5 ＞a 2 ＞a 4 ＞a 3 > 0, and a 1 +a 2 +a 3 +a 4 +a 5 ＝5.539；
The larger the user evaluation coefficient, the better the state of the user, the smaller the user evaluation coefficient, and the worse the state of the user.
To more conveniently distinguish the states of users, a user evaluation coefficient threshold is set and marked as H 0 When the user evaluation coefficient is smaller than the user evaluation coefficient threshold, marking the state of the user as poor; and when the user evaluation coefficient is greater than or equal to the user evaluation coefficient threshold value, marking the state of the user as good.
Through the comparison of the user evaluation coefficient and the user evaluation coefficient threshold value, the insurance company can more accurately know the state of the user, improve the satisfaction of the user, and the state of the user can improve the accuracy of personalized recommendation of the insurance product by collecting age, BMI index deviation value, exercise step ratio, drinking frequency ratio and blood pressure deviation value related to the health of the user: by collecting the health data of the user, the health condition of the user can be more comprehensively known, so that the insurance product suitable for the user is recommended to the user more accurately according to the health condition of the user.
Age: the age of the user is the greater, the body function is gradually reduced, and various chronic diseases such as hypertension, diabetes mellitus and the like are easy to occur.
BMI index deviation value: the BMI index (body mass index) is a value obtained by dividing the weight kilogram number by the height meter number by a calculation formula using the square of the body mass (weight) and the height as the index; the BMI index deviation value is the deviation value between the actual BMI index of the user and the set BMI index, and the greater the BMI index deviation value, the further the body weight of a person deviates from the normal range, and the risk of chronic diseases such as obesity, diabetes, hypertension and the like may be increased.
Step ratio of exercise: the motion step number ratio is the ratio of the motion step number of the user to the set motion step number, and the higher the motion step number ratio is, the larger the motion step number of the user is, and the better the physical state is.
Drinking frequency ratio: the drinking frequency ratio is the ratio of drinking times to the set drinking times, the statistical time of drinking times can be monthly or annually, and the lower the drinking frequency ratio is, the lower the drinking times of one person per week is, and the better the physical condition is.
Blood pressure deviation value: the blood pressure deviation value is a deviation value between the actual blood pressure value of the user and the set blood pressure value; the larger the blood pressure deviation value is, the more the blood pressure of the user deviates from the ideal state, and the risk of chronic diseases such as hypertension, cardiovascular and cerebrovascular diseases and the like may be increased.
The user information acquisition module also acquires user basic information, wherein the user basic information comprises but is not limited to gender, occupation and user places, and the user basic information comprises the steps of setting drinking times, setting exercise steps, setting BMI indexes and setting blood pressure values according to the gender, occupation and age included in the user information; this is because the effects of different ages, sexes, and professions on the set number of drinking, the set number of steps, the set BMI index, and the set blood pressure value are different, for example, the set number of steps for the elderly is smaller than the set number of steps for the young.
The user information acquisition module acquires user information and user basic information through channels such as webpages and APP, and the user information and the user basic information are acquired through filling in pages by a user.
It is noted that, for the user information and the user basic information collected by the user information collecting module, privacy protection should be performed on the user information and the user basic information, and the privacy protection should follow the rules including, but not limited to, the personal information protection law of the people's republic of China and the insurance law of the people's republic of China.
Example 2
The insurance product information acquisition module acquires insurance product information of an insurance company, wherein the insurance product information comprises an insurance amount, an insurance period, an insurance rate, a claim-free amount and a compensation limit.
The amount of insurance applied, the period of insurance, the insurance rate, the amount of reimbursement, and the amount of reimbursement are labeled Si, pi, pf, da, li, respectively.
The data processing module normalizes the applied amount, the insurance period, the insurance rate, the claim free amount and the compensation limit, calculates the comprehensive evaluation coefficient of the insurance product, and the expression is as follows:
wherein J is the comprehensive evaluation coefficient of the insurance product, b 1 、b 2 、b 3 、b 4 、b 5 Preset proportionality coefficients for the amount of insurance, the period of insurance, the rate of insurance, the amount of reimbursement and the limit of reimbursement, respectively, wherein b 1 ＞b 3 ＞b 2 ＞b 5 ＞b 4 > 0, and b 1 +b 2 +b 3 +b 4 +b 5 ＝5.588。
The amount of insurance, the period of insurance, the insurance rate, the free amount and the compensation limit are all common contents of the insurance industry, and the amount of insurance, the period of insurance, the insurance rate, the free amount and the compensation limit are not described herein.
The comprehensive safety product evaluation coefficient reflects the comprehensive guarantee degree of the safety product, the comprehensive safety product evaluation coefficient threshold value is set, and the comprehensive safety product evaluation coefficient threshold value is marked as J 0 。
And when the comprehensive evaluation coefficient of the insurance product is larger than the comprehensive evaluation coefficient threshold value of the insurance product, marking the insurance product as high in comprehensive guarantee degree.
And when the comprehensive evaluation coefficient of the insurance product is smaller than or equal to the threshold value of the comprehensive evaluation coefficient of the insurance product, marking the insurance product as common in comprehensive guarantee degree.
Through normalization processing of the insurance amount, the insurance period, the insurance rate, the claim free amount and the compensation limit, and calculation of the comprehensive evaluation coefficient of the insurance product, the insurance company and the user can evaluate the quality of the insurance product more comprehensively and objectively; and comparing and screening different insurance products, thereby improving the accuracy of insurance product recommendation.
Example 3
The recommendation module obtains the user evaluation coefficient and the comprehensive evaluation coefficient of the insurance product, calculates the recommendation coefficient of the insurance product through calculation of the data processing module, and determines the recommendation level according to the recommendation coefficient of the insurance product.
Calculating an insurance product recommendation coefficient, wherein the expression is as follows:
wherein T is an insurance product recommendation coefficient, α and β are preset proportional coefficients of a ratio of a user evaluation coefficient threshold to a user evaluation coefficient, and a ratio of an insurance product comprehensive evaluation coefficient to an insurance product comprehensive evaluation coefficient threshold, respectively, where α > β > 0, and α+β=2.236.
And setting a first threshold value of the insurance product recommendation coefficient and a second threshold value of the insurance product recommendation coefficient, wherein the first threshold value of the insurance product recommendation coefficient is larger than the second threshold value of the insurance product recommendation coefficient.
Classified recommendation is carried out on the insurance products according to the insurance product recommendation coefficients:
and marking the insurance product as a three-level recommendation when the insurance product recommendation coefficient is greater than the insurance product recommendation coefficient first threshold.
And marking the insurance product as a secondary recommendation when the insurance product recommendation coefficient is smaller than or equal to the first threshold value of the insurance product recommendation coefficient and the insurance product recommendation coefficient is larger than or equal to the second threshold value of the insurance product recommendation coefficient.
And when the insurance product recommendation coefficient is smaller than the second threshold value of the insurance product recommendation coefficient, marking the insurance product as a first-level recommendation.
The degree of adaptation of the insurance product marked as the third-level recommendation to the user is greater than the degree of adaptation of the insurance product marked as the second-level recommendation to the user, and the degree of adaptation of the insurance product marked as the second-level recommendation to the user is greater than the degree of adaptation of the insurance product marked as the first-level recommendation to the user.
And for the insurance products marked as three-level recommendation, sorting the insurance products according to the size of the insurance product recommendation coefficient of the insurance products in a user browsing interface for users to browse.
And prompting a user on a user browsing interface whether to display the second-level recommended insurance product or not for the insurance product marked as the second-level recommendation, and displaying the insurance product on the user browsing interface according to the descending order of the insurance product recommendation coefficient of the insurance product if the user selects to display the second-level recommended insurance product.
For insurance products marked as first-level recommendations, no user-browsing interface is displayed.
The user browsing interface refers to a platform provided by an insurance company when a user browses and selects an insurance product, and comprises but is not limited to an APP browsing interface and a webpage browsing interface.
And carrying out classified recommendation on the insurance products according to the insurance product recommendation coefficients, and displaying the insurance products on a user browsing interface according to the descending order of the insurance product recommendation coefficients, so that a user can more conveniently select the insurance products which are most suitable for the user, and the experience of the user is improved.
And the sorting module sorts the insurance products marked as the third-level recommendation and the insurance products marked as the second-level recommendation from large to small respectively according to the insurance product recommendation coefficient calculated by the data processing module.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The personalized intelligent recommendation system for the insurance products is characterized by comprising a data processing module, and a user information acquisition module, an insurance product information acquisition module, a recommendation module and a sequencing module which are connected with the data processing module through signals;
the user information acquisition module acquires user information, the user information is sent to the data processing module, the data processing module calculates to obtain a user evaluation coefficient, and the state of a user is distinguished according to the user evaluation coefficient;
the insurance product information acquisition module acquires insurance product information of an insurance company, sends the acquired insurance product information of the insurance company to the data processing module, calculates and obtains an insurance product comprehensive evaluation coefficient, and determines the comprehensive guarantee degree of the insurance product according to the insurance product comprehensive evaluation coefficient;
the data processing module calculates the user evaluation coefficient and the comprehensive evaluation coefficient of the insurance product to obtain an insurance product recommendation coefficient, and the recommendation module receives the insurance product recommendation coefficient calculated by the data processing module to conduct classified recommendation on the insurance product;
and the sorting module sorts the insurance products marked as the third-level recommendation and the insurance products marked as the second-level recommendation from large to small respectively according to the insurance product recommendation coefficients calculated by the data processing module.
2. The personalized intelligent recommendation system for insurance products according to claim 1, wherein: the user information comprises age, BMI index deviation value, exercise step ratio, drinking frequency ratio and blood pressure deviation value;
respectively marking the age, the BMI index deviation value, the exercise step ratio, the drinking frequency ratio and the blood pressure deviation value as Ag, bv, hr, dr, bd;
the data processing module normalizes the age, the BMI index deviation value, the exercise step ratio, the drinking frequency ratio and the blood pressure deviation value, calculates a user evaluation coefficient, and has the expression:
wherein H is the user evaluation systemNumber, a 1 、a 2 、a 3 、a 4 、a 5 Preset proportionality coefficients of age, BMI index deviation value, exercise step ratio, drinking frequency ratio and blood pressure deviation value respectively, wherein a 1 ＞a 5 ＞a 2 ＞a 4 ＞a 3 ＞0；
Setting a user evaluation coefficient threshold value, and marking the user evaluation coefficient threshold value as H 0 When the user evaluation coefficient is smaller than the user evaluation coefficient threshold, marking the state of the user as poor; and when the user evaluation coefficient is greater than or equal to the user evaluation coefficient threshold value, marking the state of the user as good.
3. The personalized intelligent recommendation system for insurance products according to claim 2, wherein: the user information acquisition module also acquires user basic information, wherein the user basic information comprises gender, occupation and user places, and the drinking times, the exercise steps, the BMI index and the blood pressure value are set according to the gender, the occupation and the age included in the user information.
4. A personalized intelligent recommendation system for insurance products according to claim 3, wherein: the insurance product information includes an applied amount, an insurance period, an insurance rate, a claim-free amount, and a compensation limit;
marking the applied amount, the insurance period, the insurance rate, the reimbursement amount and the reimbursement allowance as Si, pi, pf, da, li, respectively;
the data processing module normalizes the applied amount, the insurance period, the insurance rate, the claim free amount and the compensation limit, calculates the comprehensive evaluation coefficient of the insurance product, and the expression is as follows:
wherein J is the comprehensive evaluation coefficient of the insurance product, b 1 、b 2 、b 3 、b 4 、b 5 Respectively, an applied amount, an insurance period, an insurance rate, a claim-free amount andpreset scaling factor of reimbursement limit, wherein b 1 ＞b 3 ＞b 2 ＞b 5 ＞b 4 ＞0；
Setting an insurance product comprehensive evaluation coefficient threshold value, and marking the insurance product comprehensive evaluation coefficient threshold value as J 0 。
And when the comprehensive evaluation coefficient of the insurance product is larger than the comprehensive evaluation coefficient threshold value of the insurance product, marking the insurance product as high in comprehensive guarantee degree.
And when the comprehensive evaluation coefficient of the insurance product is smaller than or equal to the threshold value of the comprehensive evaluation coefficient of the insurance product, marking the insurance product as common in comprehensive guarantee degree.
5. The personalized intelligent recommendation system for insurance products according to claim 4, wherein: calculating an insurance product recommendation coefficient, wherein the expression is as follows:
wherein T is an insurance product recommendation coefficient, alpha and beta are preset proportional coefficients of a ratio of a user evaluation coefficient threshold value to a user evaluation coefficient ratio, and a ratio of an insurance product comprehensive evaluation coefficient to an insurance product comprehensive evaluation coefficient threshold value, wherein alpha is more than beta is more than 0;
setting a first threshold value of the insurance product recommendation coefficient and a second threshold value of the insurance product recommendation coefficient, wherein the first threshold value of the insurance product recommendation coefficient is larger than the second threshold value of the insurance product recommendation coefficient;
when the insurance product recommendation coefficient is greater than the insurance product recommendation coefficient first threshold, marking the insurance product as three-level recommendation;
when the insurance product recommendation coefficient is smaller than or equal to the first threshold value of the insurance product recommendation coefficient, and the insurance product recommendation coefficient is larger than or equal to the second threshold value of the insurance product recommendation coefficient, marking the insurance product as second-level recommendation;
when the insurance product recommendation coefficient is smaller than a second threshold value of the insurance product recommendation coefficient, marking the insurance product as first-level recommendation;
for the insurance products marked as three-level recommendation, sorting the insurance products according to the recommendation coefficients of the insurance products in a user browsing interface from large to small for users to browse;
prompting a user on a user browsing interface whether to present the second-level recommended insurance product on the user browsing interface, and if the user selects to present the second-level recommended insurance product, presenting the insurance product on the user browsing interface according to the descending order of the insurance product recommendation coefficients of the insurance products;
for insurance products marked as first-level recommendations, no user-browsing interface is displayed.
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| KR20160124628A (en) * | 2015-04-20 | 2016-10-28 | 이기훈 | User similarity-based insurance goods recommendation system |
| CN108446990A (en) * | 2018-03-15 | 2018-08-24 | 全民福网络科技有限公司 | Insurance service is in line platform |
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| CN111784526A (en) * | 2020-07-20 | 2020-10-16 | 湖州师范学院 | Personalized recommendation method for personal accident risk |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160124628A (en) * | 2015-04-20 | 2016-10-28 | 이기훈 | User similarity-based insurance goods recommendation system |
| CN108446990A (en) * | 2018-03-15 | 2018-08-24 | 全民福网络科技有限公司 | Insurance service is in line platform |
| CN109559190A (en) * | 2018-10-22 | 2019-04-02 | 中国平安人寿保险股份有限公司 | Insurance products data push method, device, medium and computer equipment |
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