CN111784526A - Personalized recommendation method for personal accident risk - Google Patents
Personalized recommendation method for personal accident risk Download PDFInfo
- Publication number
- CN111784526A CN111784526A CN202010696124.3A CN202010696124A CN111784526A CN 111784526 A CN111784526 A CN 111784526A CN 202010696124 A CN202010696124 A CN 202010696124A CN 111784526 A CN111784526 A CN 111784526A
- Authority
- CN
- China
- Prior art keywords
- insurance
- personal accident
- user
- accident risk
- personalized recommendation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 206010042434 Sudden death Diseases 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 208000012260 Accidental injury Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Finance (AREA)
- Theoretical Computer Science (AREA)
- Accounting & Taxation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Technology Law (AREA)
- Life Sciences & Earth Sciences (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention discloses a personalized recommendation method for personal accident insurance, wherein the number of insurance types is assumed to be n, the number of guarantee items of each insurance is assumed to be m, and the corresponding premium is the corresponding premiumCorresponding premium is ρnWherein m and n are integers greater than or equal to 1; the recommended method is as follows: the method comprises the following steps: establishing an insurance label; step two: establishing a user label; step three: calculating the similarity between the user and the guarantee project; step four: finding the optimal insurance; step five: sorting insurance types; the invention has the beneficial effects that: when a user purchases insurance, the system is convenient to match with proper personal accident risk and pushes the personal accident risk to the user, thereby reducing the time spent on knowing all existing insurance productsThe specific content improves the experience of the user.
Description
Technical Field
The invention belongs to the technical field of information, and particularly relates to a personalized recommendation method for personal accident risk.
Background
The existing insurance products have various types, cover different insurance responsibilities, achieve different insurance guarantees, and for a common user, when buying insurance, a large amount of time is needed to know the specific contents of all the existing insurance products, and the user experience is very poor when selecting the insurance products suitable for the user.
In order to improve matching effect and user experience, a personalized recommendation method for personal accident risk is provided.
Disclosure of Invention
The invention aims to provide a personalized recommendation method for personal accident risk, which improves the matching effect and improves the experience of users.
In order to achieve the purpose, the invention provides the following technical scheme: a personalized recommendation method for personal accident insurance is provided, wherein the number of insurance types is n, the number of guarantee items of each insurance is m, and the corresponding premium is the corresponding premiumCorresponding premium is ρnWherein m and n are integers greater than or equal to 1; the recommended method is as follows:
the method comprises the following steps: establishing an insurance label; the method for establishing the insurance label comprises the following steps: calculating the characteristic values of different insurance types according to the insurance label model and indicating the insurance labels, wherein the searching mode of the characteristic values is
Step two: establishing a user label; according to the personal condition of the user, indicating the guarantee items suitable for the characteristics of the user;
step three: calculating the similarity between the user and the guarantee project; calculating the similarity with the guarantee items according to the personal conditions of the users and the corresponding guarantee items;
step four: finding the optimal insurance; according to the insurance personalized recommendation optimization model, the method can obtain
V=[v1v2... vn];
Step five: and (4) sorting insurance types.
As a preferred technical solution of the present invention, in the first step, the method for establishing the insurance label includes: and calculating characteristic values of different insurance types according to the insurance label model, and indicating the insurance labels.
As a preferred technical solution of the present invention, the manner of searching for the characteristic value is
As a preferable technical scheme, the system further comprises a matching module, and the matching module is used for matching insurance types of personal accident insurance, guarantee items and users.
As a preferred technical solution of the present invention, the system further includes a dynamic update module, and the dynamic update module is configured to update the newly added personal accident risk data.
The invention further comprises a pushing module which is used for pushing the matched personal accident risk data to the user.
Compared with the prior art, the invention has the beneficial effects that:
when a user purchases insurance, the method is convenient for matching proper personal accident risk and pushing the personal accident risk to the user, so that the time spent on knowing the specific contents of all existing insurance products is reduced, and the user experience is improved.
Drawings
FIG. 1 is a flow chart of a recommendation method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Example 1
Referring to fig. 1, the present invention provides a technical solution: a personalized recommendation method for personal accident insurance is provided, wherein the number of insurance types is n, the number of guarantee items of each insurance is m, and the corresponding premium is the corresponding premiumCorresponding premium is ρnAnd m and n are integers of 1 or more.
Taking the personal accident insurance introduced by an insurance company on the market as an example, as shown in table 1:
TABLE 1 different insurance clauses of certain insurance company
Table 1 shows a personal accident insurance from an insurance company, where n is 4 insurance choices for users, i.e., basic, upgrade, luxury, and honor; each insurance has m ═ 13 guarantee items, which are respectively accidental personal failure/disability, high-risk exercise expansion, sudden death guarantee, accidental injury medical treatment, accidental fracture insurance, accidental hospitalization allowance,Ambulance fare, riding a passenger civil aviation airliner, riding a passenger motor vehicle, riding a private car, riding a passenger ship, riding a passenger rail transit, and personal third party responsibilities; the corresponding quota isThe corresponding premium is: rho1=65,ρ2=186,ρ3=348,ρ4=510。
Taking the insurance clauses in the table 1 as an example, the following user is personally recommended to be properly subjected to personal accident insurance.
The method comprises the following steps: establishing an insurance label; calculating the characteristic values of different insurance types in the table 1 according to the insurance label model, and indicating an insurance label;
the feature value searching method takes "basic money" as an example:
similarly, the guarantee weight of the 'upgrade money', 'luxury money' and 'honor-enjoy money' on the guarantee project is calculated, and the following table is listed:
TABLE 2 eigenvalue table of four insurance types
As can be seen from table 2, the maximum security weight of the four types of personal accident insurance is "accident personal accident/disability", which fully explains the maximum purpose of the insurance company to launch the insurance, and the second security weight of the basic money, the upgrade money and the luxury money is "private car riding", and the second security weight of the honorable money is "sudden death guarantee", which indicates that the security features of the four types of personal accident insurance are different; the occurrence probability data of various guarantee projects in the example come from national statistical bureau for yearbook and road transportation websites in China;
step two: establishing a user label; according to the personal condition of the user, indicating the guarantee items suitable for the characteristics of the user;
the following table is established:
TABLE 3 user tag and guarantee item Table
Step three: calculating the similarity between the user and the guarantee project; calculating the similarity with the guarantee items according to the personal conditions of the users and the corresponding guarantee items; the following table is established:
TABLE 4 similarity Table between user and guarantee item
From the table, it can be observed that the matching degree of the personal condition of mr. zhang and the guarantee item "10-riding private car" is the highest, so mr. zhang is most suitable for purchasing the insurance type with the higher guarantee weight of "riding private car";
as can be seen from Table 2, Mr. Zhang selects insurance more appropriately among the basic, upgraded and luxury money;
step four: finding the optimal insurance; according to the insurance personalized recommendation optimization model, we can obtain:
the performance-price ratio of the upgrade insurance in the three insurance types according with the personal condition of Mr. Zhang is higher than that of the basic insurance and the luxury insurance, and the upgrade insurance should be preferentially recommended to Mr. Zhang in consideration of the reasonable family income of Mr. Zhang.
Example 2
Embodiment 2 is substantially the same as embodiment 1, and further comprises a matching module for matching the insurance type of personal accident insurance and the guarantee items and users.
Example 3
Embodiment 3 is substantially the same as embodiment 1, and further includes a dynamic update module, which is configured to update the newly added personal accident risk data.
Example 4
Embodiment 4 is substantially the same as embodiment 1, further comprising a push module for pushing the matched personal adventure data to the user.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A personalized recommendation method for personal accident insurance is characterized in that the corresponding premium is n, the number of insurance types is assumed to be n, the number of guarantee items of each insurance type is assumed to be m, and the corresponding premium isCorresponding premium is ρnWherein m and n are integers greater than or equal to 1; the recommended method is as follows:
the method comprises the following steps: establishing an insurance label;
step two: establishing a user label;
step three: calculating the similarity between the user and the guarantee project;
step four: finding the optimal insurance;
step five: and (4) sorting insurance types.
2. The method for personalized recommendation of personal accident risk according to claim 1, wherein: in the first step, the method for establishing the insurance label comprises the following steps: and calculating characteristic values of different insurance types according to the insurance label model, and indicating the insurance labels.
4. The method for personalized recommendation of personal accident risk according to claim 1, wherein: the system also comprises a matching module, wherein the matching module is used for matching the insurance types of personal accident insurance, the guarantee items and the users.
5. The method for personalized recommendation of personal accident risk according to claim 1, wherein: the system further comprises a dynamic updating module, and the dynamic updating module is used for updating the newly added personal accident risk data.
6. The method for personalized recommendation of personal accident risk according to claim 1, wherein: the system further comprises a pushing module, and the pushing module is used for pushing the matched personal accident risk data to the user.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010696124.3A CN111784526A (en) | 2020-07-20 | 2020-07-20 | Personalized recommendation method for personal accident risk |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010696124.3A CN111784526A (en) | 2020-07-20 | 2020-07-20 | Personalized recommendation method for personal accident risk |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111784526A true CN111784526A (en) | 2020-10-16 |
Family
ID=72763114
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010696124.3A Pending CN111784526A (en) | 2020-07-20 | 2020-07-20 | Personalized recommendation method for personal accident risk |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111784526A (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113191907A (en) * | 2021-04-01 | 2021-07-30 | 青岛全掌柜科技有限公司 | Method for automatically generating contrast information of severe illness danger |
CN114493904A (en) * | 2022-04-18 | 2022-05-13 | 北京合理至臻科技有限公司 | Intelligent core protection wind control method, system, equipment and medium |
CN115797087A (en) * | 2023-01-05 | 2023-03-14 | 优保联(北京)科技有限公司 | Insurance policy escrow system |
CN116402582A (en) * | 2023-04-20 | 2023-07-07 | 新疆益盛鑫创展科技有限公司 | Personalized intelligent recommendation system for insurance products |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108961087A (en) * | 2018-07-13 | 2018-12-07 | 众安在线财产保险股份有限公司 | Insure recommended method, device, computer equipment and computer readable storage medium |
-
2020
- 2020-07-20 CN CN202010696124.3A patent/CN111784526A/en active Pending
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108961087A (en) * | 2018-07-13 | 2018-12-07 | 众安在线财产保险股份有限公司 | Insure recommended method, device, computer equipment and computer readable storage medium |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113191907A (en) * | 2021-04-01 | 2021-07-30 | 青岛全掌柜科技有限公司 | Method for automatically generating contrast information of severe illness danger |
CN114493904A (en) * | 2022-04-18 | 2022-05-13 | 北京合理至臻科技有限公司 | Intelligent core protection wind control method, system, equipment and medium |
CN114493904B (en) * | 2022-04-18 | 2022-06-28 | 北京合理至臻科技有限公司 | Intelligent core protection wind control method, system, equipment and medium |
CN115797087A (en) * | 2023-01-05 | 2023-03-14 | 优保联(北京)科技有限公司 | Insurance policy escrow system |
CN116402582A (en) * | 2023-04-20 | 2023-07-07 | 新疆益盛鑫创展科技有限公司 | Personalized intelligent recommendation system for insurance products |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111784526A (en) | Personalized recommendation method for personal accident risk | |
Agrawal | Sustainability of airlines in India with Covid-19: Challenges ahead and possible way-outs | |
Hall et al. | Characteristics and comparisons of functional assessment indices: disability rating scale, functional independence measure, and functional assessment measure | |
Bem | Gender schema theory and self-schema theory compared: A comment on Markus, Crane, Bernstein, and Siladi's' Self-schemas and gender. | |
US10007717B2 (en) | Clustering communications based on classification | |
EP3261303B1 (en) | Systems and methods for identifying spam messages using subject information | |
Das et al. | Vehicle consumer complaint reports involving severe incidents: mining large contingency tables | |
US20120324588A1 (en) | Data model optimization | |
Rice et al. | Driver obesity and the risk of fatal injury during traffic collisions | |
US20180081992A1 (en) | Determination of relationships between collections of disparate media types | |
WO2021121252A1 (en) | Comment-based behavior prediction | |
CN109445759A (en) | Generation method, device, computer equipment and the storage medium of insurance products | |
CN106920136A (en) | The method that the special ticket of shipping is issued | |
He et al. | Road traffic injury mortality and morbidity by country development status, 2011-2017 | |
CN112699643B (en) | Method for generating language model and automatic article generation method | |
CN112734259A (en) | Configurable seat adjustment method, device, storage medium and equipment | |
CN102984176A (en) | Identification method and system for junk mail | |
CN107122381B (en) | File generation method and device and data analysis method and device | |
Burns et al. | Review of aeromedical intra-aortic balloon pump retrieval in New South Wales | |
CN111813823A (en) | Insurance service policy adjustment system, vehicle-mounted recording device and server | |
CN109711125A (en) | A kind of unique identities identification and device | |
CN107016556A (en) | Data processing method and device | |
Kroeker et al. | Injury comparisons between paired drivers and front‐seat passengers in frontal collisions using publicly available crash and injury data | |
McCauley et al. | Injury patterns, imaging usage, and disparities associated with car restraint use in children | |
Macy et al. | Child passenger restraints in relation to other second-row passengers: An analysis of the 2007–2009 national survey of the use of booster seats |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201016 |