CN109615162A - User grouping processing method and processing device, electronic equipment and storage medium - Google Patents

User grouping processing method and processing device, electronic equipment and storage medium Download PDF

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CN109615162A
CN109615162A CN201811237294.4A CN201811237294A CN109615162A CN 109615162 A CN109615162 A CN 109615162A CN 201811237294 A CN201811237294 A CN 201811237294A CN 109615162 A CN109615162 A CN 109615162A
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user
grouping
insurance
group
similarity
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龙撷宇
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OneConnect Smart Technology Co Ltd
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OneConnect Smart Technology Co Ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
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    • G06Q10/0631Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311Scheduling, planning or task assignment for a person or group
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08Insurance

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Abstract

The disclosure is directed to a kind of user grouping processing method and processing device, electronic equipment and storage mediums, are related to field of computer technology, can be applied to the big data analysis scene of mass users information.This method comprises: being grouped in advance according to insurance type to the user of a user group, get the corresponding preset attribute requirement of attribute information of user in each pre- grouping, calculate user's ratio that insurance is bought in the historical user for meeting the preset attribute requirement in each pre- grouping, and the specific gravity required according to the proportional arrangement preset attribute, specific gravity corresponding with its is required to construct user grouping model according to the preset attribute, to be grouped using the grouping model to user.The disclosure can effectively be grouped user, and can recommend the better suited type of insurance for user.

Description

User grouping processing method and processing device, electronic equipment and storage medium
Technical field
This disclosure relates to field of computer technology, at a kind of user grouping processing method, user grouping Manage device, electronic equipment and storage medium.
Background technique
As the development of social economy and the growing good life of people need, more and more people start to buy Insurance, to cope with the following unknown event that may occur.
In face of huge Insurance User group, the operator of insurance industry needs effectively to manage it, thus more preferably Ground provides service for these users.For insurance business, the information of Insurance User is only subjected to simple storage, not User information is made full use of to be grouped processing to it.
For each insurance kind, not yet there is the scheme how to be effectively grouped to user at present and therefore mentioned for user Purchase scheme cannot be insured for specific user's rapid development during for insurance service, so that user experience is deteriorated.
It should be noted that information is only used for reinforcing the reason to the background of the disclosure disclosed in above-mentioned background technology part Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
The disclosure be designed to provide a kind of user grouping processing method, user grouping processing unit, electronic equipment with And computer readable storage medium, and then overcome what can not effectively be grouped to Insurance User to ask at least to a certain extent Topic.
According to the disclosure in a first aspect, providing a kind of user grouping processing method, comprising: by insurance type to a user The user of group is grouped in advance;Obtain preset attribute requirement corresponding to the attribute information of user in each pre- grouping;It calculates each pre- Meet the ratio that the user of insurance is bought in the user of the preset attribute requirement in grouping, and default according to the proportional arrangement The weight of attribute specification;It is required according to the preset attribute and corresponding weight constructs grouping model, to utilize the grouping Model is grouped user.
Optionally, the user grouping processing method further include: obtain the information to Add User, Add User described Information input is to the grouping model, with the corresponding user group that Adds User described in determination, and described Add User is added to In the user group.
Optionally, the corresponding user group that Adds User described in determining comprise determining that the attribute information to Add User with The similarity of the attribute information of the user of the user group;Add User corresponding user according to similarity determination Group.
Optionally, if the corresponding user group that Adds User according to similarity determination includes: the similarity Greater than the first default similarity, it is determined that the corresponding user group that Adds User is the first user group;If the similarity Between the described first default similarity and the second default similarity, it is determined that the corresponding user group that Adds User is the Two user groups;If the similarity is less than the described second default similarity, it is determined that the corresponding user group that Adds User For third user group.
Optionally, the user grouping processing method further include: by the information to Add User and the newly-increased use The information of family owning user group is sent to business personnel, so that the business personnel is true according to the owning user group that Adds User Make traffic direction.
Optionally, the user grouping processing method further include: chosen from customer data base bought insurance at random User and the user for having intention purchase insurance;The user of selection is constituted into the user group.
Optionally, the preset attribute requirement is comprised determining that according to the weight that the proportional arrangement preset attribute requires Weight coefficient;The weight that the ratio of the ratio and the weight coefficient is required as the preset attribute.
According to the second aspect of the disclosure, a kind of user grouping processing unit is provided, comprising: the pre- grouping module of user is used The user of one user group is grouped in advance in by insurance type;Attribute specification obtains module, uses for obtaining in each pre- grouping Preset attribute corresponding to the attribute information at family is wanted;Weight configuration module meets the default category for calculating in each pre- grouping Property require user in buy insurance user ratio, and according to the proportional arrangement preset attribute require weight;Grouping Model construction module constructs grouping model with corresponding weight for requiring according to the preset attribute, to utilize described point Group model is grouped user.
Optionally, the user grouping processing unit further include: Add User grouping module.
Specifically, the grouping module that Adds User is for obtaining the information to Add User, the information to Add User is defeated Enter to the grouping model, with the corresponding user group that Adds User described in determination, and described Add User is added to the use In the group of family.
Optionally, the grouping module that Adds User includes: similarity determining unit and the processing unit that Adds User.
Specifically, the user of attribute information and the user group of the similarity determining unit for Adding User described in determination Attribute information similarity;The processing unit that Adds User is used for the corresponding use that Adds User according to similarity determination Family group.
Optionally, the processing unit that Adds User includes: and is grouped to determine subelement.
If determining that subelement is greater than the first default similarity for the similarity specifically, being grouped, it is determined that described The corresponding user group that Adds User is the first user group;If the similarity is pre- with second between the described first default similarity If between similarity, it is determined that the corresponding user group that Adds User is second user group;If the similarity is less than institute State the second default similarity, it is determined that the corresponding user group that Adds User is third user group.
Optionally, the grouping module that Adds User includes group result processing module.
Specifically, group result processing module is used for the information to Add User and the affiliated use that Adds User The information of family group is sent to business personnel, so that the business personnel determines business according to the owning user group that Adds User Direction.
Optionally, the user grouping processing unit further include: user group chooses module.
It has bought the user of insurance for being chosen from customer data base at random specifically, user group chooses module and has had The user of intention purchase insurance, constitutes the user group for the user of selection.
Optionally, the weight configuration module includes: proportional roles configuration unit.
Specifically, proportional roles configuration unit is used to determine the weight coefficient of the preset attribute requirement, by the ratio The weight required with the ratio of the weight coefficient as the preset attribute.
According to the third aspect of the disclosure, a kind of electronic equipment is provided, comprising: processor;And memory, the storage It is stored with computer-readable instruction on device, is realized when the computer-readable instruction is executed by the processor according to above-mentioned any Method described in one.
According to the fourth aspect of the disclosure, a kind of computer readable storage medium is provided, computer program is stored thereon with, The method according to above-mentioned any one is realized when the computer program is executed by processor.
User grouping processing method in the exemplary embodiment of the disclosure, by insurance type to the user of a user group into The pre- grouping of row;Obtain preset attribute requirement corresponding to the attribute information of user in each pre- grouping;It calculates and meets in each pre- grouping The ratio of the user of insurance is bought in the user that the preset attribute requires, and required according to the proportional arrangement preset attribute Weight;According to the preset attribute require and corresponding weight construct grouping model, and using the grouping model to user into Row grouping.On the one hand, all users can be effectively grouped by this user grouping processing method, facilitates manager Management to user;On the other hand, suitable insurance products can be recommended to user by result, not only increases Insurance Management The working efficiency of person, and user experience is also highly improved.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not The disclosure can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure Example, and together with specification for explaining the principles of this disclosure.It should be evident that the accompanying drawings in the following description is only the disclosure Some embodiments for those of ordinary skill in the art without creative efforts, can also basis These attached drawings obtain other attached drawings.In the accompanying drawings:
Fig. 1 diagrammatically illustrates the flow chart of user grouping processing method according to an exemplary embodiment of the present disclosure;
Fig. 2 shows user grouping processing method according to an exemplary embodiment of the present disclosure using grouping model into The schematic diagram of row classification;
Fig. 3 diagrammatically illustrates the block diagram of user grouping processing unit according to an exemplary embodiment of the present disclosure;
Fig. 4 diagrammatically illustrates the box of the user grouping processing unit of the another exemplary embodiment according to the disclosure Figure;
Fig. 5 diagrammatically illustrates the block diagram of the grouping module that Adds User according to an exemplary embodiment of the present disclosure;
Fig. 6 diagrammatically illustrates the block diagram of the processing unit that Adds User according to an exemplary embodiment of the present disclosure;
Fig. 7 diagrammatically illustrates the box of the user grouping processing unit of another illustrative embodiments according to the disclosure Figure;
Fig. 8 diagrammatically illustrates the box of the user grouping processing unit according to the another exemplary embodiment of the disclosure Figure;
Fig. 9 diagrammatically illustrates the block diagram of weight configuration module according to an exemplary embodiment of the present disclosure;
Figure 10 diagrammatically illustrates the block diagram of the electronic equipment according to one exemplary embodiment of the disclosure;And
Figure 11 diagrammatically illustrates the schematic diagram of the computer readable storage medium according to one exemplary embodiment of the disclosure.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be real in a variety of forms It applies, and is not understood as limited to embodiment set forth herein;On the contrary, thesing embodiments are provided so that the disclosure will be comprehensively and complete It is whole, and the design of example embodiment is comprehensively communicated to those skilled in the art.Identical appended drawing reference indicates in figure Same or similar part, thus repetition thereof will be omitted.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner In example.In the following description, many details are provided to provide and fully understand to embodiment of the disclosure.However, It will be appreciated by persons skilled in the art that can be with technical solution of the disclosure without one in the specific detail or more It is more, or can be using other methods, constituent element, device, step etc..In other cases, known in being not shown in detail or describing Structure, method, apparatus, realization, material or operation are to avoid fuzzy all aspects of this disclosure.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity. I.e., it is possible to realize these functional entitys using software form, or these are realized in the module of one or more softwares hardening A part of functional entity or functional entity, or realized in heterogeneous networks and/or processor device and/or microcontroller device These functional entitys.
Insurance industry in the related technology, after having collected user information, fails to carry out the attribute information of user abundant Analysis and excavation, so failing for the user of insurance industry to be grouped according to default rule, in the realization of user management On have certain problems, cannot sufficiently excavate the insured intention of user, recommend suitable insurance products, final shadow for user The usage experience for ringing Insurance User, hinders the fast development of entire insurance industry.
Based on this, in this exemplary embodiment, a kind of user grouping processing method is provided firstly, can use server It realizes the user grouping processing method of the disclosure, can use terminal device also to realize method described in the disclosure, wherein The terminal device can be for example the various electronic equipments such as mobile phone, computer, PDA.With reference to Fig. 1, the user grouping processing method It may comprise steps of:
S110. the user of one user group is grouped in advance by insurance type.
It, can be according to insurance type in this example embodiment, it may be assumed that insurance kind (such as: life insurance, vehicle insurance, personal accident danger) The user of one user group is grouped in advance.The user sources of user group may include: to have bought the user of corresponding insurance kind, have Purchase intention but the user for not yet buying insurance can also include the other users that the type of insurance was understood by any approach. The user of above-mentioned user sources is chosen from customer data base at random as a user group.For example, in a certain user group User can be the user for having bought corresponding insurance kind, has purchase intention but both users for not yet buying insurance combine shape At user.Suppose there is includes 1000 users in a user group, includes having bought a certain insurance in this 1000 users These users are grouped by two kinds for not buying this insurance in advance according to corresponding insurance kind.
Certainly, it will be readily appreciated by those skilled in the art that can also be selected in other exemplary embodiments of the disclosure It takes voluntary insurance insurance kind to be grouped the user in user group in advance, does not do particular determination in the present exemplary embodiment to this.
S120. preset attribute requirement corresponding to the attribute information of user in each pre- grouping is obtained.
In this example embodiment, after be grouped in advance to the user in user group, user in each pre- grouping is obtained Attribute information corresponding to preset attribute requirement.For example, the attribute information of user refers to the association attributes of individual subscriber, It may include: gender, at the age, annual income, whether have children, whether have vehicle etc., it can be by developer for each attribute Some specific index item are preset, such as: the index item that gender attribute requires is " male ", " female ";What age attribute required Index item can be divided according to age level, can be divided into 0 years old~18 years old, 19 years old~30 years old, 31 years old~60 years old, 60 years old More than, the age cannot be negative value;Annual income can be divided according to income quantity, can be divided are as follows: and 10W or less, 10W~ 20W, 20W~30W, 30W or more.It should be understood that the preset attribute of different insurance kinds can be different, when same insurance kind Preset attribute requires one to be set, and will not change during carrying out user grouping processing.
Further, in addition to the attribute specification provided, such as: outside the division of age bracket, the specific dividing condition of annual income, also It can be directed to specific insurance kind, other any qualified specific divisions are carried out to preset attribute, as long as that is, being capable of abundant land productivity Analysis foundation is provided for the processing of next step with the attribute information of user, belongs to the protection scope of the disclosure.
S130. the ratio that the user of insurance is bought in the user for meeting the preset attribute requirement in each pre- grouping is calculated, And the weight required according to the proportional arrangement preset attribute.
In this example embodiment, for each pre- grouping, calculates to buy in the user for meeting the preset attribute requirement and protect The ratio of the user of danger, can be ranked up calculated ratio according to sequence comparative example from big to small.For example, By taking vehicle insurance is grouped as an example, " whether having vehicle " this attribute is extremely important, when user is under the premise of having vehicle, can just buy vehicle insurance. Has the user of " having vehicle " this attribute, the user's ratio for buying vehicle insurance is very high;In " annual income " this attribute, annual income is got over The ratio that high user buys vehicle insurance will be bigger, and whether " whether having children " and " age " attribute buy vehicle insurance to user Influence it is relatively small, such as: when attribute information is " having vehicle ", the user's ratio for buying vehicle insurance is 90%, and when " no vehicle " is purchased User's ratio of buying car danger is 0%, and " annual income " be " 10W or less ", " 10W~20W ", " 20W~30W ", " 30W or more " user The ratio for buying vehicle insurance is respectively 30%, 50%, 60%, 85%, and ratio difference is relatively small relative to " whether having vehicle ", and " there are children " in " whether there are children " and the ratio of " having no children " purchase vehicle insurance is respectively 60%, 45%, in " age " " 0 years old~ 18 years old ", " 19 years old~30 years old ", " 31 years old~65 years old ", " over-65s " user buy vehicle insurance ratio be 0%, 50%, 70%, 5%.It is distributed and is compared according to the user's ratio under the different situations of Attribute transposition, buying vehicle insurance, can matched according to the above ratio Set the weight of each preset attribute requirement.
In this example embodiment, it is the smallest as benchmark to can choose ratio, to determine the specific gravity of other attribute informations. For example, " whether there will be children " as benchmark, it is set to 1, then " age " is set as 1.1, and " annual income " is set as 1.4, " whether has Vehicle " is set as 1.8;It can be concluded that, the weight of " whether having vehicle " attribute is larger relative to the weight of " annual income " attribute in this way, And " whether having children " and " age " will configure relatively small value in judging weight that whether user buys vehicle insurance.
Certainly, it will be readily appreciated by those skilled in the art that the different type of insurances is directed to, in other examples of the disclosure In property embodiment, the weight of the preset attribute requirement can be configured by being applicable in the other modes of this insurance kind, to reach The effect of foundation is provided to for next step building grouping model, does not do particular determination to this in the present exemplary embodiment.
S140. it is required according to the preset attribute and corresponding weight constructs grouping model, to utilize the grouping mould Type is grouped user.
In this example embodiment, the preset attribute is required and corresponding weight is used to construct grouping model together, Attribute information that the grouping model is analyzed using above-mentioned steps and weight etc. construct can be by the division that arbitrarily Adds User To most suitable grouping, to carry out subsequent work.Specific partition process is referred to shown in Fig. 2, and 210 be use in Fig. 2 Family input, user information is input in grouping model 220, and user information passes through the calculating of grouping model, user can be returned Class is into different insurance kinds 230, and finally according to the integrated information of user, user is divided into a kind of specific a certain grouping of insurance kind In 240.
According to some embodiments of the present disclosure, grouping model can be realized using neural network, for example, can be using general Rate neural network (Probabilistic Neural Network, PNN), that is to say, that can be using PNN network as above-mentioned point Group module.The PNN network may include input layer, hidden layer, summation layer and output layer.Specifically, input layer can be used for by The attribute information input PNN network to Add User, the number of input layer can be identical as the number of user property.Hidden layer with it is defeated Enter layer connection, the matching degree of attribute information and the sample for training the model for calculating input.Summation layer can connect hidden Containing layer, the number of the neuron for layer of summing can be the categorical measure of sample (that is, number of purchase insurance probability), for calculating The probability of each insurance is bought, output layer can be used for exporting summation highest one kind of layer probability, to show that the user should be drawn Which group assigned to.
Furthermore it is possible to the attribute information of the user of the attribute information to Add User according to determining and the user group Similarity determine the corresponding user group that Adds User.For example, the essential attribute information that Adds User when one with When the information similarity of a certain user reaches preset numerical value in user group, it will this is Added User and the user in user group It is assigned in same user group.
Certainly, it will be readily appreciated by those skilled in the art that determination for above-mentioned similarity, can using Euclidean away from From, with a distance from manhatton distance, Ming Shi, cosine similarity isometry means determine, spy is not in the present exemplary embodiment to this It is different to limit.
In this example embodiment, according to the similarity determine described in Add User corresponding user group when, if institute Similarity is stated greater than the first default similarity, it is determined that the corresponding user group that Adds User is the first user group;If institute Similarity is stated between the described first default similarity and the second default similarity, it is determined that the corresponding use that Adds User Family group is second user group;If the similarity is less than the described second default similarity, it is determined that the correspondence that Adds User User group be third user group.The number and numerical value of the default similarity can be according to the differences and insurance of insurance kind The development demand of business is preset.
For example, by taking whether user can buy vehicle insurance as an example, first user group is most possibly to buy the danger Kind user, such as: a possibility that purchase, can reach 60% or more user;The second user group is to have the possibility of purchase A possibility that property, but relative to the first user group, purchase, is smaller, such as: being the use between 30%~60% a possibility that purchase Family;The third user group, which refers to, buys the minimum user of the insurance kind possibility, such as: a possibility that purchase is 30% use below Family.
It should be noted that can be increased or decreased according to needs are divided in other exemplary embodiments of the disclosure The quantity of user group, and the specific value for a possibility that user buys a certain insurance kind can be changed, to reach user's division The variation of specific value of a possibility that optimum efficiency, the variation of user grouping quantity and user buy a certain insurance kind belongs to this Disclosed protection scope.
According to other exemplary embodiments of the disclosure, by the information to Add User and the institute that Adds User The information for belonging to user group is sent to business personnel, so that the business personnel determines according to the owning user group that Adds User Traffic direction.
For example, it one Adds User, the essential information of the user is that annual income is 30W, has vehicle, there is children, not purchasing The user can be divided into the first user of purchase vehicle insurance maximum probability by 35 years old male buying car danger and often gone on business The user can also be divided into the second user group for being possible to purchase accident insurance by group, and business personnel can be for men according to this The information at family and user's owning user group determine the business for recommending vehicle insurance or accident insurance for the user.In another example year receives Enter for 20W, have vehicle, have no children, 28 years old weak and sickly women, which can be divided into the of medical insurance maximum probability One user group, business personnel can determine that one is the use according to the information of the female user and user's owning user group The business of family recommendation medical insurance.
Meanwhile for each insurance kind, it should the attribute information for increasing " whether user has bought ", when user has purchased Corresponding insurance kind was bought, when recommending insurance products to user, just no longer recommends the insurance kind.
For example, one Adds User, the essential information of the user is that annual income is 100W, possesses vehicle, there are children And do not bought the male of vehicle insurance, then the user is divided into the first user group of maximum probability in vehicle insurance, can be referred in this way Leading business personnel is in the future that the user introduces vehicle insurance insurance kind, increases a possibility that user buys insurance.In addition, another It Adds User, the essential information of the user is that annual income is 300W, possesses vehicle, there is children and bought the female of vehicle insurance Property, then the user is divided into the purchase the smallest third user group of vehicle insurance probability.
It should be noted that the same user can buy multiple insurance kinds, then the user can be only assigned to a danger The user group of kind, can also be assigned to the relative users group of multiple insurance kinds simultaneously, these grouping variations belong to the guarantor of the disclosure Protect range.
In conclusion firstly, according to insurance type the user of one user group is grouped in advance, get in each pre- grouping The corresponding preset attribute requirement of the attribute information of user;Meet going through for the preset attribute requirement secondly, calculating in each pre- grouping User's ratio of insurance, and the specific gravity required according to the proportional arrangement preset attribute are bought in history user;Again, according to described Preset attribute requires specific gravity corresponding with its to construct user grouping model.The side classified using the grouping model to user Method, on the one hand, the ratio for the weight for requiring the ratio of the purchase Insurance User and the preset attribute is as preset attribute It is required that weight, and by the similarity of the attribute information that Adds User of comparison and the attribute information of the user of the user group, So that the calculation method for the grouping that Adds User combine it is all can be to the attribute factor that user grouping has an impact;Another aspect institute Stating user group is randomly selected in customer data base, and the accuracy of grouping model training is increased, and improves the classification that Adds User Accuracy;In another aspect, allowing business personnel according to new after being grouped according to the calculated similarity of institute to user It adds family owning user group and determines the traffic direction for being directed to every client, can preferably meet the need that user buys insurance It asks, and then promotes the development of insurance business.
In addition, in this exemplary embodiment, additionally providing a kind of user grouping processing unit.Referring to shown in Fig. 3, the user Block processing device 300 may include the pre- grouping module 310 of user, attribute specification acquisition module 320, weight configuration module 330 And grouping model constructs module 340.
Specifically, the pre- grouping module 310 of user can be used for being grouped the user of a user group in advance by insurance type; Attribute specification, which obtains module 320, can be used for obtaining preset attribute requirement corresponding to the attribute information of user in each pre- grouping; Weight configuration module 330 can be used for calculating the use that insurance is bought in the user for meeting the preset attribute requirement in each pre- grouping The ratio at family, and the weight required according to the proportional arrangement preset attribute;Grouping model building module 340 can be used for basis The preset attribute requires and corresponding weight constructs grouping model, to be grouped using the grouping model to user.
The user grouping processing unit 300 combines all can buy the user that a certain type of insurance has an impact to user Attribute information, and judge the significance level of each attribute information, grouping model is established with this, user is grouped, is a kind of row Effective user grouping processing unit.
According to an exemplary embodiment of the present disclosure, referring to shown in Fig. 4, user grouping processing unit 400 is compared to user Block processing device 300, except including the pre- grouping module 310 of user, attribute specification obtain module 320, weight configuration module 330 with It can also include the grouping module 410 that Adds User and outside grouping model building module 340.
Specifically, this Adds User, grouping module 410 can be used for obtaining the information to Add User, Add User described Information input to the grouping model, with the corresponding user group that Adds User described in determination, and by the addition that Adds User Into the user group.
The grouping module that Adds User 410 can be grouped processing to all Add User, and carry out accordingly to Adding User The other division of group.
It optionally, referring to Figure 5, may include similarity determining unit 510 in the grouping module that Adds User 410 and new Add family processing unit 520.
Specifically, attribute information and the user group of the similarity determining unit 510 for Adding User described in determination The similarity of the attribute information of user;The processing unit 520 that Adds User is used to Add User according to similarity determination Corresponding user group.
By similarity determining unit 510 and the processing unit 520 that Adds User, can compare Add User information with point The similarity degree of relative users data in the training sample of group model, to carry out respective packets to user.
Optionally, the processing unit 520 that Adds User includes being grouped to determine subelement 610, as shown in Figure 6.
If determining that subelement 610 can be used for the similarity greater than the first default similarity, really specifically, being grouped The fixed corresponding user group that Adds User is the first user group;If the similarity between the described first default similarity with Between second default similarity, it is determined that the corresponding user group that Adds User is second user group;If the similarity Less than the described second default similarity, it is determined that the corresponding user group that Adds User is third user group.
It is grouped and determines that subelement 610 according to default similarity, is accurately grouped to Adding User, it is default Similarity value and the group quantity formed according to default similarity are very flexible, Add User so as to preferably meet Grouping requires.
Optionally, referring to shown in Fig. 7, the user grouping processing unit 700 may include group result processing module 710。
Specifically, group result processing module 710 is used for the information to Add User and the institute that Adds User The information for belonging to user group is sent to business personnel, so that the business personnel determines according to the owning user group that Adds User Traffic direction.
Group result processing module 710 is will to feed back to business to the result being grouped that Adds User using grouping model Personnel, business personnel can directly provide for user according to user grouping result and be most suitable for the most desirable insurance service.
Optionally, referring to shown in Fig. 8, the user grouping processing unit 800 may include that user group chooses module 810.
It can be used for choosing the use for having bought insurance from customer data base at random specifically, user group chooses module 810 The user of selection is constituted the user group by family and the user for having intention purchase insurance.
It is to select the characteristics of choosing user from customer data base at random, randomly select that user group, which chooses module 810, The user data taken is more representative, avoids due to data decimation is unscientific, leads to the training process of grouping model There is the case where poor fitting and over-fitting.
Optionally, referring to shown in Fig. 9, the weight configuration module 330 includes: proportional roles configuration unit 3301.
Specifically, proportional roles configuration unit 3301 is used to determine the weight coefficient of the preset attribute requirement, it will be described The weight that the ratio of ratio and the weight coefficient is required as the preset attribute.
Proportional roles configuration unit 3301 determines weight system of each attribute information of user in user grouping model Number, it is possible thereby to which the significance level to each attribute information is arranged according to descending sequence, can improve user grouping The accuracy rate of model.
The detail of each Virtual User block processing device module is at corresponding Virtual User grouping among the above It is described in detail in reason method, therefore details are not described herein again.
It should be noted that although being referred to several modules or list of user grouping processing unit in the above detailed description Member, but this division is not enforceable.In fact, according to embodiment of the present disclosure, it is above-described two or more Module or the feature and function of unit can embody in a module or unit.Conversely, an above-described mould The feature and function of block or unit can be to be embodied by multiple modules or unit with further division.
In addition, in an exemplary embodiment of the disclosure, additionally providing a kind of electronic equipment that can be realized the above method.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or Program product.Therefore, various aspects of the invention can be embodied in the following forms, it may be assumed that complete hardware embodiment, completely Software implementation (including firmware, microcode etc.) or hardware and software in terms of combine embodiment, may be collectively referred to as here Circuit, " module " or " system ".
The electronic equipment 1000 of this embodiment according to the present invention is described referring to Figure 10.The electronics that Figure 10 is shown Equipment 1000 is only an example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in Figure 10, electronic equipment 1000 is showed in the form of universal computing device.The component of electronic equipment 1000 can To include but is not limited to: at least one above-mentioned processing unit 1010, connects not homologous ray at least one above-mentioned storage unit 1020 The bus 1030 of component (including storage unit 1020 and processing unit 1010), display unit 1040.
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 1010 Row, so that various according to the present invention described in the execution of the processing unit 1010 above-mentioned " illustrative methods " part of this specification The step of exemplary embodiment.
Storage unit 1020 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit (RAM) 1021 and/or cache memory unit 1022, it can further include read-only memory unit (ROM) 1023.
Storage unit 1020 can also include program/utility with one group of (at least one) program module 1025 1024, such program module 1025 includes but is not limited to: operating system, one or more application program, other program moulds It may include the realization of network environment in block and program data, each of these examples or certain combination.
Bus 1030 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures Local bus.
Electronic equipment 1000 can also be with one or more external equipments 1070 (such as keyboard, sensing equipment, bluetooth equipment Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 1000 communicate, and/or with make The electronic equipment 1000 can with it is one or more of the other calculating equipment be communicated any equipment (such as router, modulation Demodulator etc.) communication.This communication can be carried out by input/output (I/O) interface 1050.Also, electronic equipment 1000 Network adapter 1060 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public affairs can also be passed through Common network network, such as internet) communication.As shown, network adapter 1060 passes through its of bus 1030 and electronic equipment 1000 The communication of its module.It should be understood that although not shown in the drawings, other hardware and/or software can be used in conjunction with electronic equipment 1000 Module, including but not limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, magnetic Tape drive and data backup storage system etc..
By the description of above embodiment, those skilled in the art is it can be readily appreciated that example embodiment described herein It can also be realized in such a way that software is in conjunction with necessary hardware by software realization.Therefore, implemented according to the disclosure The technical solution of example can be embodied in the form of software products, which can store in a non-volatile memories In medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) or on network, including some instructions are so that a calculating equipment (can To be personal computer, server, terminal installation or network equipment etc.) it executes according to the method for the embodiment of the present disclosure.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is additionally provided, energy is stored thereon with Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention can be with It is embodied as a kind of form of program product comprising program code, it is described when described program product is run on the terminal device Program code is for executing the terminal device described in above-mentioned " illustrative methods " part of this specification according to the present invention The step of various exemplary embodiments.
With reference to shown in Figure 11, the program product for realizing the above method of embodiment according to the present invention is described 1100, can using portable compact disc read only memory (CD-ROM) and including program code, and can in terminal device, Such as it is run on PC.However, program product of the invention is without being limited thereto, in this document, readable storage medium storing program for executing can be with To be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or It is in connection.
Described program product can be using any combination of one or more readable mediums.Readable medium can be readable letter Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or System, device or the device of semiconductor, or any above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive List) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal, In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal, Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing Matter, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or and its The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have Line, optical cable, RF etc. or above-mentioned any appropriate combination.
The program for executing operation of the present invention can be write with any combination of one or more programming languages Code, described program design language include object oriented program language-Java, C++ etc., further include conventional Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network (WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP To be connected by internet).
In addition, above-mentioned attached drawing is only the schematic theory of processing included by method according to an exemplary embodiment of the present invention It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable Sequence.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure His embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or Adaptive change follow the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure or Conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by claim It points out.
It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and And various modifications and changes may be made without departing from the scope thereof.The scope of the present disclosure is only limited by the attached claims.

Claims (10)

1. a kind of user grouping processing method characterized by comprising
The user of one user group is grouped in advance by insurance type;
Obtain preset attribute requirement corresponding to the attribute information of user in each pre- grouping;
The ratio that the user of insurance is bought in the user for meeting the preset attribute requirement in each pre- grouping is calculated, and according to described The weight that proportional arrangement preset attribute requires;
According to the preset attribute require and corresponding weight construct grouping model, so as to using the grouping model to user into Row grouping.
2. user grouping processing method according to claim 1, which is characterized in that the user grouping processing method is also wrapped It includes:
The information to Add User is obtained, it is described newly-increased with determination by the information input to Add User to the grouping model The corresponding user group of user, and described Add User is added in the user group.
3. user grouping processing method according to claim 2, which is characterized in that Add User corresponding use described in determining Family group includes:
The similarity of the attribute information of the user of the attribute information and the user group that Add User described in determination;
Add User corresponding user group according to similarity determination.
4. user grouping processing method according to claim 3, which is characterized in that determined according to the similarity described new Adding the corresponding user group in family includes:
If the similarity is greater than the first default similarity, it is determined that the corresponding user group that Adds User is the first user Group;
If the similarity is between the described first default similarity and the second default similarity, it is determined that the newly-increased use The corresponding user group in family is second user group;
If the similarity is less than the described second default similarity, it is determined that the corresponding user group that Adds User is third User group.
5. user grouping processing method according to any one of claim 2 to 4, which is characterized in that the user grouping Processing method further include:
The information to Add User and the information for Adding User owning user group are sent to business personnel, with toilet It states business personnel and traffic direction is determined according to the owning user group that Adds User.
6. user grouping processing method according to claim 1, which is characterized in that the user grouping processing method is also wrapped It includes:
The user for having bought insurance is chosen from customer data base at random and has intention the user of purchase insurance;
The user of selection is constituted into the user group.
7. user grouping processing method according to claim 1, which is characterized in that according to the proportional arrangement preset attribute It is required that weight include:
Determine the weight coefficient of the preset attribute requirement;
The weight that the ratio of the ratio and the weight coefficient is required as the preset attribute.
8. a kind of user grouping processing unit characterized by comprising
The pre- grouping module of user, for being grouped in advance by insurance type to the user of a user group;
Attribute specification obtains module, for obtaining preset attribute requirement corresponding to the attribute information of user in each pre- grouping;
Weight configuration module, for calculating the user for buying insurance in the user for meeting the preset attribute requirement in each pre- grouping Ratio, and according to the proportional arrangement preset attribute require weight;
Grouping model constructs module, grouping model is constructed with corresponding weight for requiring according to the preset attribute, with convenience User is grouped with the grouping model.
9. a kind of electronic equipment, which is characterized in that including
Processor;And
Memory is stored with computer-readable instruction on the memory, and the computer-readable instruction is held by the processor Method according to any one of claim 1 to 7 is realized when row.
10. a kind of computer readable storage medium, is stored thereon with computer program, the computer program is executed by processor Shi Shixian method according to any one of claim 1 to 7.
CN201811237294.4A 2018-10-23 2018-10-23 User grouping processing method and processing device, electronic equipment and storage medium Pending CN109615162A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110390601A (en) * 2019-06-18 2019-10-29 平安科技(深圳)有限公司 The strong dangerous promotion rate calculation method of meaning based on big data and relevant device
CN110648086A (en) * 2019-10-31 2020-01-03 上海复岸网络信息科技有限公司 Online teaching student grouping method and device
CN113360500A (en) * 2021-06-03 2021-09-07 北京沃东天骏信息技术有限公司 Data processing method and device, storage medium and electronic equipment

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004126975A (en) * 2002-10-03 2004-04-22 Nippon Telegr & Teleph Corp <Ntt> Method and device for extracting similar user with similar preference tendency, program for the same, and recording medium recording the same
CN103198418A (en) * 2013-03-15 2013-07-10 北京亿赞普网络技术有限公司 Application recommendation method and application recommendation system
CN103914783A (en) * 2014-04-13 2014-07-09 北京工业大学 E-commerce website recommending method based on similarity of users
US20170148103A1 (en) * 2015-11-19 2017-05-25 Mastercard International Incorporated Method for generating a precedent risk-mitigation schedule for a customer segment
US20170169447A1 (en) * 2015-12-09 2017-06-15 Oracle International Corporation System and method for segmenting customers with mixed attribute types using a targeted clustering approach
US9749355B1 (en) * 2015-03-25 2017-08-29 Amazon Technologies, Inc. Network packet prioritization
CN107609958A (en) * 2017-10-16 2018-01-19 泰康保险集团股份有限公司 Behavioral guidance strategy determines method and device, storage medium and electronic equipment

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004126975A (en) * 2002-10-03 2004-04-22 Nippon Telegr & Teleph Corp <Ntt> Method and device for extracting similar user with similar preference tendency, program for the same, and recording medium recording the same
CN103198418A (en) * 2013-03-15 2013-07-10 北京亿赞普网络技术有限公司 Application recommendation method and application recommendation system
CN103914783A (en) * 2014-04-13 2014-07-09 北京工业大学 E-commerce website recommending method based on similarity of users
US9749355B1 (en) * 2015-03-25 2017-08-29 Amazon Technologies, Inc. Network packet prioritization
US20170148103A1 (en) * 2015-11-19 2017-05-25 Mastercard International Incorporated Method for generating a precedent risk-mitigation schedule for a customer segment
US20170169447A1 (en) * 2015-12-09 2017-06-15 Oracle International Corporation System and method for segmenting customers with mixed attribute types using a targeted clustering approach
CN107609958A (en) * 2017-10-16 2018-01-19 泰康保险集团股份有限公司 Behavioral guidance strategy determines method and device, storage medium and electronic equipment

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110390601A (en) * 2019-06-18 2019-10-29 平安科技(深圳)有限公司 The strong dangerous promotion rate calculation method of meaning based on big data and relevant device
CN110390601B (en) * 2019-06-18 2023-09-15 平安科技(深圳)有限公司 Method and related equipment for calculating risk promotion rate based on big data
CN110648086A (en) * 2019-10-31 2020-01-03 上海复岸网络信息科技有限公司 Online teaching student grouping method and device
CN113360500A (en) * 2021-06-03 2021-09-07 北京沃东天骏信息技术有限公司 Data processing method and device, storage medium and electronic equipment

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