CN109300050A - Insurance method for pushing, device and storage medium based on user's portrait - Google Patents
Insurance method for pushing, device and storage medium based on user's portrait Download PDFInfo
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Abstract
The embodiment of the invention provides a kind of insurance method for pushing, device and storage mediums based on user's portrait.The historical trading data that the embodiment of the present invention passes through acquisition user, then, utilize machine learning algorithm and feature extraction algorithm, the historical trading data is handled, the insurance transaction portrait of the user is obtained, the insurance transaction portrait is inclined to degree for the transaction of each insurance to characterize the user, thus, it is traded and is drawn a portrait according to the insurance, pushed and insure to the user.Therefore, the mode that technical solution provided in an embodiment of the present invention is able to solve artificial push insurance in the prior art there are problems that recommending accuracy poor and heavy workload.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of computers, in particular to an insurance pushing method and device based on user portrait and a storage medium.
[ background of the invention ]
At present, when a user needs to purchase insurance, insurance types which are more inclined to trade by the user are generally judged by an insurance salesperson in a subjective judgment mode according to the requirements and work experience of the user, and the insurance types are explained and introduced to the user. Based on this, the insurance introduction in the prior art is generally done by the insurance sales staff through manual judgment.
However, the insurance mode for manually judging the user requirements mainly takes subjective judgment of insurance sales personnel as the standard, which depends on experience and subjective judgment of the insurance sales personnel greatly, and the workload is large, and meanwhile, due to poor judgment accuracy of the insurance required by the user, the recommendation needs to be repeatedly judged, and the workload is further increased.
[ summary of the invention ]
In view of this, embodiments of the present invention provide a method and an apparatus for pushing insurance based on a user portrait, and a storage medium, so as to solve the problems of poor recommendation accuracy and large workload in a manual insurance pushing manner in the prior art.
In a first aspect, an embodiment of the present invention provides an insurance pushing method based on a user portrait, including:
acquiring historical transaction data of a user;
processing the historical transaction data by utilizing a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction portrait of the user, wherein the insurance transaction portrait is used for representing transaction tendency degrees of the user for various insurance;
and pushing insurance to the user according to the insurance transaction portrait.
The above-mentioned aspects and any possible implementation manners further provide an implementation manner that the processing the historical transaction data by using a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction portrait of the user, including:
processing the historical transaction data by using the feature extraction algorithm to obtain historical transaction features and transaction tendency features of the historical transaction data;
taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a neural network mechanism to obtain an insurance transaction probability prediction model;
and predicting each insurance through the insurance transaction probability prediction model to obtain the transaction tendency degree of the user aiming at each insurance to be used as the insurance transaction portrait.
The above-mentioned aspects and any possible implementation manners further provide an implementation manner, where the deep learning is performed by using a neural network mechanism with the historical transaction characteristics as input and the transaction tendency characteristics as output, so as to obtain an insurance transaction probability prediction model, including:
and taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using at least one of a random forest algorithm and a decision tree machine algorithm to obtain the insurance transaction probability prediction model.
The above-described aspects and any possible implementations further provide an implementation in which pushing insurance to the user based on the insurance transaction representation includes:
determining a target insurance according to the insurance transaction image;
and pushing the target insurance to the user.
The above-described aspects and any possible implementations further provide an implementation in which determining a target insurance from the insurance transaction representation includes:
sequencing all the insurance according to the sequence that the transaction tendency degree of the user aiming at all the insurance is from high to low;
and acquiring a specified number of insurance from front to back in the sorting to serve as the target insurance, wherein the specified number is an integer larger than 0.
The above-described aspects and any possible implementations further provide an implementation in which the pushing the target insurance to the user includes:
pushing the target insurance list to the user; alternatively, the target insurance is output in sequence.
One of the above technical solutions has the following beneficial effects:
in the embodiment of the invention, the insurance transaction portrait which accords with the transaction tendency of the user aiming at each insurance can be obtained by analyzing the historical transaction data of the user and carrying out feature extraction and machine learning algorithm processing on the historical transaction data, so that the insurance which accords with the transaction tendency of the user better can be pushed to the user.
In a second aspect, an embodiment of the present invention provides an insurance pushing apparatus based on a user portrait, including:
the acquisition unit is used for acquiring historical transaction data of a user;
the processing unit is used for processing the historical transaction data by utilizing a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction portrait of the user, and the insurance transaction portrait is used for representing transaction tendency degrees of the user for various insurance;
and the pushing unit is used for pushing insurance to the user according to the insurance transaction portrait.
The above-described aspect and any possible implementation further provide an implementation, where the processing unit is specifically configured to:
processing the historical transaction data by using the feature extraction algorithm to obtain historical transaction features and transaction tendency features of the historical transaction data;
taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a neural network mechanism to obtain an insurance transaction probability prediction model;
and predicting each insurance through the insurance transaction probability prediction model to obtain the transaction tendency degree of the user aiming at each insurance to be used as the insurance transaction portrait.
The above-mentioned aspect and any possible implementation further provide an implementation, where the processing unit is further specifically configured to:
and taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using at least one of a random forest algorithm and a decision tree machine algorithm to obtain the insurance transaction probability prediction model.
The above-described aspect and any possible implementation further provide an implementation, where the pushing unit is configured to:
determining a target insurance according to the insurance transaction image;
and pushing the target insurance to the user.
The above-mentioned aspect and any possible implementation manner further provide an implementation manner, where the pushing unit is specifically configured to:
sequencing all the insurance according to the sequence that the transaction tendency degree of the user aiming at all the insurance is from high to low;
and acquiring a specified number of insurance from front to back in the sorting to serve as the target insurance, wherein the specified number is an integer larger than 0.
The above-mentioned aspect and any possible implementation manner further provide an implementation manner, where the pushing unit is specifically configured to:
pushing the target insurance list to the user; alternatively, the target insurance is output in sequence.
One of the above technical solutions has the following beneficial effects:
in the embodiment of the invention, the insurance transaction portrait which accords with the transaction tendency of the user aiming at each insurance can be obtained by analyzing the historical transaction data of the user and carrying out feature extraction and machine learning algorithm processing on the historical transaction data, so that the insurance which accords with the transaction tendency of the user better can be pushed to the user.
In a third aspect, an embodiment of the present invention provides an insurance pushing apparatus based on a user portrait, including: memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a storage medium, including: computer-executable instructions which, when executed, are operable to perform a user representation-based insurance push method as claimed in any one of the first aspects.
One of the above technical solutions has the following beneficial effects:
in the embodiment of the invention, the insurance transaction portrait which accords with the transaction tendency of the user aiming at each insurance can be obtained by analyzing the historical transaction data of the user and carrying out feature extraction and machine learning algorithm processing on the historical transaction data, so that the insurance which accords with the transaction tendency of the user better can be pushed to the user.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flowchart illustrating a first embodiment of a user-portrait-based insurance pushing method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a second embodiment of an insurance pushing method based on a user representation according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a third embodiment of an insurance pushing method based on a user representation according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of an insurance pushing apparatus based on a user profile according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a physical device of an insurance pushing device based on a user representation according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all 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.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
Aiming at the problems of poor judgment accuracy and large workload in the mode of manually judging the insurance requirements of users in the prior art, the embodiment of the invention provides the following solution ideas: the method comprises the steps of obtaining historical transaction data of a user, processing the transaction data to obtain transaction tendency degrees of the user aiming at various insurance, and forming an insurance transaction portrait of the user.
Under the guidance of this idea, the present embodiment provides the following feasible embodiments.
Example one
The embodiment of the invention provides an insurance pushing method based on user portrait. With particular reference to fig. 1, the method may comprise the steps of:
s102, obtaining historical transaction data of the user.
In an embodiment of the present invention, the historical transaction data includes historical insurance transaction data, wherein the related historical insurance transaction data is transaction data related to insurance services. Specifically, it may include, but is not limited to: car insurance transaction data previously purchased by users, life insurance transaction data purchased between users, and the like.
The historical insurance transaction data can be obtained by inquiring the historical policy stored in the database of the user. Alternatively, they may be obtained by other routes. For example, the user actively provides; for example, the user's public insurance transaction information is obtained through other channels, such as through an insurance industry blockchain, etc.
In a practical scenario, the historical transaction data may also include other historical transaction information that can reflect the transaction habits of the user. In some possible implementation scenarios, other aspects of information may include, but are not limited to: at least one of historical shopping transaction data, historical loan transaction data, and historical fixed asset transaction data. The above-mentioned other historical transaction information may be acquired by any method. For example, the user actively provides; for example, the user's published historical transaction information is obtained through other channels, such as through a transaction industry blockchain, and the like.
And S104, processing the historical transaction data by using a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction image of the user, wherein the insurance transaction image is used for representing the transaction tendency degree of the user aiming at each insurance.
It should be noted that the insurance transaction image according to the embodiment of the present invention may have various expressions. For example, the transaction tendency degree of the user to various types of insurance (such as personal insurance, vehicle insurance and other different types of insurance) can be represented in a preset expression form; alternatively, for example, the user's transaction propensity level for each insurance (e.g., for each insurance under the life insurance) may also be characterized in a predetermined form of representation. The embodiment of the present invention is not particularly limited to the preset expression form of the transaction tendency degree. For example, it may be embodied in a proportional fraction, such as a percentage, or it may be embodied in a positive inclination and a negative inclination, or it may be embodied. In actual use, the setting can be carried out according to the requirement.
S106, pushing insurance to the user according to the insurance transaction image.
Based on the historical transaction information acquired in S102, the following implementation manner of executing the step S104 is provided in the embodiment of the present invention, and referring to fig. 2, the step S104 specifically includes the following steps:
and S1042, processing the historical transaction data by using a feature extraction algorithm to obtain historical transaction features and transaction tendency features of the historical transaction data.
The feature extraction algorithm involved in step S1042 may include, but is not limited to: the N-Gram algorithm.
For example, if the historical transaction data is 'X element of purchase accident risk', feature extraction can be performed on the historical transaction data based on an N-Gram algorithm to obtain corresponding Unigram features, Bigram features, Trigram features and the like; wherein each Unigram feature contains one word, each Bigram feature contains two words, and each Trigram feature contains three words. Based on this, the historical transaction characteristics that can be extracted after the above historical transaction data is processed by the N-Gram algorithm may include, but are not limited to: buy, love, exterior, insurance, X, yuan, buy, accident, X yuan and accident insurance, etc.; the expression form of the obtained trading tendency characteristics can be trading probability, or can be simply expressed as a positive tendency and a negative tendency, wherein the positive tendency indicates that there is a trading tendency, and the negative tendency indicates that there is no trading tendency.
In a specific implementation scenario, the historical transaction features obtained by using the feature extraction algorithm may include, but are not limited to: purchase amount, frequency of purchases, risk categories of purchases, etc.
And S1044, taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a neural network mechanism to obtain an insurance transaction probability prediction model.
And S1046, predicting each insurance through the insurance transaction probability prediction model to obtain the transaction tendency degree of the user aiming at each insurance to be used as an insurance transaction image.
The machine learning algorithm involved in step S1044 may include, but is not limited to: the decision tree machine algorithm and the random forest machine algorithm are realized in two modes.
Firstly, executing the step S1044 by a decision tree machine algorithm: and taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a decision tree machine algorithm to obtain an insurance transaction probability prediction model.
The Decision Tree (Decision Tree) is a probability analysis method, and in machine learning, the Decision Tree is used as a prediction model for representing a mapping relation between an object attribute and an object value. Each node in the tree represents an object and each divergent path represents a possible attribute value, and each leaf node corresponds to the value of the object represented by the path traversed from the root node to that leaf node. In the embodiment of the invention, historical transaction data of the user can be used as sample data, and the sample data is trained by a preset decision tree model when encountering, so that the insurance transaction probability prediction model of the user can be obtained.
Therefore, the historical transaction characteristics are processed through the decision tree machine algorithm, an insurance transaction probability model of the user can be obtained, and the insurance transaction probability model can achieve the transaction tendency degree of the user for insurance. In this implementation scenario, subsequently, when S1046 is implemented specifically, the insurance transaction probability prediction model may be used to predict the transaction tendency degree of each insurance.
In the implementation mode, the insurance transaction portrait of the user is obtained through the decision tree machine algorithm, understanding and implementation are easy, the requirement on processing of sample data is low, analysis on big data can be achieved in a short time, and the analysis accuracy is high.
And secondly, executing S1044 through a random forest algorithm, taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning through the random forest algorithm to obtain an insurance transaction probability prediction model.
The random forest belongs to a bagging algorithm in Ensemble Learning (Ensemble Learning), and is mainly a combined algorithm of the bagging algorithm and a decision tree algorithm. Wherein, the bagging algorithm process is approximately as follows: randomly extracting n training samples from the original sample set by using a bootstrapping method, and performing k rounds of extraction to obtain k training sets. (k training sets are independent of each other, and there may be repetitions of elements), then, for k training sets, we train k models (these k models may be specific to a particular problem, such as decision tree, knn, etc.), so that, for the classification problem: generating a classification result by voting; for the regression problem: the average of the k model predictors is used as the final predictor.
Because the random forest algorithm can process a large number of input variables, when a forest is built, the generalized error can be estimated without deviation, and the machine algorithm can balance the error for an unbalanced classified data set, so that the user insurance transaction image obtained by the random forest algorithm is more accurate due to the characteristics; and because the learning speed is fast, the data processing efficiency is high.
In a specific implementation of S1044, S104 may be performed in one implementation or a combination of at least two implementations described above.
Based on this, when S106 is executed, it can refer to fig. 3, including the following steps:
s1062, determining the target insurance according to the insurance transaction image.
Wherein the number of target insureds determined by this step is at least one. For example, in some scenarios, multiple target insurance may also be determined in order to extend the rate of return.
And S1064, pushing the target insurance to the user.
In the step S1062, the determining the target insurance mode according to the insurance transaction image may include the following steps:
sequencing all the insurance according to the sequence that the transaction tendency degree of the user aiming at all the insurance is from high to low;
and acquiring a specified number of insurance from front to back in the sorting to serve as target insurance, wherein the specified number is an integer larger than 0.
Specifically, the specified number may be set as needed. If the designated number is one, acquiring one insurance with the top ranking as a target insurance; or when the specified number is N and N is an integer greater than 1, acquiring N insurance in the top order as the target insurance.
Therefore, the target insurance can be obtained and determined.
Based on this, when the step S1064 is executed, the target insurance list may be pushed to the user; alternatively, the target insurance is output in sequence.
The technical scheme of the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the insurance transaction portrait which accords with the transaction tendency of the user aiming at each insurance can be obtained by analyzing the historical transaction data of the user and carrying out feature extraction and machine learning algorithm processing on the historical transaction data, so that the insurance which accords with the transaction tendency of the user better can be pushed to the user.
Example two
Based on the insurance pushing method based on the user portrait provided by the first embodiment, the embodiment of the invention further provides an embodiment of a device for realizing the steps and the method in the embodiment of the method.
In one aspect, an embodiment of the present invention provides an insurance pushing apparatus based on a user profile. Specifically, referring to fig. 4, the user profile-based insurance pushing apparatus 400 includes:
an acquisition unit 41 for acquiring historical transaction data of a user;
the processing unit 42 is used for processing historical transaction data by utilizing a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction image of the user, wherein the insurance transaction image is used for representing the transaction tendency degree of the user aiming at each insurance;
and the pushing unit 43 is used for pushing insurance to the user according to the insurance transaction image.
Wherein, the processing unit 42 is specifically configured to:
processing the historical transaction data by using the feature extraction algorithm to obtain historical transaction features and transaction tendency features of the historical transaction data;
taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a neural network mechanism to obtain an insurance transaction probability prediction model;
and predicting each insurance through the insurance transaction probability prediction model to obtain the transaction tendency degree of the user aiming at each insurance to be used as the insurance transaction portrait.
In a specific implementation process, the processing unit 42 is further specifically configured to:
and taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using at least one of a random forest algorithm and a decision tree machine algorithm to obtain the insurance transaction probability prediction model.
In this embodiment of the present invention, the pushing unit 43 is configured to:
determining a target insurance according to the insurance transaction image;
and pushing the target insurance to the user.
In one aspect, in the step of determining a target insurance based on the insurance transaction image, the pushing unit 43 may be specifically configured to:
sequencing all the insurance according to the sequence that the transaction tendency degree of the user aiming at all the insurance is from high to low;
and acquiring a specified number of insurance from front to back in the sorting to serve as target insurance, wherein the specified number is an integer larger than 0.
On the other hand, in the step of pushing the target insurance to the user, the pushing unit 43 may include, but is not limited to, the following processing manners:
pushing the target insurance list to the user; or,
and outputting each target insurance in turn.
The historical transaction data according to the embodiment of the present invention further includes: at least one of historical shopping transaction data, historical loan transaction data, and historical fixed asset transaction data.
In addition, an embodiment of the present invention further provides a response device based on deep learning, please refer to fig. 5, in which the insurance pushing device 500 based on user portrait includes: a memory 51, a processor 52, and a computer program stored in the memory 51 and executable on the processor 52, wherein the processor 52 implements the steps of the user-representation-based insurance push method according to any one of the embodiments when the computer program is executed.
In another aspect, an embodiment of the present invention provides a storage medium, including: computer-executable instructions, when executed, perform a user representation-based insurance push method as in any one of the embodiments.
Since each unit in this embodiment can execute the method shown in the first embodiment, reference may be made to the related description of the first embodiment for a part of this embodiment that is not described in detail.
The technical scheme of the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the insurance transaction portrait which accords with the transaction tendency of the user aiming at each insurance can be obtained by analyzing the historical transaction data of the user and carrying out feature extraction and machine learning algorithm processing on the historical transaction data, so that the insurance which accords with the transaction tendency of the user better can be pushed to the user.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A user profile-based insurance push method, the method comprising:
acquiring historical transaction data of a user;
processing the historical transaction data by utilizing a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction portrait of the user, wherein the insurance transaction portrait is used for representing transaction tendency degrees of the user for various insurance;
and pushing insurance to the user according to the insurance transaction portrait.
2. The method of claim 1, wherein processing the historical transaction data using a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction representation of the user comprises:
processing the historical transaction data by using the feature extraction algorithm to obtain historical transaction features and transaction tendency features of the historical transaction data;
taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a neural network mechanism to obtain an insurance transaction probability prediction model;
and predicting each insurance through the insurance transaction probability prediction model to obtain the transaction tendency degree of the user aiming at each insurance to be used as the insurance transaction portrait.
3. The method of claim 2, wherein the obtaining of the insurance transaction probability prediction model by using the historical transaction characteristics as input and the transaction tendency characteristics as output and performing deep learning by using a neural network mechanism comprises:
and taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using at least one of a random forest algorithm and a decision tree machine algorithm to obtain the insurance transaction probability prediction model.
4. A method according to any one of claims 1 to 3, wherein said pushing insurance to said user based on said insurance transaction representation comprises:
determining a target insurance according to the insurance transaction image;
and pushing the target insurance to the user.
5. The method of claim 4, wherein determining a target insurance from the insurance transaction representation comprises:
sequencing all the insurance according to the sequence that the transaction tendency degree of the user aiming at all the insurance is from high to low;
and acquiring a specified number of insurance from front to back in the sorting to serve as the target insurance, wherein the specified number is an integer larger than 0.
6. The method of claim 4, wherein the pushing the target insurance to the user comprises:
pushing the target insurance list to the user; alternatively, the target insurance is output in sequence.
7. An insurance pushing apparatus based on user portrait, the apparatus comprising:
the acquisition unit is used for acquiring historical transaction data of a user;
the processing unit is used for processing the historical transaction data by utilizing a machine learning algorithm and a feature extraction algorithm to obtain an insurance transaction portrait of the user, and the insurance transaction portrait is used for representing transaction tendency degrees of the user for various insurance;
and the pushing unit is used for pushing insurance to the user according to the insurance transaction portrait.
8. The apparatus according to claim 7, wherein the processing unit is specifically configured to:
processing the historical transaction data by using the feature extraction algorithm to obtain historical transaction features and transaction tendency features of the historical transaction data;
taking the historical transaction characteristics as input, taking the transaction tendency characteristics as output, and performing deep learning by using a neural network mechanism to obtain an insurance transaction probability prediction model;
and predicting each insurance through the insurance transaction probability prediction model to obtain the transaction tendency degree of the user aiming at each insurance to be used as the insurance transaction portrait.
9. An insurance pushing apparatus based on user portrait, comprising: memory, processor and computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when executing the computer program.
10. A storage medium, comprising: computer-executable instructions, when executed, to perform the user representation-based insurance push method of any of claims 1 to 6.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811013020.7A CN109300050A (en) | 2018-08-31 | 2018-08-31 | Insurance method for pushing, device and storage medium based on user's portrait |
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| CN201811013020.7A CN109300050A (en) | 2018-08-31 | 2018-08-31 | Insurance method for pushing, device and storage medium based on user's portrait |
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| CN201811013020.7A Pending CN109300050A (en) | 2018-08-31 | 2018-08-31 | Insurance method for pushing, device and storage medium based on user's portrait |
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| CN110335060A (en) * | 2019-05-20 | 2019-10-15 | 微民保险代理有限公司 | Product information method for pushing, device, storage medium and computer equipment |
| CN110659318A (en) * | 2019-08-15 | 2020-01-07 | 中国平安财产保险股份有限公司 | Big data based strategy pushing method and system and computer equipment |
| CN111080355A (en) * | 2019-12-10 | 2020-04-28 | 支付宝(杭州)信息技术有限公司 | User set display method and device and electronic equipment |
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| CN110659318A (en) * | 2019-08-15 | 2020-01-07 | 中国平安财产保险股份有限公司 | Big data based strategy pushing method and system and computer equipment |
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| CN111080355B (en) * | 2019-12-10 | 2022-12-20 | 蚂蚁胜信(上海)信息技术有限公司 | User set display method and device and electronic equipment |
| CN113362097A (en) * | 2020-03-06 | 2021-09-07 | 北京京东振世信息技术有限公司 | User determination method and device |
| CN113362097B (en) * | 2020-03-06 | 2023-11-07 | 北京京东振世信息技术有限公司 | User determination method and device |
| CN111428885A (en) * | 2020-03-31 | 2020-07-17 | 深圳前海微众银行股份有限公司 | User indexing method in federated learning and federated learning device |
| CN111428885B (en) * | 2020-03-31 | 2021-06-04 | 深圳前海微众银行股份有限公司 | User indexing method in federated learning and federated learning device |
| CN112270350A (en) * | 2020-10-23 | 2021-01-26 | 泰康保险集团股份有限公司 | Organization mechanism image method, device, equipment and storage medium |
| CN112270350B (en) * | 2020-10-23 | 2023-11-21 | 泰康保险集团股份有限公司 | Method, apparatus, device and storage medium for portraying organization |
| CN112395331B (en) * | 2020-11-17 | 2023-10-10 | 平安科技（深圳）有限公司 | User portrait method, device, equipment and medium for credit card customer |
| CN112395331A (en) * | 2020-11-17 | 2021-02-23 | 平安科技（深圳）有限公司 | User portrayal method, apparatus, device and medium for credit card client |
| US11556935B1 (en) | 2021-07-28 | 2023-01-17 | International Business Machines Corporation | Financial risk management based on transactions portrait |
| CN113850623A (en) * | 2021-09-27 | 2021-12-28 | 中国人寿保险股份有限公司上海数据中心 | Screening method for potential life insurance long-risk customers in vehicle insurance customer group |
| CN117078421A (en) * | 2023-08-23 | 2023-11-17 | 江苏中创供应链服务有限公司 | User big data portrait generation method and device for transaction scene |
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