CN111833078A - Block chain based recommendation method, device, medium and electronic equipment - Google Patents
Block chain based recommendation method, device, medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a recommendation method, a device, a medium and electronic equipment based on a block chain, wherein the recommendation method based on the block chain comprises the following steps: storing historical user information, historical insurance value-added service and historical evaluation information thereof in a block chain; if a new block of the current user information, the current insurance information and the current insurance value-added service is generated in the block chain, triggering execution to obtain a recommendation result of the current insurance value-added service according to the current user information, the current insurance information and the current insurance value-added service and by using a trained recommendation model; the recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof. The technical scheme of the embodiment of the invention can train the recommendation model based on the stored historical data and automatically recommend proper insurance value-added service to the user by utilizing the recommendation model.
Description
Technical Field
The invention relates to the technical field of electrical data processing, in particular to a recommendation method, a recommendation device, a recommendation medium and electronic equipment based on a block chain.
Background
At present, a plurality of insurance companies exist in the market, the competition among the insurance companies is very strong, and in order to attract customers or retain the customers to the maximum extent, the insurance companies can also put forward various insurance value-added services on the basis of the insurance services.
However, various types of insurance value-added services released by the insurance company are specifically required and liked by a certain client, and the insurance company cannot know the requirement, so that the insurance value-added services with pertinence cannot be pushed to the client; if the insurance company adopts an undifferentiated push strategy, namely, every time an insurance value-added service is newly created, a push message is sent to all clients, and the method is 'information harassment' for the clients who do not need the insurance value-added service, so that the aim of improving the user experience is not fulfilled, and the dislike of the clients is caused.
Meanwhile, in the prior art, an insurance company can store a large amount of various relevant data of customers, such as personal identity information, insurance contract information, claim settlement history records and the like, in a database, the existing centralized storage mode is easy to attack, the data storage structure is simple, the data storage structure is easy to tamper, information leakage is easy to occur, and user information is easy to tamper.
Therefore, a new block chain based recommendation method, apparatus, computer readable medium and electronic device are needed.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present invention and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
The embodiment of the invention aims to provide a recommendation method, a recommendation device, a recommendation medium and electronic equipment based on a block chain, and further solves the problems that targeted insurance value-added services cannot be recommended to customers and various related data are stored in a centralized storage mode in the related technology at least to a certain extent.
Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to an aspect of the present disclosure, there is provided a block chain-based recommendation method, including: storing historical user information, historical insurance value-added service and historical evaluation information thereof in a block chain; if a new block of the current user information, the current insurance information and the current insurance value-added service is generated in the block chain, triggering execution to obtain a recommendation result of the current insurance value-added service according to the current user information, the current insurance information and the current insurance value-added service and by using a trained recommendation model; the recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
In an exemplary embodiment of the present disclosure, the method further comprises: extracting the age, sex, region, policy type, health status, insurance claim type and insurance value-added service type of the user from the historical user information, the historical insurance information and the historical insurance value-added service; processing the age, gender, region, policy type, health state, insurance claim type and insurance value-added service type of the user to generate a historical user feature vector; generating a target value of the historical insurance value-added service according to the historical evaluation information; and training the recommendation model by taking the historical user feature vector and the target value thereof as training data.
In an exemplary embodiment of the present disclosure, obtaining a recommendation result of the current insurance value-added service according to the current user information, the current insurance information, and the current insurance value-added service and by using a trained recommendation model includes: generating a current user characteristic vector according to the current user information, the current insurance information and the current insurance value-added service; inputting the current user feature vector into the recommendation model to obtain a prediction probability value of the current insurance value-added service; if the prediction probability value is larger than a preset threshold value, the recommendation result is that the current insurance value-added service is recommended; and if the prediction probability value is less than or equal to the preset threshold value, the recommendation result is that the current insurance value-added service is not recommended.
In an exemplary embodiment of the present disclosure, the historical user information includes any one or more of age, gender, region, health monitoring data of the user when the historical insurance value-added service is provided to the user.
In an exemplary embodiment of the present disclosure, if the historical user information includes health monitoring data of the user; the method further comprises the following steps: determining a health status of the user based on the health monitoring data.
In an exemplary embodiment of the present disclosure, the historical insurance information includes a policy type and/or an insurance claim type of a user when the historical insurance value-added service is provided to the user.
In an exemplary embodiment of the present disclosure, the recommendation model is a logistic regression model.
According to an aspect of the present disclosure, there is provided a block chain-based recommendation apparatus including: the historical data storage module is used for storing historical user information, historical insurance value-added service and historical evaluation information thereof in the blockchain; a recommendation result obtaining module, configured to trigger execution of a recommendation model according to the current user information, the current insurance information, and the current insurance value-added service and using training to obtain a recommendation result of the current insurance value-added service if a new block of the current user information, the current insurance information, and the current insurance value-added service is generated in the block chain; the recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
According to an aspect of the present disclosure, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the block chain based recommendation method according to any of the embodiments described above.
According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the block chain based recommendation method according to any of the above embodiments.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in the technical solutions provided in some embodiments of the present invention, on one hand, a decentralized storage manner can be implemented by storing historical user information, historical insurance value-added service, historical evaluation information thereof, current user information, current insurance information, and current insurance value-added service by using a block chain technology, and the method has the characteristics of privacy protection, traceability, tamper resistance and the like, and ensures the security and reliability of stored data, thereby preventing information leakage of user data and improving the security of data storage; on the other hand, a recommendation model can be trained based on historical user information, historical insurance information and historical insurance value-added service stored in a block chain, a new block of current user information, current insurance information and current insurance value-added service of the target object is generated in the block chain, and meanwhile, the recommendation result of the current insurance value-added service is obtained according to the current user information, the current insurance information and the current insurance value-added service by triggering execution and utilizing the trained recommendation model, so that whether the current insurance value-added service is pushed to the current user or not can be automatically predicted, personalized recommendation of the insurance value-added service can be realized, user experience is improved, and user stickiness is enhanced; and the block chain technology can be effectively promoted to be applied to the aspect of value-added service management of the life insurance customer full policy-keeping period and full life period. With the wide application of the block chain technology in multiple fields of life insurance customer full-policy-period full-life-period value-added service management, medical treatment, old age preservation, insurance, finance, logistics and the like, the scheme can bring considerable economic and social benefits.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 schematically shows a flow diagram of a block chain based recommendation method according to an embodiment of the invention;
FIG. 2 schematically illustrates a flow diagram of a blockchain-based recommendation method according to another embodiment of the present invention;
FIG. 3 schematically shows a flowchart of one embodiment of step S120 in FIG. 1;
FIG. 4 schematically shows a block diagram of a blockchain-based recommendation apparatus according to an embodiment of the present invention;
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The Blockchain (Blockchain) is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The consensus mechanism is a mathematical algorithm for establishing trust and obtaining rights and interests among different nodes in the blockchain system.
A blockchain is essentially a decentralized database. The block chain is a series of data blocks which are associated by using a cryptographic method, and each data block contains information of one bitcoin network transaction, so that the validity (anti-counterfeiting) of the information is verified and the next block is generated.
In a narrow sense, the blockchain is a distributed account book which is a chain data structure formed by combining data blocks in a sequential connection mode according to a time sequence and is guaranteed in a cryptographic mode and cannot be tampered and forged.
Broadly, the blockchain technique is a completely new distributed infrastructure and computing approach that utilizes blockchain data structures to verify and store data, utilizes distributed node consensus algorithms to generate and update data, utilizes cryptography to secure data transmission and access, and utilizes intelligent contracts composed of automated script code to program and manipulate data.
Generally, a blockchain system consists of a data layer, a network layer, a consensus layer, a stimulus layer, a contract layer, and an application layer. The data layer encapsulates a bottom layer data block, basic data such as related data encryption and time stamp and a basic algorithm; the network layer comprises a distributed networking mechanism, a data transmission mechanism, a data verification mechanism and the like; the consensus layer mainly encapsulates various consensus algorithms of the network nodes; the incentive layer integrates economic factors into a block chain technology system, and mainly comprises an economic incentive issuing mechanism, an economic incentive distributing mechanism and the like; the contract layer mainly encapsulates various scripts, algorithms and intelligent contracts and is the basis of the programmable characteristic of the block chain; the application layer encapsulates various application scenarios and cases of the blockchain. In the model, a chained block structure based on a timestamp, a consensus mechanism of distributed nodes, economic excitation based on consensus computing power and a flexible programmable intelligent contract are the most representative innovation points of the block chain technology.
The recommendation method based on the block chain provided by the embodiment of the invention can effectively realize the personalized recommendation of the insurance value-added service in the block chain network. The method can utilize a transaction chain data structure of a block chain hash pointer and a mechanism of Hash calculation of cryptography and digital signature of cryptography to realize multi-level evidence confirmation in the transaction process, thereby realizing the trust problem among different individual transaction parties. Meanwhile, the user information, the insurance value-added service and the historical evaluation information thereof are stored by using the block chain, and the block chain has the characteristics of privacy protection, traceability, tamper resistance and the like.
Fig. 1 schematically shows a flowchart of a block chain-based recommendation method according to an embodiment of the present invention, where an execution subject of the block chain-based recommendation method may be a device with a computing processing function, such as a server and/or a mobile terminal.
As shown in fig. 1, a block chain-based recommendation method provided by an embodiment of the present invention may include the following steps.
In step S110, the historical user information, the historical insurance information, and the historical insurance value-added service and its historical evaluation information are stored in the block chain.
In an embodiment of the present invention, the historical user information may include any one or more of an age, a gender, a region, health monitoring data, and the like of a user (for example, a life insurance client of an insurance company, but the present invention is not limited thereto) when the historical insurance value-added service is provided to the user.
In this embodiment of the present invention, if the historical user information includes the health monitoring data of the user, the method may further include: determining a health status of the user based on the health monitoring data.
The health monitoring data may include health indicators monitored in daily life of the user, such as a blood pressure indicator, a blood glucose indicator, and the like, may further include physical examination indicators of the user, and may also include daily medical records of the user, diagnostic results of doctors, and the like. The health status of the user may be determined by a comprehensive analysis of the health monitoring data.
In this embodiment of the present invention, the historical insurance information may include any one or more of insurance claim records, insurance contracts, policy information, and the like of the user when the historical insurance value-added service is provided to the user.
In the embodiment of the present invention, the historical insurance value-added service may include any one or more of free or discounted health examination, free or discounted health food, free or discounted sports activities, free or discounted sports facility usage, and the like.
In the embodiment of the present invention, the method may further include a step of constructing the blockchain node and the blockchain network, which is responsible for construction, update and maintenance of the blockchain node and the blockchain network. For example, one or more groups/companies may participate in the block chain network construction of life insurance customer full policy-keeping period full life period value-added service management transaction with a certain company's primary business organization as a minimum node.
In the embodiment of the present invention, the method may further include defining an information storage and information authentication data format in advance, that is, storing and authenticating the shared information according to the data structure mode, the information storage mode, and the protocol defined in the embodiment of the present invention, so as to ensure high efficiency of information storage and information processing.
In the embodiment of the invention, enterprises (such as insurance companies, related medical institutions and the like) or individuals registered in the system upload information such as a full-policy-period full-life-period value-added service case, client policy information, client policy types, client policy aging, health monitoring data, insurance claim records, insurance value-added services and the like of related life insurance clients to the block chain, and can prove that related materials such as audio, video, images, system investigation diagnosis record information and the like of the related information can also be uploaded to the block chain, so that the information stored in the block chain has the characteristics of privacy protection (for example, the information can be subjected to technical means such as authority management, watermarking of images or videos, encryption and the like), public transparency, traceability, difficulty in tampering and the like.
The full policy-keeping period full life period value-added service case of the relevant life insurance customer refers to historical data stored in a block chain, and the historical data comprises the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
In step S120, if a new block of the current user information, the current insurance information, and the current insurance value-added service is generated in the block chain, the execution is triggered to obtain the recommendation result of the current insurance value-added service according to the current user information, the current insurance information, and the current insurance value-added service and by using the trained recommendation model.
The recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
According to the recommendation method based on the blockchain provided by the embodiment of the invention, on one hand, a decentralized storage mode can be realized by storing historical user information, historical insurance value-added service and historical evaluation information thereof, and current user information, current insurance information and current insurance value-added service by using a blockchain technology, so that the recommendation method has the characteristics of privacy protection, traceability, tamper resistance and the like, the safety and reliability of stored data are ensured, the information leakage of user data can be prevented, and the safety of data storage is improved; on the other hand, a recommendation model can be trained based on historical user information, historical insurance information and historical insurance value-added service stored in a block chain, a new block of current user information, current insurance information and current insurance value-added service of the target object is generated in the block chain, and meanwhile, the recommendation result of the current insurance value-added service is obtained according to the current user information, the current insurance information and the current insurance value-added service by triggering execution and utilizing the trained recommendation model, so that whether the current insurance value-added service is pushed to the current user or not can be automatically predicted, personalized recommendation of the insurance value-added service can be realized, user experience is improved, and user stickiness is enhanced; and the block chain technology can be effectively promoted to be applied to the aspect of value-added service management of the life insurance customer full policy-keeping period and full life period. With the wide application of the block chain technology in multiple fields of life insurance customer full-policy-period full-life-period value-added service management, medical treatment, old age preservation, insurance, finance, logistics and the like, the scheme can bring considerable economic and social benefits.
Fig. 2 schematically shows a flow chart of a block chain based recommendation method according to another embodiment of the present invention.
As shown in fig. 2, the difference from the above embodiment shown in fig. 1 is that the block chain based recommendation method provided in the embodiment of the present invention may further include the following steps.
In step S210, the age, gender, region, policy type, health status, insurance claim type and insurance value-added service type of the user are extracted from the historical user information, the historical insurance information and the historical insurance value-added service.
In the embodiment of the invention, data acquisition and feature extraction are firstly carried out: the existing policy information, health monitoring data, insurance claim settlement records of each client, the recommended insurance value-added services to the clients, and historical data information such as evaluation of each insurance value-added service by the clients receiving each insurance value-added service are collected from the blockchain.
In step S220, the age, gender, region, policy type, health status, insurance claim type and insurance value-added service type of the user are processed to generate a historical user feature vector.
In the embodiment of the present invention, from the above historical data collected from the block chain, the age x1, the gender x2, the location x3, the policy type x4, the health status x5, the insurance claim type x6, and the insurance value-added service type x7 of the user who has received a certain insurance value-added service are extracted, and 7 feature values corresponding to each value-added service that has been provided to a certain client are formed. Further, when the training data is obtained, data screening may be performed on the historical data in the block chain first, and a record with complete data is selected as the training data, where the record with complete data refers to a record of the historical insurance value-added service that completely has the above 7 types of data, and if any one of the records is absent, the record is not adopted.
In an embodiment of the present invention, the health state x5 may be obtained by comprehensively analyzing health indicators, physical examination indicators, medical records, hospital records, and diagnosis results of doctors in the health monitoring data, and the health state x5 may include: health (no disease), chronic disease, severe disease, postoperative conditions. For example, whether the patient is healthy or not can be determined based on the examination result of the client (examination report issued by the doctor), and whether the patient has chronic disease, serious disease or has undergone surgery can be determined based on the visit information of the client and the diagnosis record of the doctor.
In the embodiment of the present invention, the insurance claim type x6 may include a pay type (for disabilities, serious diseases, etc.), a claim type (for disease medical treatment, accidental injury medical treatment, etc.), and the like.
In the embodiment of the invention, the insurance value-added services can be divided into a plurality of types, and the label value of the insurance value-added service type x7 is determined according to the type of the insurance value-added services in each history record. For example, a free physical examination is the first insurance value-added service type, and x7 is labeled "1"; free health food is the second insurance value-added service type, and x7 is labeled as "2"; … and so on.
Specifically, for non-quantitatively described class characteristics such as region x3, gender x2 and the like, different label values are assigned to each class, for example, if the gender is female, the position of x2 is assigned to "1"; the position of x2 is assigned a value of "0" for a male sex, but the present invention is not limited thereto.
Specifically, for a quantitative description of a continuous characteristic such as age x1, it can be divided into different age stages and assigned different label values. For example, if the customer age range is 18-60 years, then users with an age of 10-20 years will be given an x1 label value of "0"; users aged 20-30 years, with an x1 label value of "1"; users aged 30-40 years, with an x1 label value of "2"; users aged 40-50 years, with an x1 label value of "3"; users aged 50-60 years are given an x1 label value of "4".
After the data is processed, a historical user feature vector X of a historical insurance value-added service record is formed [ X1, X2., X7 ]. Assuming that n (n is a positive integer greater than or equal to 1) historical insurance value-added service records are collected from the blockchain, the feature vectors of the n historical insurance value-added service records may constitute a feature matrix M ═ X1; x2; ...; xn ].
In step S230, a target value of the historical insurance value-added service is generated according to the historical evaluation information.
In the embodiment of the invention, each item of the historically accepted insurance value-added service can be scored according to the difference between the evaluation and the love degree of the customer on different insurance value-added services, for example, if the evaluation given by the customer is more than 4 points (including 4 points), the insurance value-added service of the type is recommended to the customer, and the target value of the historical insurance value-added service is assumed to be 1; if the historical evaluation given by the client is less than 4 points, the insurance value-added service of the type is not recommended to the client, and the target value of the historical insurance value-added service is assumed to be 0, then aiming at the characteristic matrix M as [ X1; x2; ...; xn ], a target vector Y [ Y1, Y2,.., yn ] may be formed that is a combination of the target values of the n historical premium services.
In step S240, the recommendation model is trained using the historical user feature vectors and their target values as training data.
In the embodiment of the present invention, the recommendation model may be a logistic regression model, but the present invention is not limited thereto, and in other embodiments, the recommendation model may be any other suitable machine learning model.
Specifically, a logistic regression model may be established in an off-line state, the feature matrix M is used as an input of the logistic regression model, and the corresponding target vector Y is used as a label to train the logistic regression model and learn model parameters. And then, automatically and individually recommending the insurance value-added service based on the logistic regression model by using the online obtained data.
Fig. 3 schematically shows a flow chart of an embodiment of step S120 in fig. 1.
In step S121, a current user feature vector is generated according to the current user information, the current insurance information, and the current insurance value-added service.
In the embodiment of the present invention, the current user information may include any one or more of the age, sex, region, health monitoring data, and the like of the life insurance client obtained on line in real time. And from the health monitoring data, a current health status of the user may be determined. The current insurance information may include a policy type and/or an insurance claim type of the life insurance client acquired online in real time. The current insurance value-added service can be an insurance value-added service newly introduced by an insurance company, and the newly introduced insurance value-added service can be classified into a certain insurance value-added service type according to the pre-classified insurance value-added service types. After the current user information, the current insurance information and the current insurance value-added service are processed, a current user feature vector can be generated.
In step S122, the current user feature vector is input to the recommendation model, and a prediction probability value of the current insurance value-added service is obtained.
In step S123, determining whether the predicted probability value is greater than a preset threshold; if yes, go to step S124; if not, the process proceeds to step S125.
In step S124, if the predicted probability value is greater than a preset threshold, the recommendation result is to recommend the current insurance value-added service.
In step S125, if the predicted probability value is smaller than or equal to the preset threshold, the recommendation result is that the current insurance value-added service is not recommended.
Specifically, the feature vector of the current user generated in the above step is input into a trained logistic regression model, the output of the logistic regression model is a prediction probability value p of whether to recommend the current insurance value-added service to the current user, a preset threshold value is assumed to be v, and if p is greater than v, the current insurance value-added service is recommended to the current user; otherwise, the current insurance value-added service is not recommended to the current user.
The recommendation method based on the blockchain provided by the embodiment of the invention can be effectively realized in the blockchain network, and the specific transaction information is shown in the following table 1:
TABLE 1
In the embodiment of the present invention, the method may further include: the timeliness, effectiveness and accuracy of the life insurance customer full policy-keeping period full life cycle value-added service management system are evaluated, system parameters are continuously adjusted and optimized based on a personalized value-added service recommendation method of user characteristics such as customer health monitoring data, insurance claim records, insurance value-added service evaluation and the like, so that the life insurance customer full policy-keeping period full life cycle value-added service management is effectively realized in a block chain network, and therefore the block chain technology is powerfully promoted to be applied to the life insurance customer full policy-keeping period full life cycle value-added service management aspect.
The following describes an embodiment of the apparatus of the present invention, which can be used to perform the above block chain-based recommendation method of the present invention.
Fig. 4 schematically shows a block diagram of a block chain based recommendation apparatus according to an embodiment of the present invention.
As shown in fig. 4, the recommendation apparatus 400 based on a blockchain according to an embodiment of the present invention may include a history data storage module 410 and a recommendation result obtaining module 420.
The historical data storage module 410 may be configured to store historical user information, historical insurance information, and historical insurance value-added services and historical evaluation information thereof in the blockchain.
The recommendation result obtaining module 420 may be configured to, if a new block of the current user information, the current insurance information, and the current insurance value-added service is generated in the block chain, trigger execution of a recommendation model according to the current user information, the current insurance information, and the current insurance value-added service and using a trained result to obtain a recommendation result of the current insurance value-added service.
The recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
In an exemplary embodiment, the block chain based recommendation apparatus 400 may further include: the historical user characteristic extraction module can be used for extracting the age, the gender, the region, the policy type, the health state, the insurance claim type and the insurance value-added service type of the user from the historical user information, the historical insurance information and the historical insurance value-added service; the historical characteristic vector generating module can be used for processing the age, the gender, the region, the policy type, the health state, the insurance claim type and the insurance value-added service type of the user to generate a historical user characteristic vector; the target value generating module can be used for generating a target value of the historical insurance value-added service according to the historical evaluation information; and the model training module can be used for training the recommendation model by taking the historical user feature vector and the target value thereof as training data.
In an exemplary embodiment, the recommendation obtaining module 420 may include: a current feature vector generating unit, configured to generate a current user feature vector according to the current user information, the current insurance information, and the current insurance value-added service; the model prediction unit may be configured to input the current user feature vector to the recommendation model, and obtain a prediction probability value of the current insurance value-added service; the recommending unit may be configured to recommend the current insurance value-added service according to the recommending result if the predicted probability value is greater than a preset threshold; and the non-recommending unit may be configured to, if the predicted probability value is less than or equal to the preset threshold, recommend the current insurance value-added service as the recommending result.
In an exemplary embodiment, the historical user information may include any one or more of age, gender, region, health monitoring data of the user when the historical insurance value-added service is provided to the user.
In an exemplary embodiment, if the historical user information includes health monitoring data for the user; the block chain based recommendation apparatus 400 may further include: a health status determination module may be configured to determine a health status of the user based on the health monitoring data.
In an exemplary embodiment, the historical insurance information may include a policy type and/or an insurance claim type of the user when the historical insurance value-added service is provided to the user.
In an exemplary embodiment, the recommendation model may be a logistic regression model.
For details that are not disclosed in the embodiment of the apparatus of the present invention, please refer to the above embodiment of the recommendation method based on a block chain of the present invention for the details that are not disclosed in the embodiment of the apparatus of the present invention.
Referring now to FIG. 5, a block diagram of a computer system 800 suitable for use with the electronic device implementing an embodiment of the invention is shown. The computer system 800 of the electronic device shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of the use of the embodiments of the present invention.
As shown in fig. 5, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 807 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for system operation are also stored. The CPU801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted into the storage section 807 as necessary.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program executes the above-described functions defined in the system of the present application when executed by the Central Processing Unit (CPU) 801.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and/or units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described modules and/or units may also be disposed in a processor. Wherein the names of such modules and/or units do not in some way constitute a limitation on the modules and/or units themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which when executed by an electronic device, cause the electronic device to implement the recommendation method based on the block chain as described in the above embodiments.
For example, the electronic device may implement the following as shown in fig. 1: step S110, storing historical user information, historical insurance value-added service and historical evaluation information thereof in a block chain; step S120, if a new block of the current user information, the current insurance information and the current insurance value-added service is generated in the block chain, triggering execution according to the current user information, the current insurance information and the current insurance value-added service and by using a trained recommendation model, and obtaining a recommendation result of the current insurance value-added service; the recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
As another example, the electronic device may implement the steps shown in fig. 2 to 3.
It should be noted that although in the above detailed description several modules and/or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more of the modules and/or units described above may be embodied in one module and/or unit according to embodiments of the invention. Conversely, the features and functions of one module and/or unit described above may be further divided into embodiments by a plurality of modules and/or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A recommendation method based on a block chain is characterized by comprising the following steps:
storing historical user information, historical insurance value-added service and historical evaluation information thereof in a block chain;
if a new block of the current user information, the current insurance information and the current insurance value-added service is generated in the block chain, triggering execution to obtain a recommendation result of the current insurance value-added service according to the current user information, the current insurance information and the current insurance value-added service and by using a trained recommendation model;
the recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
2. The method of claim 1, further comprising:
extracting the age, sex, region, policy type, health status, insurance claim type and insurance value-added service type of the user from the historical user information, the historical insurance information and the historical insurance value-added service;
processing the age, gender, region, policy type, health state, insurance claim type and insurance value-added service type of the user to generate a historical user feature vector;
generating a target value of the historical insurance value-added service according to the historical evaluation information;
and training the recommendation model by taking the historical user feature vector and the target value thereof as training data.
3. The method of claim 1, wherein obtaining the recommendation result of the current insurance value-added service according to the current user information, the current insurance information, and the current insurance value-added service and by using a trained recommendation model comprises:
generating a current user characteristic vector according to the current user information, the current insurance information and the current insurance value-added service;
inputting the current user feature vector into the recommendation model to obtain a prediction probability value of the current insurance value-added service;
if the prediction probability value is larger than a preset threshold value, the recommendation result is that the current insurance value-added service is recommended;
and if the prediction probability value is less than or equal to the preset threshold value, the recommendation result is that the current insurance value-added service is not recommended.
4. The method of claim 1, wherein the historical user information comprises any one or more of age, gender, region, health monitoring data of the user when the historical insurance value-added service is provided to the user.
5. The method of claim 4, wherein if the historical user information includes health monitoring data for the user; the method further comprises the following steps:
determining a health status of the user based on the health monitoring data.
6. The method according to claim 1, wherein the historical insurance information comprises a policy type and/or an insurance claim type of a user when the historical insurance value-added service is provided to the user.
7. The method of claim 1, wherein the recommendation model is a logistic regression model.
8. A blockchain-based recommendation apparatus, comprising:
the historical data storage module is used for storing historical user information, historical insurance value-added service and historical evaluation information thereof in the blockchain;
a recommendation result obtaining module, configured to trigger execution of a recommendation model according to the current user information, the current insurance information, and the current insurance value-added service and using training to obtain a recommendation result of the current insurance value-added service if a new block of the current user information, the current insurance information, and the current insurance value-added service is generated in the block chain;
the recommendation model is obtained by training according to the historical user information, the historical insurance value-added service and the historical evaluation information thereof.
9. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the blockchain-based recommendation method according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the blockchain-based recommendation method of any one of claims 1 to 7.
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