CN112035611A - Target user recommendation method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a user consumption portrait in the field of data analysis, in particular to a target user recommendation method and device, computer equipment and a storage medium. The method comprises the steps of searching a user portrait according to a medical consultation message sent by a receiving terminal to obtain user portrait information; extracting the medical consultation message and user characteristic information corresponding to the user image information; acquiring a user quality parameter corresponding to the user according to the user characteristic information; and recommending a target user to a medical service provider according to the user portrait information and the user quality parameter. According to the method and the system, the user characteristic information is mined through the consultation information of the user and the portrait information of the user, the user quality parameter is determined based on the user characteristic information, so that the target client is selected, the target user is recommended to the medical service provider, and the effective recommendation rate of the target user can be effectively improved.
Description
Technical Field
The present application relates to user consumption portraits in the field of data analysis, and in particular, to a target user recommendation method, apparatus, computer device, and storage medium.
Background
With the development of computer technology and artificial intelligence technology, user portrait technology has emerged. The user portrait is an effective tool for sketching the appeal and the design direction of a target user and a contact user, and is widely applied to various fields. Under the background of the big data era, user information is flooded in a network, each concrete information of a user is abstracted into labels, and the labels are utilized to concretize the user image, so that targeted services are provided for the user.
Currently, the technology of analyzing user portrait based on medical big data mainly focuses on analyzing through professional structured knowledge such as personal population information of patients, electronic health files, electronic medical records and physical examination reports. However, this solution requires a large amount of manpower and material cost, and the obtained samples are often limited in locality and universality, so that the recommendation to the user cannot be effectively realized.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a target user recommendation method, apparatus, computer device and storage medium capable of improving recommendation efficiency.
A method of target user recommendation, the method comprising:
receiving a medical consultation message sent by a terminal, and searching a user portrait according to the medical consultation message to obtain user portrait information;
extracting the medical consultation message and user characteristic information corresponding to the user image information;
acquiring a user quality parameter corresponding to the user according to the user characteristic information;
and recommending a target user to a medical service provider according to the user portrait information and the user quality parameter.
In one embodiment, the user image information comprises real-time image characteristics and off-line inquiry characteristics;
the receiving terminal sends medical consultation information, the user portrait is searched according to the medical consultation information, and the user portrait information is obtained by the method comprising the following steps:
receiving a medical consultation message sent by a terminal;
analyzing the medical consultation message to obtain a user identifier corresponding to the terminal;
and extracting the real-time portrait features from a preset online hive database platform according to the user identification, and extracting the offline inquiry features from a preset offline updating platform according to the user identification.
In one embodiment, the user image information comprises real-time image characteristics and off-line inquiry characteristics;
the receiving terminal sends medical consultation information, and before searching the user portrait according to the medical consultation information and obtaining user portrait information, the method further comprises the following steps:
acquiring an image information updating request;
searching user identity information, historical inquiry flow information and historical inquiry transaction information corresponding to the portrait information updating request;
and acquiring real-time portrait characteristics according to the user identity information and the historical inquiry flow information, storing the real-time portrait characteristics to a preset online hive database platform, acquiring offline inquiry characteristics according to the historical inquiry transaction information, and storing the offline inquiry characteristics to a preset offline updating platform.
In one embodiment, the receiving a medical consultation message sent by a terminal, searching a user portrait according to the medical consultation message, and obtaining user portrait information includes:
receiving a medical consultation message sent by a terminal;
inputting the medical consultation message into a preset semantic recognition model to obtain a recognition result;
and when the identification result represents that the medical consultation message is an effective message, searching the user portrait according to the medical consultation message to obtain user portrait information.
In one embodiment, the obtaining the user quality parameter corresponding to the user according to the user characteristic information includes:
constructing a multi-dimensional feature matrix according to the user feature information;
and inputting the multi-dimensional characteristic matrix into a preset user quality evaluation model to obtain user quality parameters corresponding to the user, wherein the preset user quality evaluation model is obtained by an initial xgboost model based on marked historical characteristic data through supervised training.
In one embodiment, before the constructing the multi-dimensional feature matrix according to the user feature information, the method further includes:
acquiring a user type and historical characteristic data corresponding to a historical user;
determining the differentiation characteristics corresponding to each type of user according to the user type corresponding to the historical user and the historical characteristic data;
performing pearson correlation coefficient analysis on the differential features to obtain strong correlation feature types corresponding to the historical feature data;
the constructing of the multi-dimensional feature matrix according to the user feature information includes:
and constructing a multi-dimensional feature matrix according to the user feature information corresponding to the strongly correlated feature type.
A target user recommendation apparatus, the apparatus comprising:
the information searching module is used for receiving the medical consultation message sent by the terminal and searching the user portrait according to the medical consultation message to obtain user portrait information;
the characteristic extraction module is used for extracting the medical consultation message and the user characteristic information corresponding to the user image information;
the quality parameter evaluation module is used for acquiring the user quality parameters corresponding to the user according to the user characteristic information;
and the target recommending module is used for recommending a target user to a medical service provider according to the user portrait information and the user quality parameter.
In one embodiment, the user image information includes a real-time image feature and an offline inquiry feature, and the information search module is configured to:
receiving a medical consultation message sent by a terminal;
analyzing the medical consultation message to obtain a user identifier corresponding to the terminal;
and extracting the real-time portrait features from a preset online hive database platform according to the user identification, and extracting the offline inquiry features from a preset offline updating platform according to the user identification.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
receiving a medical consultation message sent by a terminal, and searching a user portrait according to the medical consultation message to obtain user portrait information;
extracting the medical consultation message and user characteristic information corresponding to the user image information;
acquiring a user quality parameter corresponding to the user according to the user characteristic information;
and recommending a target user to a medical service provider according to the user portrait information and the user quality parameter.
A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of:
receiving a medical consultation message sent by a terminal, and searching a user portrait according to the medical consultation message to obtain user portrait information;
extracting the medical consultation message and user characteristic information corresponding to the user image information;
acquiring a user quality parameter corresponding to the user according to the user characteristic information;
and recommending a target user to a medical service provider according to the user portrait information and the user quality parameter.
According to the target user recommendation method, the target user recommendation device, the computer equipment and the storage medium, the user portrait is searched according to the medical consultation message sent by the receiving terminal, and the user portrait information is obtained; extracting the medical consultation message and user characteristic information corresponding to the user image information; acquiring a user quality parameter corresponding to the user according to the user characteristic information; and recommending a target user to a medical service provider according to the user portrait information and the user quality parameter. According to the method and the system, the user characteristic information is mined through the consultation information of the user and the portrait information of the user, the user quality parameter is determined based on the user characteristic information, so that the target client is selected, the target user is recommended to the medical service provider, and the effective recommendation rate of the target user can be effectively improved.
Drawings
FIG. 1 is a diagram illustrating an exemplary implementation of a target user recommendation method;
FIG. 2 is a flowchart illustrating a method for recommending a target user in one embodiment;
FIG. 3 is a schematic sub-flow chart illustrating step 201 of FIG. 2 according to an embodiment;
FIG. 4 is a flowchart illustrating steps for storing user representation information in one embodiment;
FIG. 5 is a schematic sub-flow chart of step 201 in FIG. 2 according to another embodiment;
FIG. 6 is a schematic sub-flow chart illustrating step 205 of FIG. 2 according to one embodiment;
FIG. 7 is a block diagram showing the structure of a target user recommending apparatus according to an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The target user recommendation method provided by the application can be applied to the application environment shown in fig. 1. Wherein, the terminal 102 communicates with the target recommendation server 104 through a network. When a user performs medical inquiry through an intelligent medical interaction platform, the target recommendation server 104 carrying the target user recommendation method can perform simulated question-answer communication with the user through the medical interaction platform, so that the medical inquiry information and the user portrait information of the user can be obtained, and the corresponding user quality parameters of the user can be obtained. Specifically, the user may log into the medical interaction platform through the terminal 102. First, the terminal 102 transmits a medical consultation message to the target recommendation server 104. The target recommendation server 104 receives the medical consultation message sent by the terminal 102, searches the user portrait according to the medical consultation message and obtains user portrait information; extracting medical consultation information and user characteristic information corresponding to user image information; acquiring user quality parameters corresponding to the user according to the user characteristic information; and recommending the target user to the medical service provider according to the user portrait information and the user quality parameters. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a target user recommendation method is provided, which is described by taking the method as an example applied to the target recommendation server 104 in fig. 1, and includes the following steps:
The medical consultation message refers to a piece of inquiry information submitted by a user to the medical interaction platform. The method specifically comprises the following steps: a query message asking what disease symptom a corresponds to, or whether the nail disease can be treated with the drug a. The user portrait information is a message that is stored in the system after being photographed by the user.
Specifically. According to the scheme, the user recommendation server can add corresponding feature labels to the users based on the use of the medical interaction platform by the users and the identity information of the users, construct user portraits of the users, and analyze the users through the user portraits information when the users use the online medical platform to obtain medical services, so that the user recommendation function is realized. In the consultation process of the server, the target recommendation server 104 may also feed back some inquiry simulation messages to the user, so as to extract data such as a chief complaint, a template inquiry, a diagnosis label and the like corresponding to the medical consultation message, so as to more completely supplement the medical self-selection message.
And step 203, extracting the user characteristic information corresponding to the medical consultation message and the user image information.
In order to effectively extract the user quality parameters later, corresponding user characteristic information needs to be extracted from the user portrait information, and the medical consultation message and the user characteristics available in the user portrait information can be extracted. In one embodiment, step S203 further includes a step of simplifying the features, for example, for the historical inquiry transaction information included in the user portrait information, the historical inquiry transaction information of the user in different historical time periods may be obtained first, for example, the historical inquiry transaction information of 365 days, 180 days, 90 days and 60 days is directly used as the features for calculation, and the historical inquiry transaction information is expressed in an interval manner, so that the longitudinal dimension of the feature vector is further reduced, and the spatial complexity required for constructing the feature vector is reduced. And the characteristics of the human mouth dimensions such as age, gender and province can be expressed by one-dimensional digital characteristics, so that the user characteristics are further simplified.
And step 205, acquiring a user quality parameter corresponding to the user according to the user characteristic information.
Wherein the user quality parameter is an evaluation criterion for recommending the user. The user quality parameters corresponding to the user can be comprehensively acquired according to a plurality of different user characteristic information.
Specifically, in one embodiment, the user quality parameter may be performed according to specific requirements of the medical service provider, for example, a higher user quality parameter may be assigned to the user according to a characteristic that the user has a high transaction rate, or a higher user quality parameter may be assigned to the user according to a characteristic that the user has a rich content of the consultation message. In a specific embodiment, the user quality parameters corresponding to the user feature information may be extracted through a pre-established preset user quality evaluation model. The preset user quality evaluation model can be specifically an xgboost model, is obtained through supervised training, and evaluates the user quality by inputting a multidimensional feature matrix containing user feature information into the user quality evaluation model, for example, the purchase intention of an online user on a prescription can be reflected through user quality parameters, so that a platform user with higher quality can be effectively extracted. In one embodiment, the user quality parameter may be performed according to specific requirements of the medical service provider, for example, a higher user quality parameter may be assigned to the user according to a characteristic that the user has a high transaction rate, or a higher user quality parameter may be assigned to the user according to a characteristic that the user has a rich consultation message.
The preset user quality evaluation model is used for evaluating the quality of the user, and specifically, recommendation of the target user can be performed by quantizing the quality of the user into corresponding user quality parameters. The preset user quality evaluation model can be specifically an xgboost model, the user quality is evaluated by supervised training and inputting a multidimensional characteristic matrix containing user characteristic information into the user quality evaluation model, so that the purchase intention of an online user on a prescription is stripped from mass inquiry, and a platform user with higher quality can be effectively extracted from mass users of the medical interaction platform.
And step 207, recommending a target user to the medical service provider according to the user portrait information and the user quality parameter.
The medical service provider is a processor providing medical services for users on an online medical platform, and can perform services such as remote inquiry and prescription filling for users according to medical consultation of the users. Similar to the store of an e-commerce platform. The target user recommendation method is used for recommending users with high quality to the medical service providers.
After the quality parameters of the user are determined, the corresponding target user can be recommended to the medical service provider on the online medical platform based on the quality parameters of the user, and specifically, the medical service provider of the online medical platform can set the corresponding quality parameters of the user online and set which medical consultation messages with which diagnosis labels can be accepted. After the user recommendation server obtains the user quality parameters corresponding to the user, the user recommendation server can determine which medical service providers meet the requirements of the user based on the diagnosis labels corresponding to the medical consultation messages and the user quality parameters, and then recommend the current user to the medical server providers.
The target user recommendation method comprises the steps of searching a user portrait according to a medical consultation message sent by a receiving terminal to obtain user portrait information; extracting medical consultation information and user characteristic information corresponding to user image information; acquiring user quality parameters corresponding to the user according to the user characteristic information; and recommending the target user to the medical service provider according to the user portrait information and the user quality parameters. According to the method and the system, the user characteristic information is mined through the consultation information of the user and the portrait information of the user, the user quality parameter is determined based on the user characteristic information, so that the target client is selected, the target user is recommended to the medical service provider, and the effective recommendation rate of the target user can be effectively improved.
In one embodiment, the user image information includes a real-time image feature and an offline inquiry feature, as shown in fig. 3, step 201 includes:
And step 304, analyzing the medical consultation message and acquiring a user identifier corresponding to the terminal.
And step 306, extracting real-time portrait characteristics from a preset online hive database platform according to the user identification, and extracting offline inquiry characteristics from a preset offline updating platform according to the user identification.
The user portrait information can be hierarchically calculated and stored through an online hive database platform according to the user identification. The storage system of the user portrait information is divided into an online real-time storage module and an offline updating module. The online module corresponding to the real-time portrait characteristics can respond to the portrait characteristics of the user in real time, and information contained in the portrait characteristics specifically comprises user identity information, inquiry flow information and other information. And the off-line module corresponding to the off-line inquiry characteristic can update the characteristics of the historical inquiry transaction information and the like of the user at regular time.
Specifically, in order to improve the calculation efficiency in the target recommendation process, in the present application, portrait information related to a user is stored in an offline storage mode and an online update mode. When target user recommendation is needed, the target recommendation server 104 firstly receives the medical consultation message sent by the terminal, and then analyzes the medical consultation message to obtain a user identifier of corresponding user portrait data in the user search database; the user identification may be in the form of a UID, i.e. a user account. And then the target recommendation server 104 extracts real-time portrait characteristics from a preset online hive database platform according to the user identification, and extracts offline inquiry characteristics from a preset offline update platform according to the user identification. In the embodiment, the online module and the offline module are organically integrated, so that the portrait information of the user can be more accurately stored on the premise of not influencing the fluency of the processing environment, and the recommendation accuracy of the target user is improved.
In one embodiment, as shown in fig. 4, before step 201, the method further includes:
The portrait information updating request is that a corresponding portrait information updating request is generated every time a user uses the medical interactive platform. The historical inquiry flow information refers to information generated in the process of online inquiry of a user through the medical interactive platform every time. The historical inquiry transaction information refers to whether the user accepts the prescription issued by the doctor on line after the on-line inquiry through the medical interactive platform, and the historical record corresponding to the medicine is purchased according to the prescription.
Specifically, the user portrait information of the user can be stored through a pre-designed high-performance storage module to conveniently and rapidly retrieve the related portrait data of the user, for example, in one embodiment, online features of 23 dimensions can be stored in a preset online hive database platform through a service party in a json character string manner, and meanwhile, a 15-dimensional offline feature cluster is stored in a redis storage unit (from a preset offline update platform) in a task update mode at the backstage zeus and is invoked. When the online model is called, the user identification is used as a key value of searching, and the related characteristics can be called quickly. Meanwhile, on the system level, the comprehensive characteristics of the current user are stored in the redis in a key value pair mode so as to be convenient for the subsequent application and retrieval. In the embodiment, the user portrait information of the user is stored through the preset online hive database platform and the preset offline updating platform, so that portrait information can be extracted more efficiently when the target user recommends, and the processing efficiency is ensured.
In one embodiment, as shown in fig. 5, step 201 includes:
The preset semantic recognition model can be a classification neural network model specifically, and is used for performing semantic classification on medical consultation messages input by a user and dividing the medical consultation messages into effective consultation and ineffective consultation. When the input medical consultation message is meaningless information which is not related to the inquiry, such as 'hello' or 'thank you', the medical consultation message input by the user is determined to be valid consultation, and when the input by the user is specific symptom information, the medical consultation message input by the user can be determined to be invalid consultation.
Specifically, the message sent by the user on the online medical platform is not necessarily valid medical information, and in order to avoid wasting the computing resources of the user recommendation server 104, it may be determined whether the medical consultation message is valid medical message before parsing the medical consultation message. This step can be determined in particular by a semantically recognized neural network model. For the case that the medical consultation message is not a valid medical message, the server directly feeds back a consultation failure message to the user, and if the consultation failure message is a valid message, subsequent analysis operation can be carried out. By performing semantic recognition processing on the medical consultation message and eliminating invalid information, the mishandling rate recommended by the target user can be effectively reduced, and the processing efficiency of recommendation is improved.
In one embodiment, as shown in FIG. 6, step 205 comprises:
601, constructing a multi-dimensional feature matrix according to user feature information;
The multidimensional feature matrix is obtained based on all user feature information, for example, the features of user identity dimensions such as age, gender, province and the like can be expressed through one-dimensional digital features, and the user features are further simplified. A multi-dimensional feature matrix corresponding to the feature type number can be constructed based on all the features in the real-time portrait features and the off-line inquiry features, and therefore the multi-dimensional feature matrix is used as model input to estimate user quality parameters. In addition, before step 603, a step of constructing a preset user quality evaluation model is further included, specifically, historical data can be obtained, historical feature information corresponding to the historical data is constructed based on predetermined feature parameters, corresponding labels are added to the historical feature information, then supervised training is performed on the initial xgboost model based on the labeled historical feature information, the labeled historical feature information is divided into a training set and a verification set, the preset user quality evaluation model is trained and verified respectively, and when a result obtained after verification is that the model is available, the trained model is output and is used as the preset user quality evaluation model. In this embodiment, the user quality parameters are extracted through the xgboost model, so that the efficiency and accuracy of the quality parameter extraction process can be effectively improved.
In one embodiment, before step 601, the method further includes: acquiring a user type and historical characteristic data corresponding to a historical user; determining the differentiation characteristics corresponding to various types of users according to the user types corresponding to the historical users and the historical characteristic data; and performing pearson correlation coefficient analysis on the differentiated features to obtain strong correlation feature types corresponding to the historical feature data. Step 601 comprises: and constructing a multi-dimensional feature matrix according to the user feature information corresponding to the strongly correlated feature types.
The user recommendation server 104 may determine, according to the requirements of the medical service provider, which users in the historical users are high-quality users and which users are low-quality users in advance, and add corresponding users, such as users with high transaction rate as high-quality users and users with low transaction rate as low-quality users. Then, the user recommendation server 104 can perform computer statistical analysis on the real-time big data of the online medical platform to obtain the differentiation characteristics of the high-quality user and the low-quality user. And then, analyzing the pearson correlation coefficient to obtain the strong correlation characteristics. The determined strong correlation characteristics are the available characteristics in the user quality parameter calculation process. In the embodiment, the multi-dimensional feature matrix is constructed through the strong correlation feature types related to the user quality, so that the validity of the user parameters in the user quality parameters can be ensured, and the calculation accuracy of the user quality parameters is improved.
It should be understood that although the various steps in the flow charts of fig. 2-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided a target user recommendation device, including:
and the information searching module 702 is used for receiving the medical consultation message sent by the terminal, searching the user portrait according to the medical consultation message and obtaining the user portrait information.
And the feature extraction module 704 is configured to extract user feature information corresponding to the medical consultation message and the user image information.
And the quality parameter evaluation module 706 is configured to obtain a user quality parameter corresponding to the user according to the user characteristic information.
And the target recommending module 708 is used for recommending a target user to the medical service provider according to the user portrait information and the user quality parameter.
In one embodiment, the user profile information includes real-time profile characteristics and offline inquiry characteristics, and the information search module 702 is configured to: receiving a medical consultation message sent by a terminal; analyzing the medical consultation message to obtain a user identifier corresponding to the terminal; and extracting real-time portrait characteristics from a preset online hive database platform according to the user identification, and extracting offline inquiry characteristics from a preset offline updating platform according to the user identification.
In one embodiment, the user profile information includes real-time profile characteristics and offline interrogation characteristics, and the apparatus further includes a user profile module configured to: acquiring an image information updating request; searching user identity information, historical inquiry flow information and historical inquiry transaction information corresponding to the portrait information updating request; the method comprises the steps of obtaining real-time portrait characteristics according to user identity information and historical inquiry flow information, storing the real-time portrait characteristics to a preset online hive database platform, obtaining offline inquiry characteristics according to historical inquiry transaction information, and storing the offline inquiry characteristics to a preset offline updating platform.
In one embodiment, the system further comprises a consultation message checking module for: receiving a medical consultation message sent by a terminal; inputting the medical consultation message into a preset semantic recognition model to obtain a recognition result; and when the identification result represents that the medical consultation message is an effective message, searching the user portrait according to the medical consultation message to obtain the user portrait information.
In one embodiment, the quality parameter evaluation module is specifically configured to: constructing a multi-dimensional feature matrix according to the user feature information; and inputting the multi-dimensional characteristic matrix into a preset user quality evaluation model to obtain user quality parameters corresponding to the user, wherein the preset user quality evaluation model is obtained by an initial xgboost model based on marked historical characteristic data through supervised training.
In one embodiment, the system further comprises a feature screening unit, configured to: acquiring a user type and historical characteristic data corresponding to a historical user; determining the differentiation characteristics corresponding to various types of users according to the user types corresponding to the historical users and the historical characteristic data; and performing pearson correlation coefficient analysis on the differentiated features to obtain strong correlation feature types corresponding to the historical feature data.
For specific limitations of the target user recommendation device, reference may be made to the above limitations of the target user recommendation method, which are not described herein again. The modules in the target user recommending device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the target user recommendation data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a target user recommendation method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:
receiving a medical consultation message sent by a terminal, and searching a user portrait according to the medical consultation message to obtain user portrait information;
extracting medical consultation information and user characteristic information corresponding to user image information;
acquiring user quality parameters corresponding to the user according to the user characteristic information;
and recommending the target user to the medical service provider according to the user portrait information and the user quality parameters.
In one embodiment, the processor, when executing the computer program, further performs the steps of: receiving a medical consultation message sent by a terminal; analyzing the medical consultation message to obtain a user identifier corresponding to the terminal; and extracting real-time portrait characteristics from a preset online hive database platform according to the user identification, and extracting offline inquiry characteristics from a preset offline updating platform according to the user identification.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring an image information updating request; searching user identity information, historical inquiry flow information and historical inquiry transaction information corresponding to the portrait information updating request; the method comprises the steps of obtaining real-time portrait characteristics according to user identity information and historical inquiry flow information, storing the real-time portrait characteristics to a preset online hive database platform, obtaining offline inquiry characteristics according to historical inquiry transaction information, and storing the offline inquiry characteristics to a preset offline updating platform.
In one embodiment, the processor, when executing the computer program, further performs the steps of: receiving a medical consultation message sent by a terminal; inputting the medical consultation message into a preset semantic recognition model to obtain a recognition result; and when the identification result represents that the medical consultation message is an effective message, searching the user portrait according to the medical consultation message to obtain the user portrait information.
In one embodiment, the processor, when executing the computer program, further performs the steps of: constructing a multi-dimensional feature matrix according to the user feature information; and inputting the multi-dimensional characteristic matrix into a preset user quality evaluation model to obtain user quality parameters corresponding to the user, wherein the preset user quality evaluation model is obtained by an initial xgboost model based on marked historical characteristic data through supervised training.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a user type and historical characteristic data corresponding to a historical user; determining the differentiation characteristics corresponding to various types of users according to the user types corresponding to the historical users and the historical characteristic data; and performing pearson correlation coefficient analysis on the differentiated features to obtain strong correlation feature types corresponding to the historical feature data.
In one embodiment, a computer storage medium is provided, having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of:
receiving a medical consultation message sent by a terminal, and searching a user portrait according to the medical consultation message to obtain user portrait information;
extracting medical consultation information and user characteristic information corresponding to user image information;
acquiring user quality parameters corresponding to the user according to the user characteristic information;
and recommending the target user to the medical service provider according to the user portrait information and the user quality parameters.
In one embodiment, the computer program when executed by the processor further performs the steps of: receiving a medical consultation message sent by a terminal; analyzing the medical consultation message to obtain a user identifier corresponding to the terminal; and extracting real-time portrait characteristics from a preset online hive database platform according to the user identification, and extracting offline inquiry characteristics from a preset offline updating platform according to the user identification.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring an image information updating request; searching user identity information, historical inquiry flow information and historical inquiry transaction information corresponding to the portrait information updating request; the method comprises the steps of obtaining real-time portrait characteristics according to user identity information and historical inquiry flow information, storing the real-time portrait characteristics to a preset online hive database platform, obtaining offline inquiry characteristics according to historical inquiry transaction information, and storing the offline inquiry characteristics to a preset offline updating platform.
In one embodiment, the computer program when executed by the processor further performs the steps of: receiving a medical consultation message sent by a terminal; inputting the medical consultation message into a preset semantic recognition model to obtain a recognition result; and when the identification result represents that the medical consultation message is an effective message, searching the user portrait according to the medical consultation message to obtain the user portrait information.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing a multi-dimensional feature matrix according to the user feature information; and inputting the multi-dimensional characteristic matrix into a preset user quality evaluation model to obtain user quality parameters corresponding to the user, wherein the preset user quality evaluation model is obtained by an initial xgboost model based on marked historical characteristic data through supervised training.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a user type and historical characteristic data corresponding to a historical user; determining the differentiation characteristics corresponding to various types of users according to the user types corresponding to the historical users and the historical characteristic data; and performing pearson correlation coefficient analysis on the differentiated features to obtain strong correlation feature types corresponding to the historical feature data.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of target user recommendation, the method comprising:
receiving a medical consultation message sent by a terminal, and searching a user portrait according to the medical consultation message to obtain user portrait information;
extracting user characteristic information corresponding to the medical consultation message and the user image information;
acquiring user quality parameters corresponding to the user according to the user characteristic information;
and recommending the target user according to the user portrait information and the user quality parameter.
2. The method of claim 1, wherein the user imagery information includes real-time imagery features and offline inquiry features;
the receiving terminal sends medical consultation information, the user portrait is searched according to the medical consultation information, and the user portrait information is obtained by the method comprising the following steps:
receiving a medical consultation message sent by a terminal;
analyzing the medical consultation message to obtain a user identifier corresponding to the terminal;
and extracting the real-time portrait features from a preset online hive database platform according to the user identification, and extracting the offline inquiry features from a preset offline updating platform according to the user identification.
3. The method of claim 1, wherein the user imagery information includes real-time imagery features and offline inquiry features;
the receiving terminal sends medical consultation information, and before searching the user portrait according to the medical consultation information and obtaining user portrait information, the method further comprises the following steps:
acquiring an image information updating request;
searching user identity information, historical inquiry flow information and historical inquiry transaction information corresponding to the portrait information updating request;
and acquiring real-time portrait characteristics according to the user identity information and the historical inquiry flow information, storing the real-time portrait characteristics to a preset online hive database platform, acquiring offline inquiry characteristics according to the historical inquiry transaction information, and storing the offline inquiry characteristics to a preset offline updating platform.
4. The method of claim 3, wherein the receiving of the medical consultation message from the terminal, searching for the user representation based on the medical consultation message, and obtaining the user representation information comprises:
receiving a medical consultation message sent by a terminal;
inputting the medical consultation message into a preset semantic recognition model to obtain a recognition result;
and when the identification result represents that the medical consultation message is an effective message, searching the user portrait according to the medical consultation message to obtain user portrait information.
5. The method according to claim 1, wherein the obtaining the user quality parameter corresponding to the user according to the user characteristic information comprises:
constructing a multi-dimensional feature matrix according to the user feature information;
and inputting the multi-dimensional characteristic matrix into a preset user quality evaluation model to obtain user quality parameters corresponding to the user, wherein the preset user quality evaluation model is obtained by an initial xgboost model based on marked historical characteristic data through supervised training.
6. The method of claim 5, wherein before the constructing the multi-dimensional feature matrix according to the user feature information, the method further comprises:
acquiring a user type and historical characteristic data corresponding to a historical user;
determining the differentiation characteristics corresponding to each type of user according to the user type corresponding to the historical user and the historical characteristic data;
performing pearson correlation coefficient analysis on the differential features to obtain strong correlation feature types corresponding to the historical feature data;
the constructing of the multi-dimensional feature matrix according to the user feature information includes:
and constructing a multi-dimensional feature matrix according to the user feature information corresponding to the strongly correlated feature type.
7. A target user recommendation apparatus, the apparatus comprising:
the information searching module is used for receiving the medical consultation message sent by the terminal and searching the user portrait according to the medical consultation message to obtain user portrait information;
the characteristic extraction module is used for extracting the medical consultation message and the user characteristic information corresponding to the user image information;
the quality parameter evaluation module is used for acquiring the user quality parameters corresponding to the user according to the user characteristic information;
and the target recommending module is used for recommending a target user to a medical service provider according to the user portrait information and the user quality parameter.
8. The apparatus of claim 7, wherein the user profile information comprises a real-time profile feature and an offline interrogation feature, and wherein the information lookup module is configured to:
receiving a medical consultation message sent by a terminal;
analyzing the medical consultation message to obtain a user identifier corresponding to the terminal;
and extracting the real-time portrait features from a preset online hive database platform according to the user identification, and extracting the offline inquiry features from a preset offline updating platform according to the user identification.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
10. A computer storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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