CN112017775A - Information recommendation method and device, computer equipment and storage medium - Google Patents
Information recommendation method and device, computer equipment and storage medium Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The application discloses an information recommendation method, an information recommendation device, computer equipment and a storage medium, wherein the method is suitable for the field of digital medical treatment and comprises the following steps: acquiring target user attribute information and target knowledge intake information of a target user; determining a target knowledge reserve level of a target user based on the target user attribute information and the target knowledge intake information, and determining information to be displayed from multiple groups of knowledge reserve information of the target application based on the target knowledge reserve level; and outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user. By the method and the device, the required information to be displayed can be accurately pushed to the user in the target application, and efficiency is high.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an information recommendation method and apparatus, a computer device, and a storage medium.
Background
Currently, a Clinical Decision Support System (CDSS) is an interactive system for assisting medical staff in making medical decisions, and a doctor can input information of a patient into the CDSS, and then the CDSS generates a customized suggestion for the physical condition of the patient according to the input information of the patient, and then the doctor selects useful information from the customized suggestion to treat the patient, so as to implement clinical decision assistance for the doctor. However, since the personal condition of the doctor and the requirement for the CDSS are different, when the doctor inputs the same data into the CDSS, the recommendation generated by the CDSS is always the same and single, so that the CDSS cannot generate different recommendations according to different requirements of the doctor, and the efficiency is low.
Disclosure of Invention
The application provides an information recommendation method, an information recommendation device, computer equipment and a storage medium, which can accurately push information to be displayed required by a user in a target application and are high in efficiency.
In a first aspect, the present application provides an information recommendation method, including:
acquiring target user attribute information and target knowledge ingestion information of a target user, wherein the target knowledge ingestion information is information ingested by the target user from a target application in a specified period;
determining a target knowledge reserve level of a target user based on the target user attribute information and the target knowledge intake information, determining information to be displayed from multiple groups of knowledge reserve information of a target application based on the target knowledge reserve level, wherein one group of knowledge reserve information in the multiple groups of knowledge reserve information corresponds to one knowledge reserve level;
and outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
With reference to the first aspect, in one possible implementation, determining a target knowledge reserve level of a target user based on target user attribute information and target knowledge intake information includes:
determining a first knowledge reserve level of the target user based on the target user attribute information;
determining a second knowledge reserve level of the target user based on the target knowledge intake information;
and determining a first knowledge reserve parameter, and determining a target knowledge reserve level of the target user based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter.
With reference to the first aspect, in one possible implementation, determining a first knowledge reserve level of a target user based on target user attribute information includes:
the method comprises the steps of inputting target user attribute information into a first-grade evaluation model, determining a first knowledge reserve grade of a target user based on the first-grade evaluation model, wherein the first-grade evaluation model is obtained by training first sample data, and the first sample data comprises user attribute information of at least two users.
With reference to the first aspect, in one possible implementation, determining a second knowledge reserve level of a target user based on target knowledge intake information includes:
and inputting the target knowledge ingestion information into a second-level evaluation model, determining a second knowledge reserve level of the target user based on the second-level evaluation model, wherein the second-level evaluation model is obtained by training second sample data, and the second sample data comprises information ingested by at least two users from the target application.
With reference to the first aspect, in one possible implementation, determining a target knowledge reserve level of a target user based on a first knowledge reserve level, a second knowledge reserve level, and a first knowledge reserve parameter includes:
determining a target knowledge reserve level K of the target user according to the following formula based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter:
K＝(K1+k*K2)/(1+k)
wherein, K1For representing a first knowledge reserve level, K2For representing the second knowledge reserve level, k for representing the first knowledge reserve parameter, k being greater than or equal to 0 and less than or equal to 1.
With reference to the first aspect, in a possible implementation manner, determining information to be presented from multiple sets of knowledge reserve information in a target application based on a target knowledge reserve level includes:
determining a second knowledge reserve parameter, and determining a knowledge reserve grade range based on the target knowledge reserve grade and the second knowledge reserve parameter;
determining at least one knowledge reserve level in the knowledge reserve level range according to the knowledge reserve levels corresponding to the multiple groups of knowledge reserve information in the target application;
and determining at least one group of knowledge reserve information corresponding to at least one knowledge reserve level as the information to be displayed.
With reference to the first aspect, in one possible implementation, the target user attribute information includes at least one of a medical year, a doctor's job title, a history, a specialty, and a knowledge reserve level of the target user;
the target knowledge ingestion information includes at least one of the use frequency of the target user for the target application, knowledge reserve level change information corresponding to the ingestion information, and feedback information.
With reference to the first aspect, in a possible implementation manner, the target user attribute information includes a knowledge reserve level of the target user;
determining a first level of knowledge reserve for the target user based on the target user attribute information, comprising:
and acquiring the knowledge reserve level of the target user from the target application and determining the knowledge reserve level as a first knowledge reserve level of the target user.
With reference to the first aspect, in a possible implementation manner, determining information to be presented from multiple sets of knowledge reserve information in a target application based on a target knowledge reserve level includes:
and determining target knowledge reserve information with the knowledge reserve level as the target knowledge reserve level from the multiple groups of knowledge reserve information of the target application, and determining the target knowledge reserve information as the information to be displayed.
With reference to the first aspect, in one possible implementation manner, after determining the target knowledge reserve level of the target user based on the target user attribute information and the target knowledge intake information, the method further includes:
and updating the knowledge reserve level of the target user stored in the target application to the target knowledge reserve level.
In a second aspect, the present application provides an information recommendation apparatus, including:
the acquisition module is used for acquiring target user attribute information and target knowledge ingestion information of a target user, wherein the target knowledge ingestion information is information ingested by the target user from a target application in a specified period;
the determining module is used for determining a target knowledge reserve level of a target user based on the target user attribute information and the target knowledge intake information, determining information to be displayed from multiple groups of knowledge reserve information of the target application based on the target knowledge reserve level, wherein one group of knowledge reserve information in the multiple groups of knowledge reserve information corresponds to one knowledge reserve level;
and the information display module is used for outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
With reference to the second aspect, in one possible implementation, the determining module includes:
a first rank determination unit for determining a first knowledge reserve rank of the target user based on the target user attribute information;
a second rank determination unit configured to determine a second knowledge reserve rank of the target user based on the target knowledge intake information;
and the third level determination unit is used for determining the first knowledge reserve parameter and determining the target knowledge reserve level of the target user based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter.
With reference to the second aspect, in one possible implementation, the first rank determining unit includes:
the first rank determination subunit is configured to input the target user attribute information into a first rank evaluation model, and determine a first knowledge storage rank of the target user based on the first rank evaluation model, where the first rank evaluation model is obtained by training first sample data, and the first sample data includes user attribute information of at least two users.
With reference to the second aspect, in a possible implementation manner, the target user attribute information includes a knowledge reserve level of the target user;
the first rank determination unit includes:
and the second level determining subunit is used for acquiring the knowledge reserve level of the target user from the target application and determining the knowledge reserve level as the first knowledge reserve level of the target user.
With reference to the second aspect, in one possible implementation, the second rank determining unit includes:
and the third-level determining subunit is used for inputting the target knowledge ingestion information into a second-level evaluation model, determining a second knowledge reserve level of the target user based on the second-level evaluation model, wherein the second-level evaluation model is obtained by training second sample data, and the second sample data comprises information ingested by at least two users from the target application.
With reference to the second aspect, in one possible implementation, the third level determining unit includes:
a fourth level determination subunit, configured to determine a target knowledge reserve level K of the target user according to the following formula based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter:
K＝(K1+k*K2)/(1+k)
wherein, K1For representing a first knowledge reserve level, K2For representing the second knowledge reserve level, k for representing the first knowledge reserve parameter, k being greater than or equal to 0 and less than or equal to 1.
With reference to the second aspect, in one possible implementation, the determining module includes:
the first information determining unit is used for determining target knowledge reserve information with a knowledge reserve level as a target knowledge reserve level from a plurality of groups of knowledge reserve information of the target application, and determining the target knowledge reserve information as information to be displayed.
With reference to the second aspect, in one possible implementation, the determining module includes:
the range determining unit is used for determining a second knowledge reserve parameter and determining the range of the knowledge reserve level based on the target knowledge reserve level and the second knowledge reserve parameter;
the fourth level determining unit is used for determining at least one knowledge reserve level in the knowledge reserve level range according to the knowledge reserve levels corresponding to the multiple groups of knowledge reserve information in the target application;
and the second information determining unit is used for determining at least one group of knowledge reserve information corresponding to at least one knowledge reserve level as the information to be displayed.
With reference to the second aspect, in one possible implementation, the target user attribute information includes at least one of a medical year, a doctor's job title, a history, a specialty, and a knowledge reserve level of the target user;
the target knowledge ingestion information includes at least one of the use frequency of the target user for the target application, knowledge reserve level change information corresponding to the ingestion information, and feedback information.
With reference to the second aspect, in a possible implementation manner, the apparatus further includes:
and the level updating module is used for updating the knowledge reserve level of the target user stored in the target application to the target knowledge reserve level.
In a third aspect, the present application provides a computer device comprising: a processor, a memory, a network interface;
the processor is connected with a memory and a network interface, wherein the network interface is used for providing a data communication function, the memory is used for storing a computer program, and the processor is used for calling the computer program to execute the information recommendation method in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program comprising program instructions that, when executed by a processor, perform the information recommendation method of the first aspect described above in the present application.
In the application, the computer device can determine the target knowledge reserve level of the target user after acquiring the target user attribute information and the target knowledge intake information of the target user, and the target knowledge reserve level (such as the medical knowledge level of a doctor) can be subsequently used for determining the information to be displayed in multiple groups of knowledge reserve information of the target application, so that the information recommendation accuracy can be improved. At this time, the computer device can determine information to be presented (such as medical knowledge information for any disease) from a plurality of sets of knowledge reserve information of a target application (such as CDSS) based on a target knowledge reserve level, the information to be presented may then be output to a user interface of the target application to present the information to be presented to the target user, so that the information to be presented according to the requirements (such as medical knowledge level) of the users with different requirements can be pushed to the users with different requirements in the user interface of the target application, for example, when the target reserve knowledge level of the user is higher, some complex medical knowledge information can be pushed in the user interface, when the target reserve knowledge level of the user is low, some basic medical knowledge information can be pushed in the user interface, so that different requirements of different users are met, and the information recommendation efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of a network architecture provided herein;
FIG. 2 is a flow chart of an information recommendation method provided in the present application;
FIG. 3 is another schematic flow chart diagram of an information recommendation method provided herein;
FIG. 4 is a schematic structural diagram of an information recommendation device provided in the present application;
fig. 5 is a schematic structural diagram of a computer device provided in the present application.
Detailed Description
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings in the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a network architecture provided in the present application. As shown in fig. 1, the network architecture may include a server 10 and a user terminal cluster, and the user terminal cluster may include a plurality of user terminals, as shown in fig. 1, and specifically may include a user terminal 100a, a user terminal 100b, user terminals 100c, …, and a user terminal 100 n.
The server 10 may be an independent physical server, or may be a cloud server that provides basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data, and an artificial intelligence platform. Each user terminal in the user terminal cluster may include, but is not limited to: intelligent terminals such as smart phones, tablet computers, notebook computers, desktop computers, intelligent sound boxes and intelligent watches.
It is understood that the computer device in the present application may be an entity terminal having an information recommendation function, and the entity terminal may be the server 10 shown in fig. 1, or may be a user terminal, which is not limited herein.
As shown in fig. 1, the user terminal 100a, the user terminal 100b, the user terminals 100c, …, and the user terminal 100n may be respectively connected to the server 10 via a network, so that each user terminal may interact with the server 10 via the network. For example, the server 10 may output the information to be presented to a user interface of a target application in a user terminal where a target user is located, so that the target user views the information to be presented on the user interface, where the user terminal of the target user may be any one user terminal (e.g., the user terminal 100a) in a user terminal cluster, and the user terminal is loaded with the target application, where the user interface of the target application may be presented through the user terminal interface, and for convenience of description, the user interface of the target application may be directly illustrated here. The application with the information recommendation function can be collectively called as a target application, and the knowledge reserve information used for showing the target user in various knowledge reserve information of the target application can be collectively called as information to be shown. At this time, the target user may view the information to be presented in the user interface, and may also send feedback suggestion information for the information to be presented to the server 10.
The information recommendation method provided by the application can be applied to information recommendation scenes of any kind of information, such as medical information recommendation scenes, book information recommendation scenes, commodity information recommendation scenes or other information recommendation scenes, for convenience of description, a medical information recommendation scene (a target user can be a doctor and a target application can be a CDSS in the medical information recommendation scene) is taken as an example for explanation, and details are not repeated below. The information recommendation method, the information recommendation apparatus, and the computer device of the present application will be described below with reference to fig. 2 to 5.
Referring to fig. 2, fig. 2 is a flow chart of the information recommendation method provided in the present application. The method may be performed by a computer device, which may be a user terminal; optionally, the computer device may also be the server 10 in the embodiment corresponding to fig. 1, which is not limited here. The present application will be described by taking a computer device as an example, and the method shown in fig. 2 may include the following steps S101 to S103:
step S101, acquiring target user attribute information and target knowledge intake information of a target user.
In some possible embodiments, the present application may collectively refer to a plurality of basic information entered by a target user when using a target application as target user attribute information. The information that a target user (such as a doctor) takes from the target application in a specified period can be collectively referred to as target knowledge taking information, the specified period can be set for the target user according to the requirement of the target user, and can also be a default period in the target application, and can be specifically determined according to the actual application scene, without limitation. For example, the specified period may be 1 day, 1 week, or 1 month. The target application may be a CDSS, which is an interactive system for assisting medical staff (such as the target user) to make medical decisions, and the CDSS may include various medical knowledge information, such as but not limited to various medical knowledge information for different diseases and historical medical information of patients in the past, etc. The target user attribute information may include, but is not limited to, at least one or any combination of the years of medical practice of the doctor, the titles of the doctor, the history (e.g., whether the doctor has worked in the medical, preventive, and health care institutions), the specialties (e.g., whether the doctor has worked in the medical specialties), the knowledge reserve level, the academic records (e.g., the specialist, the subject, the researcher, or the doctor) and whether the medical practice is qualified, and may be determined according to the actual application scenario, which is not limited herein. The level of reserve of knowledge in the target user attribute information may be a level of reserve of knowledge of the target user stored in the target application, and the physician's job title may include a primary job title (e.g., physician/inpatient), a secondary job title (e.g., attending physician), a higher order job title (e.g., chief physician). The target knowledge intake information may include, but is not limited to, a frequency of use of the target application by the target user, knowledge reserve level change information corresponding to the intake information from the target application, and at least one or more of feedback information (e.g., suggested feedback information of medical knowledge information for any disease) for using the target application, which may be determined according to an actual application scenario, and is not limited herein. The target application may include multiple sets of knowledge reserve information, and one set of knowledge reserve information in the multiple sets of knowledge reserve information corresponds to one knowledge reserve level, that is, different knowledge reserve levels corresponding to different knowledge reserve information are different, assuming that the multiple sets of knowledge reserve information are different medical knowledge information for any one disease, the knowledge reserve level corresponding to the basic medical knowledge information is lower, and the knowledge reserve level corresponding to the complex medical knowledge information is higher. The knowledge reserve level may be a value for representing the medical knowledge level of the target user, and a larger value may indicate a higher knowledge reserve level of the target user, for example, the knowledge reserve level may be a value in the range of 1-100.
And S102, determining a target knowledge reserve level of the target user based on the target user attribute information and the target knowledge intake information, and determining information to be displayed from multiple groups of knowledge reserve information of the target application based on the target knowledge reserve level.
In some possible implementations, the computer device may determine the target level of knowledge reserve for the target user through a model and/or a stored level of knowledge reserve for the target user in the target application, where the target level of knowledge reserve may be used to indicate the current medical knowledge level of the target user (informativedgetstate, IKS).
In some possible implementations, after determining the target level of knowledge reserve for the target user, the computer device may update the level of knowledge reserve for the target user stored in the target application to the target level of knowledge reserve. It is understood that during the use of the target application by the target user, the computer device may continuously update the target user's knowledge reserve level stored in the target application according to the target user's target knowledge reserve level over different time periods.
In some possible embodiments, the computer device may use, as the information to be displayed, knowledge reserve information in which the knowledge reserve level in the target application is the target knowledge reserve level or knowledge reserve information in which the knowledge reserve level is within a certain knowledge reserve level range including the target knowledge reserve level, and may specifically be determined according to an actual application scenario, which is not limited herein. It can be understood that the computer device may determine, from the multiple sets of knowledge reserve information of the target application, target knowledge reserve information whose knowledge reserve level is the target knowledge reserve level, and determine the target knowledge reserve information as information to be displayed. Specifically, the computer device may determine, in knowledge reserve levels corresponding to multiple sets of knowledge reserve information in the target application, a knowledge reserve level that is the same as the target knowledge reserve level, and use the knowledge reserve information corresponding to the knowledge reserve level that is the same as the target knowledge reserve level as the target knowledge reserve information, thereby determining the target knowledge reserve information as information to be displayed. Alternatively, the computer device may determine a second knowledge reserve parameter and determine a knowledge reserve level range based on the target knowledge reserve level and the second knowledge reserve parameter. The second knowledge storage parameter may be a parameter preset in the target application by the target user, or may also be a default parameter in the target application, and may be specifically determined according to an actual application scenario, which is not limited herein. After determining the range of knowledge reserve levels, the computer device may determine at least one knowledge reserve level within the range of knowledge reserve levels at the knowledge reserve levels corresponding to the plurality of sets of knowledge reserve information in the target application. Further, the computer device may determine at least one set of knowledge reserve information corresponding to the at least one knowledge reserve level as the information to be presented. Specifically, the computer device may determine a second knowledge reserve parameter, and use a difference between the target knowledge reserve level and the second knowledge reserve parameter and a sum of the target knowledge reserve level and the second knowledge reserve parameter as both ends of the knowledge reserve level range, respectively, to obtain the knowledge reserve level range. Assuming that the target knowledge reserve level can be represented as K and the second knowledge reserve parameter can be represented as T, the knowledge reserve level range can be represented as K-T, K + T. After determining the range of the knowledge reserve levels, the computer device may determine one or more knowledge reserve levels within the range of the knowledge reserve levels from among the knowledge reserve levels corresponding to the plurality of sets of knowledge reserve information, and determine at least one set of knowledge reserve information corresponding to the one or more knowledge reserve levels as the information to be presented. Assuming that the knowledge reserve level range is [ K-T, K + T ] as described above, the computer device may use at least one set of knowledge reserve information (e.g., different medical knowledge information for any disease) corresponding to a knowledge reserve level greater than or equal to K-T and less than or equal to K + T among the knowledge reserve levels as the information to be displayed. The information to be displayed can be subsequently used for being displayed to the target user on the user interface of the target application, so that the computer device can push the information to be displayed (such as medical knowledge information) meeting the requirements (such as medical knowledge level) of the user (such as a doctor) with different requirements to the user (such as the doctor), for example, when the target reserve knowledge level of the doctor is higher, the computer device can push some complex medical knowledge information to the doctor, and conversely, when the target reserve knowledge level of the doctor is lower, the computer device can push some basic medical knowledge information to the doctor, so that different requirements of different users are met, and meanwhile, the individualization and the usability of the target application are improved.
And step S103, outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
It can be understood that the computer device may output the information to be presented to the user interface of the target application, so that the target user views the information to be presented in the user interface. At this time, after the target user views the information to be presented in the user interface, the target user may send feedback suggestion information for the information to be presented to the computer device.
In the application, the computer device can determine the target knowledge reserve level of the target user after acquiring the target user attribute information and the target knowledge intake information of the target user, and the target knowledge reserve level (such as the medical knowledge level of a doctor) can be subsequently used for determining the information to be displayed in multiple groups of knowledge reserve information of the target application, so that the information recommendation accuracy can be improved. At this time, the computer device can determine information to be presented (such as medical knowledge information for any disease) from a plurality of sets of knowledge reserve information of a target application (such as CDSS) based on a target knowledge reserve level, the information to be presented may then be output to a user interface of the target application to present the information to be presented to the target user, so that the information to be presented according to the requirements (such as medical knowledge level) of the users with different requirements can be pushed to the users with different requirements in the user interface of the target application, for example, when the target reserve knowledge level of the user is higher, some complex medical knowledge information can be pushed in the user interface, when the target reserve knowledge level of the user is low, some basic medical knowledge information can be pushed in the user interface, so that different requirements of different users are met, and the information recommendation efficiency is improved.
Referring to fig. 3, fig. 3 is another schematic flow chart of the information recommendation method provided in the present application. The method may be performed by a computer device, which may be a user terminal; optionally, the computer device may also be the server 10 in the embodiment corresponding to fig. 1, which is not limited here. The present application will be described by taking a computer device as an example, and the method shown in fig. 3 may include the following steps S201 to S205:
in step S201, target user attribute information and target knowledge intake information of a target user are acquired.
For a specific implementation of step S201, reference may be made to the description of step S101 in the embodiment corresponding to fig. 2, which will not be described herein again.
Step S202, a first knowledge reserve level of the target user is determined based on the attribute information of the target user.
In some possible embodiments, the computer device may determine the first knowledge reserve level of the target user through a model (e.g., a first level assessment model described below) or a stored knowledge reserve level of the target user in the target application. The first knowledge reserve level may be a value for characterizing the initial medical knowledge level of the target user before using the target application, and a larger value may indicate a higher initial medical knowledge level of the doctor, for example, the first knowledge reserve level may be a value in the range of 1-100. It is understood that the computer device may determine a first knowledge storage level of the target user based on a first level evaluation model, where the first level evaluation model is trained from first sample data, and the first sample data includes user attribute information of at least two users. Specifically, the computer device may obtain first sample data, perform data cleaning and feature screening on the first sample data, and finally form some feature data about evaluating the first knowledge reserve level of any user. The feature data may include, but is not limited to, feature data of multiple dimensions of the medical years, the doctor's titles, the resume, the profession, the knowledge reserve level, the academic history, and whether the medical practitioners pass the qualification tests, etc. of any user, and is not limited herein. Further, the computer device may use the first sample data as an input to a first level assessment model through which the first sample data is learned to obtain an ability to assess a first level of knowledge reserve for any user. In other words, the computer device may perform data feature learning on the feature data based on the first level assessment model to obtain an ability to assess the first level of knowledge reserve of any user. After obtaining the first level assessment model, the computer device may input the target user attribute information into the first level assessment model and determine a first level of knowledge storage for the target user based on the first level assessment model.
Optionally, since the target user attribute information includes the knowledge reserve level of the target user (where the knowledge reserve level may be a level entered into the target application for the target user), the computer device may directly obtain the knowledge reserve level of the target user from the target application, and use the knowledge reserve level as the first knowledge reserve level of the target user after obtaining the knowledge reserve level of the target user.
Step S203, determining a second knowledge reserve level of the target user based on the target knowledge intake information.
In some possible implementations, the computer device may input the target knowledge intake information into a second level evaluation model, determine a second knowledge reserve level of the target user based on the second level evaluation model, the second level evaluation model being trained from second sample data including information that at least two users took from the target application. The second knowledge reserve level may be a value for representing the amount of information that the target user has ingested from the target application, and a larger value indicates that the doctor has ingested more medical knowledge from the CDSS, for example, the second knowledge reserve level may be a value in the range of 1-100. The second sample data may also be information that at least two users take from the target application in a certain period, for example, the certain period may be 1 day, 1 week or 1 month. The information ingested by at least two users from the target application may be referred to herein simply as knowledge ingestion information. Specifically, the computer device may obtain second sample data, perform data cleaning and feature screening on the second sample data, and finally form some feature data about evaluating a second knowledge reserve level of any user. The feature data may include, but is not limited to, feature data of multiple dimensions, such as a frequency of use of the target application by the target user, knowledge reserve level change information corresponding to information captured from the target application, and feedback information of the target application, and the like. Further, the computer device may use the second sample data as an input to the second level assessment model, and learn the second sample data through the second level assessment model to obtain an ability to assess a second level of knowledge reserve of any user. In other words, the computer device may perform data feature learning on the feature data based on the second-level assessment model to obtain an ability to assess the second level of knowledge reserve of any user. After obtaining the second-level assessment model, the computer device may enter the target knowledge intake information into the second-level assessment model and determine a second level of knowledge reserve for the target user based on the second-level assessment model.
Step S204, determining a first knowledge reserve parameter, and determining a target knowledge reserve level of the target user based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter.
In some possible embodiments, after determining the first knowledge reserve parameter, the computer device may determine a target knowledge reserve level for the target user based on any combination of the first knowledge reserve level, the second knowledge reserve level, and the first knowledge reserve parameter. It will be appreciated that the formula for the computer device to determine the target knowledge reserve level K for the target user may be as shown in equation (1) below:
K＝(K1+k*K2)/(1+k)， (1)
wherein, K1For representing a first knowledge reserve level, K2For representing the second knowledge reserve level, k for representing the firstThe knowledge reserve parameter k is greater than or equal to 0 and less than or equal to 1, where the first knowledge reserve parameter k may be a parameter preset by the target user, and may also be a default parameter. The target knowledge reserve level K may be a numerical value for characterizing the medical knowledge level of the target user in using the target application, and a larger numerical value indicates a higher current medical knowledge level of the doctor, for example, the target knowledge reserve level K may be a numerical value in the range of 1-100.
Optionally, the computer device may further determine the target knowledge reserve level of the target user according to the first knowledge reserve level and the second knowledge reserve level, for example, the computer device may use the sum between the first knowledge reserve level and the second knowledge reserve level as the target knowledge reserve level of the target user, and may specifically be determined according to an actual application scenario, which is not limited herein.
Step S205, outputting the information to be displayed to a user interface of the target application to display the information to be displayed to the target user.
The specific implementation of step S205 may refer to the description of step S103 in the embodiment corresponding to fig. 2, and will not be described again here.
In the application, the computer device can determine the target knowledge reserve level of the target user after acquiring the target user attribute information and the target knowledge intake information of the target user, and the target knowledge reserve level (such as the medical knowledge level of a doctor) can be subsequently used for determining the information to be displayed in multiple groups of knowledge reserve information of the target application, so that the information recommendation accuracy can be improved. At this time, the computer device can determine information to be presented (such as medical knowledge information for any disease) from a plurality of sets of knowledge reserve information of a target application (such as CDSS) based on a target knowledge reserve level, the information to be presented may then be output to a user interface of the target application to present the information to be presented to the target user, so that the information to be presented according to the requirements (such as medical knowledge level) of the users with different requirements can be pushed to the users with different requirements in the user interface of the target application, for example, when the target reserve knowledge level of the user is higher, some complex medical knowledge information can be pushed in the user interface, when the target reserve knowledge level of the user is low, some basic medical knowledge information can be pushed in the user interface, so that different requirements of different users are met, and the information recommendation efficiency is improved.
Further, please refer to fig. 4, fig. 4 is a schematic structural diagram of the information recommendation device provided in the present application. The information recommendation device may be a computer program (including program code) running in a computer apparatus, for example, the information recommendation device is an application software; the information recommendation device can be used for executing corresponding steps in the method provided by the application. As shown in fig. 4, the information recommendation apparatus 1 may be applied to a computer device, and the information recommendation apparatus 1 may include: the system comprises an acquisition module 10, a determination module 20, an information presentation module 30 and a grade update module 40.
The acquisition module 10 is configured to acquire target user attribute information and target knowledge ingestion information of a target user, where the target knowledge ingestion information is information ingested by the target user from a target application in a specified period;
the determining module 20 is configured to determine a target knowledge reserve level of the target user based on the target user attribute information and the target knowledge intake information, determine information to be displayed from multiple sets of knowledge reserve information of the target application based on the target knowledge reserve level, where one set of knowledge reserve information in the multiple sets of knowledge reserve information corresponds to one knowledge reserve level;
and the information display module 30 is configured to output the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
The target user attribute information comprises at least one of the medical years, the doctor titles, the curriculum vitae, the professions and the knowledge reserve levels of the target user;
the target knowledge ingestion information includes at least one of the use frequency of the target user for the target application, knowledge reserve level change information corresponding to the ingestion information, and feedback information.
In some possible embodiments, the determining module 20 includes: a first level determination unit 201, a second level determination unit 202, and a third level determination unit 203.
A first rank determination unit 201 for determining a first knowledge reserve level of a target user based on target user attribute information;
a second rank determination unit 202 configured to determine a second knowledge reserve rank of the target user based on the target knowledge intake information;
and a third level determination unit 203, configured to determine the first knowledge reserve parameter, and determine a target knowledge reserve level of the target user based on the first knowledge reserve level, the second knowledge reserve level, and the first knowledge reserve parameter.
For specific implementation manners of the first level determining unit 201, the second level determining unit 202, and the third level determining unit 203, reference may be made to the description of step S202 to step S204 in the embodiment corresponding to fig. 3, and details will not be further described here.
In some possible embodiments, the first rank determining unit 201 includes: the first rank determines the child unit 2011.
The first rank determination subunit 2011 is configured to input the attribute information of the target user into a first rank evaluation model, and determine a first knowledge storage rank of the target user based on the first rank evaluation model, where the first rank evaluation model is obtained by training first sample data, and the first sample data includes user attribute information of at least two users.
For a specific implementation manner of the first rank determination subunit 2011, reference may be made to the description of step S202 in the embodiment corresponding to fig. 3, which will not be further described here.
In some possible embodiments, the target user attribute information includes a knowledge reserve level of the target user; the first rank determination unit 201 includes: the second stage defines the subunits 2012.
And a second rank determining subunit 2012, configured to obtain the knowledge reserve level of the target user from the target application and determine the knowledge reserve level as the first knowledge reserve level of the target user.
For a specific implementation manner of the second-level determining subunit 2012, refer to the description of step S202 in the embodiment corresponding to fig. 3, which will not be further described here.
In some possible embodiments, the second rank determination unit 202 includes: the third stage defines the sub-unit 2021.
The third-level determining subunit 2021 is configured to input the target knowledge intake information into a second-level evaluation model, and determine a second knowledge reserve level of the target user based on the second-level evaluation model, where the second-level evaluation model is trained from second sample data, and the second sample data includes information that is taken by at least two users from the target application.
For a specific implementation manner of the third-level determining subunit 2021, reference may be made to the description of step S203 in the embodiment corresponding to fig. 3, and details will not be further described here.
In some possible embodiments, the third level determining unit 203 includes: the fourth level determines the subunit 2031.
A fourth level determining subunit 2031, configured to determine a target knowledge reserve level K of the target user according to the following formula based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter:
K＝(K1+k*K2)/(1+k)
wherein, K1For representing a first knowledge reserve level, K2For representing the second knowledge reserve level, k for representing the first knowledge reserve parameter, k being greater than or equal to 0 and less than or equal to 1.
For a specific implementation manner of the fourth level determining subunit 2031, refer to the description of step S204 in the embodiment corresponding to fig. 3, and details will not be further described here.
In some possible embodiments, the determining module 20 includes: a first information determination unit 204.
The first information determining unit 204 is configured to determine, from multiple sets of knowledge reserve information of the target application, target knowledge reserve information whose knowledge reserve level is the target knowledge reserve level, and determine the target knowledge reserve information as information to be displayed.
For a specific implementation manner of the first information determining unit 204, reference may be made to the description of step S103 in the embodiment corresponding to fig. 2, and details will not be further described here.
In some possible embodiments, the determining module 20 includes: a range determination unit 205, a fourth level determination unit 206, and a second information determination unit 207.
A range determination unit 205 for determining a second knowledge reserve parameter, and determining a knowledge reserve level range based on the target knowledge reserve level and the second knowledge reserve parameter;
a fourth rank determining unit 206, configured to determine at least one knowledge reserve rank within a knowledge reserve rank range according to knowledge reserve ranks corresponding to multiple sets of knowledge reserve information in the target application;
the second information determining unit 207 is configured to determine at least one group of knowledge reserve information corresponding to at least one knowledge reserve level as information to be displayed.
For specific implementation manners of the range determining unit 205, the fourth level determining unit 206, and the second information determining unit 207, reference may be made to the description of step S103 in the embodiment corresponding to fig. 2, and details will not be further described here.
In some possible embodiments, the information recommendation device 1 further includes:
and the level updating module 40 is used for updating the knowledge reserve level of the target user stored in the target application to the target knowledge reserve level.
For specific implementation of the obtaining module 10, the determining module 20, the information displaying module 30, and the level updating module 40, reference may be made to the description of steps S101 to S103 in the embodiment corresponding to fig. 2 and/or the description of steps S201 to S205 in the embodiment corresponding to fig. 3, which will not be further described here. In addition, the beneficial effects of the same method are not described in detail.
Further, please refer to fig. 5, fig. 5 is a schematic structural diagram of the computer device provided in the present application. As shown in fig. 5, the computer apparatus 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a display (display) and a keyboard (keyboard), and the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a Random Access Memory (RAM) memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 1005 may optionally also be at least one storage device located remotely from the aforementioned processor 1001. As shown in fig. 5, the memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a device control application program.
In the computer apparatus 1000 shown in fig. 5, the network interface 1004 is mainly used for network communication of the user terminal; the user interface 1003 is an interface for providing a user with input; and the processor 1001 may be used to invoke a device control application stored in the memory 1005 to implement:
acquiring target user attribute information and target knowledge ingestion information of a target user, wherein the target knowledge ingestion information is information ingested by the target user from a target application in a specified period;
determining a target knowledge reserve level of a target user based on the target user attribute information and the target knowledge intake information, determining information to be displayed from multiple groups of knowledge reserve information of a target application based on the target knowledge reserve level, wherein one group of knowledge reserve information in the multiple groups of knowledge reserve information corresponds to one knowledge reserve level;
and outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
It should be understood that the computer device 1000 described in this application may perform the description of the information recommendation method in the embodiment corresponding to fig. 2 and/or fig. 3, and may also perform the description of the information recommendation apparatus 1 in the embodiment corresponding to fig. 4, which is not described herein again. In addition, the beneficial effects of the same method are not described in detail.
Further, here, it is to be noted that: the present application further provides a computer-readable storage medium, and the computer-readable storage medium stores therein the aforementioned computer program executed by the information recommendation apparatus 1, and the computer program includes program instructions, and when the processor executes the program instructions, the description of the information recommendation method in the embodiment corresponding to fig. 2 and/or fig. 3 can be executed, so that details will not be repeated here. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in embodiments of the computer-readable storage medium referred to in the present application, reference is made to the description of embodiments of the method of the present application. As an example, program instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network, which may comprise a block chain system.
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 a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The computer-readable storage medium may be the information recommendation apparatus provided in any of the foregoing embodiments or an internal storage unit of the foregoing device, for example, a hard disk or a memory of an electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash card (flash card), and the like, which are provided on the electronic device. The computer readable storage medium may further include a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (ram), or the like. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the electronic device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the electronic device. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. An information recommendation method, comprising:
acquiring target user attribute information and target knowledge ingestion information of a target user, wherein the target knowledge ingestion information is information ingested by the target user from a target application in a specified period;
determining a target knowledge reserve level of the target user based on the target user attribute information and the target knowledge intake information, determining information to be displayed from multiple groups of knowledge reserve information of the target application based on the target knowledge reserve level, wherein one group of knowledge reserve information in the multiple groups of knowledge reserve information corresponds to one knowledge reserve level;
and outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
2. The method of claim 1, wherein determining the target level of knowledge reserve for the target user based on the target user attribute information and the target knowledge intake information comprises:
determining a first knowledge reserve level of the target user based on the target user attribute information;
determining a second knowledge reserve level for the target user based on the target knowledge intake information;
determining a first knowledge reserve parameter, and determining a target knowledge reserve level for the target user based on the first knowledge reserve level, the second knowledge reserve level, and the first knowledge reserve parameter.
3. The method of claim 2, wherein determining the first level of knowledge reserve for the target user based on the target user attribute information comprises:
inputting the attribute information of the target user into a first-level evaluation model, determining a first knowledge reserve level of the target user based on the first-level evaluation model, wherein the first-level evaluation model is obtained by training first sample data, and the first sample data comprises the user attribute information of the at least two users.
4. The method of claim 2, wherein the determining a second level of knowledge reserve for the target user based on the target knowledge intake information comprises:
inputting the target knowledge ingestion information into a second-level evaluation model, and determining a second knowledge reserve level of the target user based on the second-level evaluation model, wherein the second-level evaluation model is obtained by training second sample data, and the second sample data comprises information ingested by the at least two users from the target application.
5. The method of claim 2, wherein determining the target level of knowledge reserve for the target user based on the first level of knowledge reserve, the second level of knowledge reserve, and the first level of knowledge reserve parameter comprises:
determining a target knowledge reserve level K of the target user based on the first knowledge reserve level, the second knowledge reserve level and the first knowledge reserve parameter according to the following formula:
K＝(K1+k*K2)/(1+k)
wherein, K1For representing said first level of knowledge reserve, K2Is used for representing the second knowledge reserve level, k is used for representing the first knowledge reserve parameter, and k is greater than or equal to 0 and less than or equal to 1.
6. The method according to any one of claims 1-5, wherein the determining information to be presented from a plurality of sets of knowledge reserve information in the target application based on the target knowledge reserve level comprises:
determining a second knowledge reserve parameter, and determining a knowledge reserve level range based on the target knowledge reserve level and the second knowledge reserve parameter;
determining at least one knowledge reserve level in the knowledge reserve level range according to the knowledge reserve levels corresponding to the multiple groups of knowledge reserve information in the target application;
and determining at least one group of knowledge reserve information corresponding to the at least one knowledge reserve level as information to be displayed.
7. The method according to any one of claims 1-6, wherein the target user attribute information includes at least one of a medical years, a doctor's job title, a history, a specialty, and a level of knowledge reserve of the target user;
the target knowledge ingestion information comprises at least one of the use frequency of the target user for the target application, knowledge reserve level change information corresponding to the ingestion information, and feedback information.
8. An information recommendation apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring target user attribute information and target knowledge ingestion information of a target user, and the target knowledge ingestion information is information ingested by the target user from a target application in a specified period;
the determining module is used for determining a target knowledge reserve level of the target user based on the target user attribute information and the target knowledge intake information, and determining information to be displayed from multiple groups of knowledge reserve information of the target application based on the target knowledge reserve level, wherein one group of knowledge reserve information in the multiple groups of knowledge reserve information corresponds to one knowledge reserve level;
and the information display module is used for outputting the information to be displayed to a user interface of the target application so as to display the information to be displayed to the target user.
9. A computer device, comprising: a processor, a memory, and a network interface;
the processor is connected with a memory and a network interface, wherein the network interface is used for providing data communication functions, the memory is used for storing program codes, and the processor is used for calling the program codes and executing the method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method of any of claims 1-7.
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| PCT/CN2020/131813 WO2021159804A1 (en) | 2020-09-09 | 2020-11-26 | Information recommendation method and apparatus, and computer device and storage medium |
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