CN111639256B - Discipline-based professional recommendation method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a subject-based professional recommendation method, a subject-based professional recommendation device, computer equipment and a storage medium. The method comprises the following steps: receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction; extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data; inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result; and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display. By adopting the method, the accuracy of the college entrance examination volunteer professional recommendation can be improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a subject-based professional recommendation method, apparatus, computer device, and storage medium.
Background
Along with the continuous development of college entrance examination, a new elective course college entrance examination mode of '3+ [ 6 ] 3' appears, wherein 3 in the 3+ [ 6 ] 3] elective course college entrance examination mode represents Chinese, mathematics and foreign language subjects. Selection of 3 from 6 refers to selecting 3 subject references from physical, chemical, biological, geographic, historical, and political subjects. Under the elective course college entrance examination mode of new '3+ [ 6 ] 3', the college entrance examination professional volunteer fills up a current difficulty. At present, the college entrance examination volunteer professional recommendation is usually recommended according to college entrance examination achievements or employment prospects of examinees.
However, in the current college entrance examination volunteer professional recommendation mode, the problem that the recommendation profession is not matched with the examinee exists in the recommendation according to the college entrance examination score or employment prospect of the examinee, and the accuracy of the recommendation profession is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a subject-based professional recommendation method, apparatus, computer device, and storage medium that can improve the accuracy of the professional recommendation of college entrance examination volunteers.
A subject-based professional recommendation method, the method comprising:
Receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction;
extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data;
Inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result;
and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, after extracting the corresponding subject knowledge features, skill features, thinking ability features, and personality trait features from the user answer data, the method further includes:
Computing capability evaluation information according to the subject knowledge features, the skill features, the thinking capability features and the personality trait features;
And determining a corresponding target recommendation specialty according to the capability evaluation information, and sending the target recommendation specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, determining a corresponding capability recommendation specialty according to the capability evaluation information, and sending the capability recommendation specialty to a user terminal corresponding to the user identifier for display, where the method includes:
acquiring each piece of professional characteristic data, and calculating the correlation degree between each piece of professional characteristic data and the capability evaluation information;
And determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminal corresponding to the user identifier for display.
In one embodiment, the training step of the volunteer professional recommendation model comprises:
obtaining answer data of a historical user and corresponding historical recommendation professions;
extracting corresponding historical subject knowledge features, historical skill features, historical thinking ability features and historical personality trait features from the historical user answer data;
And taking the historical discipline knowledge features, the historical skill features, the historical thinking ability features and the historical personality trait features as inputs of the neural network model, training the historical recommendation professions as labels of the neural network model, and obtaining the volunteer professional recommendation model when training is completed.
In one embodiment, obtaining user answer data corresponding to a user identifier according to a professional recommendation instruction includes:
Obtaining elective course subjects corresponding to the user identifications according to the professional recommendation instruction, obtaining the target number of test questions from a preset question library according to the elective course subjects, and sending the test questions to the user terminals corresponding to the user identifications so that the user terminals display the test questions;
And receiving user answer data corresponding to the test questions returned by the user terminal.
A discipline-based professional recommendation device, the device comprising:
The data acquisition module is used for receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction;
The feature extraction module is used for extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the answer data of the user;
The feature recognition module is used for inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result;
and the recommending module is used for obtaining the corresponding recommending profession according to the output identifying result, and sending the recommending profession to the user terminal corresponding to the user identifier for displaying.
In one embodiment, the apparatus further comprises:
The information calculation module is used for calculating capability evaluation information according to subject knowledge features, skill features, thinking capability features and personality trait features;
And the target recommendation module is used for determining a corresponding target recommendation specialty according to the capability evaluation information, and sending the target recommendation specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, the goal recommendation module includes:
the correlation calculation unit is used for acquiring each piece of professional characteristic data and calculating the correlation between each piece of professional characteristic data and the capability evaluation information;
the professional display unit is used for determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminals corresponding to the user identifiers for display.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction;
extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data;
Inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result;
and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction;
extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data;
Inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result;
and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display.
According to the subject-based professional recommendation method, device, computer equipment and storage medium, the professional recommendation instruction is received, the professional recommendation instruction carries the user identification, and the user answer data corresponding to the user identification is obtained according to the professional recommendation instruction; extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data; inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result; and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display. Through extracting subject knowledge characteristics, skill characteristics, thinking ability characteristics and personality characteristics of the user, corresponding recommendation professions are determined according to the subject knowledge characteristics, the skill characteristics, the thinking ability characteristics and the personality characteristics, and accuracy of college entrance application professional recommendation is improved.
Drawings
FIG. 1 is an application environment diagram of a subject-based professional recommendation method in one embodiment;
FIG. 2 is a flow diagram of a subject-based professional recommendation method in one embodiment;
FIG. 3 is a flow diagram of determining target recommendation professions in one embodiment;
FIG. 4 is a flow chart of calculating correlation in one embodiment;
FIG. 5 is a flow chart of training a volunteer specialty recommendation model in one embodiment;
Fig. 6 is a schematic flow chart of obtaining answer data of a user in one embodiment;
FIG. 7 is a block diagram of a proprietary recommendation device in one embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The subject-based professional recommendation method provided by the application can be applied to an application environment shown in fig. 1. Wherein the user terminal 102 communicates with the server 104 via a network. The server 104 receives a professional recommendation instruction sent by the user terminal 102, wherein the professional recommendation instruction carries a user identifier, and the server 104 acquires user answer data corresponding to the user identifier according to the professional recommendation instruction; the server 104 extracts corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data; the server 104 inputs the subject knowledge features, skill features, thinking ability features and personality trait features into the volunteer professional recommendation model to obtain an output recognition result; the server 104 obtains the corresponding recommended profession according to the output identification result, and sends the recommended profession to the user terminal 102 corresponding to the user identifier for display. The user terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a subject-based professional recommendation method is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes the following steps:
S202, receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction.
The user identification is used for uniquely identifying the user, which can be a college entrance examination taker, for example, the user identification can be a college entrance examination number, an identity card number, and the like. The user answer data refers to the answer of the user to the preset test questions of the college entrance examination staff. For example, the test questions can be displayed on the user terminal in the form of selection questions through a web page, the test questions are displayed through an applet, an APP (mobile phone software) and the like, the user replies to the test questions, and the server obtains answers selected by the user. For example, 100 test questions are set, each corresponding to a user selected answer.
Specifically, when the user needs to make a professional recommendation, a professional recommendation instruction can be sent to the server through the user terminal, the server receives the professional recommendation instruction, and the professional recommendation instruction is analyzed to obtain a user identification. And searching user answer data corresponding to the user identification in a preset user answer database.
S204, extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data.
The subject knowledge features are features extracted according to the knowledge content answer data corresponding to the subject selected by the user. For example, data of a user replying to the physical test questions is obtained, and physical discipline knowledge features are extracted according to the replied physical test data. The skill characteristics refer to characteristics extracted from data of a user's answer skill test questions, for example, answer data of a music skill test question is acquired, and the music skill characteristics are extracted. The thinking ability feature refers to a feature extracted from data of a test question related to the thinking ability of a user. For example, the answer data of the logical thinking test questions is acquired, and the logical thinking features are extracted. The personality trait refers to a feature extracted from data of a user replying to a personality-related test question. For example, the characteristics extracted from the data of the personality trait related test are obtained.
Specifically, the server extracts corresponding discipline knowledge features, skill features, thinking ability features, and personality trait features from the various user answer data.
S206, inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result.
The volunteer specialty recommendation model is a model obtained by training historical user answer data and corresponding historical recommendation specialty by using an artificial intelligence algorithm. The artificial intelligence algorithm may be a linear regression algorithm, a random forest algorithm, a neural network algorithm, or the like. The recognition result refers to the recommendation probability results of various professions output by the volunteer professional recommendation model.
Specifically, the server inputs subject knowledge features, skill features, thinking ability features and personality trait features into the trained volunteer specialty recommendation model for calculation, and obtains the recognition result output by the volunteer specialty recommendation model, namely, obtains the recommendation probability result of each specialty.
And S208, obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display.
Specifically, the server takes the profession with the recommendation probability larger than the preset probability threshold as the recommendation specialty according to the output identification result, namely the recommendation probability result of each specialty, and then sends the recommendation specialty to the user terminal corresponding to the user identifier for display.
In the subject-based professional recommendation method, receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction; extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data; inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result; and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display. Through extracting subject knowledge characteristics, skill characteristics, thinking ability characteristics and personality characteristics of the user, corresponding recommendation professions are determined according to the subject knowledge characteristics, the skill characteristics, the thinking ability characteristics and the personality characteristics, and accuracy of college entrance application professional recommendation is improved.
In one embodiment, as shown in fig. 3, after step S204, that is, after extracting the corresponding subject knowledge features, skill features, thinking ability features, and personality trait features from the user answer data, the method further includes the steps of:
S302, computing capability evaluation information according to subject knowledge features, skill features, thinking capability features and personality trait features.
The ability evaluation information is evaluation information of user ability, and the ability includes general learning ability, mathematical logic ability, space judgment ability, data processing ability, movement coordination ability, interpersonal interaction ability and the like. Different users have different capabilities themselves, so that different professions need to be recommended. For example, users with strong mathematical logic capabilities may recommend mathematical professions, users with strong athletic coordination capabilities may recommend sports professions, users with high data processing capabilities may recommend accounting professions, management professions, and so forth.
Specifically, the server calculates the capability assessment information from the discipline knowledge features, skill features, thinking capability features, and personality trait features, for example, the capability assessment information may be calculated using the following formulaWherein S refers to the capability evaluation information obtained by calculation. e refers to discipline knowledge features, s refers to skill features, f refers to thinking ability features, and p refers to personality trait features. Through the formula, the evaluation information corresponding to each capability can be obtained through calculation.
S304, determining a corresponding target recommendation specialty according to the capability evaluation information, and sending the target recommendation specialty to a user terminal corresponding to the user identifier for display.
The target recommendation profession is a recommendation profession determined according to the capability evaluation information, and a plurality of recommendation professions can be provided. For example, the user generally has a strong learning ability, and can determine that the corresponding recommended profession is a literature professional, such as a chinese language literature professional, a philosophy professional, a news science professional, and the like.
Specifically, the server determines a corresponding target recommendation specialty according to the capability evaluation information, and sends the target recommendation specialty to a user terminal corresponding to the user for display. In one embodiment, the server may comprehensively determine the corresponding comprehensive recommended profession according to the capability evaluation information and the recognition result output by the volunteer professional recommendation model, that is, match the target recommended profession with the recommended profession, use the same professional as the comprehensive recommended profession, and send the comprehensive recommended profession to the user terminal corresponding to the user identifier for display.
In the embodiment, the capability evaluation information is obtained through calculation, and the corresponding target recommendation profession is determined according to the capability evaluation information, so that the accuracy of obtaining the target recommendation profession is improved.
In one embodiment, as shown in fig. 4, step S304, that is, determining a corresponding capability recommendation specialty according to the capability evaluation information, sends the capability recommendation specialty to a user terminal corresponding to the user identifier for display, includes:
S402, acquiring each piece of professional characteristic data, and calculating the correlation degree between each piece of professional characteristic data and the capability evaluation information.
The professional characteristic data refers to capability characteristic data required by learning profession.
Specifically, the server may acquire each piece of professional characteristic data, and calculate the correlation between each piece of professional characteristic data and the capability evaluation information using a similarity algorithm. The similarity algorithm may be a euclidean distance algorithm, a pearson correlation coefficient, a cosine similarity algorithm, or the like.
S404, determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminals corresponding to the user identifiers for display.
Specifically, the server ranks the similarities to obtain ranked professions, and a preset number of target recommended professions can be selected from the ranked professions in sequence according to the similarities from large to small, wherein the preset number is the preset recommended number of professions, for example, the first three professions of the similarity ranking result can be selected. And sequentially returning the preset number of recommended professions to the user terminal corresponding to the user identifier, and displaying the preset number of recommended professions sent by the server by the user terminal.
In the embodiment, the accuracy of the recommended professions obtained by the user is further improved by determining the preset number of the target recommended professions according to the similarity and sending the target recommended professions to the user terminal corresponding to the user identifier for display.
In one embodiment, as shown in fig. 5, the training step of the volunteer specialty recommendation model includes:
s502, obtaining answer data of a historical user and corresponding historical recommendation professions.
The historical user answer data refers to answer data when the mechanical energy of the historical user is specially recommended. The history recommended profession refers to profession recommended according to history answer data.
Specifically, the server acquires answer data of the historical user and corresponding historical recommendation professions from the historical database.
S504, extracting corresponding historical subject knowledge features, historical skill features, historical thinking ability features and historical personality trait features from the historical user answer data.
S506, taking the history subject knowledge features, the history skill features, the history thinking ability features and the history personality trait features as inputs of the neural network model, taking the history recommendation profession as a label of the neural network model for training, and obtaining the volunteer professional recommendation model when training is completed.
The neural network model is a model trained by using a neural network algorithm. The neural network algorithm includes an input layer, a hidden layer, and an output layer, uses an S-type function as an activation function, and uses a cross entropy function as a loss function.
Specifically, the server extracts corresponding historical subject knowledge features, historical skill features, historical thinking ability features and historical personality characteristics from the historical user answer data, takes the historical subject knowledge features, the historical skill features, the historical thinking ability features and the historical personality characteristics as inputs of a neural network model, trains the historical recommendation profession as a label of the neural network model, and when training is completed, namely when the training times reach the maximum iteration times or the value of a loss function is smaller than a preset threshold value, the training is completed, and the model which is completed last time is taken as a volunteer professional recommendation model. The server deploys the trained volunteer recommendation model, so that the subsequent use is convenient.
In one embodiment, as shown in fig. 6, step S202, that is, obtaining answer data corresponding to a user identifier according to a professional recommendation instruction, includes the steps of:
S602, acquiring elective course subjects corresponding to the user identifications according to the professional recommendation instruction, acquiring the target number of test questions from a preset question bank according to elective course subjects, and sending the test questions to the user terminals corresponding to the user identifications so that the user terminals display the test questions.
S604, receiving user answer data corresponding to the test questions returned by the user terminal.
Wherein elective course subjects refer to subjects selected from physical, chemical, biological, geographic, historical, and political subjects to which reference is made. For example, the user may select a physical, chemical, biological three-family reference elevation. Test questions refer to test questions related to elective course subjects of the user, such as subject knowledge points of course selection subjects, skills of course selection subjects, and the like. The preset question library refers to various preset test questions, such as test questions for testing subject knowledge points of a user, test questions for testing skill trends of the user, test questions for testing the thinking ability of the user and test questions for testing personality traits.
Specifically, the server obtains elective course subjects corresponding to the user identification according to the professional recommendation instruction, obtains the target number of test questions from the preset question library according to the elective course subjects, sends the test questions to the user terminal corresponding to the user identification, so that the user terminal displays the test questions, the user replies the displayed test questions in the terminal, and when the reply is completed, the user reply data are sent to the server. And the server receives user answer data corresponding to the test questions returned by the user terminal.
In the embodiment, the user is tested by acquiring the test questions corresponding to elective course subjects, so that the answer data of the user are obtained, the obtained answer data of the user can be more accurate, and the obtained recommended profession is more accurate.
It should be understood that, although the steps in the flowcharts of fig. 2-5 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or steps.
In one embodiment, as shown in fig. 7, there is provided a subject-based professional recommendation apparatus 700, comprising: a data acquisition module 702, a feature extraction module 704, a feature identification module 706, and a recommendation module 708, wherein:
The data acquisition module 702 is configured to receive a professional recommendation instruction, where the professional recommendation instruction carries a user identifier, and acquire user answer data corresponding to the user identifier according to the professional recommendation instruction;
the feature extraction module 704 is configured to extract corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the answer data of the user;
The feature recognition module 706 is configured to input the subject knowledge feature, the skill feature, the thinking ability feature, and the personality trait feature into the volunteer professional recommendation model, to obtain an output recognition result;
And the recommending module 708 is used for obtaining a corresponding recommending specialty according to the output identification result, and sending the recommending specialty to a user terminal corresponding to the user identifier for displaying.
In one embodiment, the professional recommendation device 700 includes:
The information calculation module is used for calculating capability evaluation information according to subject knowledge features, skill features, thinking capability features and personality trait features;
And the target recommendation module is used for determining a corresponding target recommendation specialty according to the capability evaluation information, and sending the target recommendation specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, the goal recommendation module includes:
the correlation calculation unit is used for acquiring each piece of professional characteristic data and calculating the correlation between each piece of professional characteristic data and the capability evaluation information;
the professional display unit is used for determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminals corresponding to the user identifiers for display.
In one embodiment, the professional recommendation device 700 includes:
The history acquisition module is used for acquiring answer data of the history user and corresponding history recommendation professions;
The history extraction module is used for extracting corresponding history subject knowledge features, history skill features, history thinking ability features and history personality trait features from the history user answer data;
The training module is used for taking the history subject knowledge features, the history skill features, the history thinking ability features and the history personality trait features as the input of the neural network model, taking the history recommendation profession as the label of the neural network model for training, and obtaining the volunteer professional recommendation model when the training is completed.
In one embodiment, a data acquisition module includes:
the test unit is used for acquiring elective course subjects corresponding to the user identification according to the professional recommendation instruction, acquiring the target number of test questions from a preset question bank according to the elective course subjects, and sending the test questions to a user terminal corresponding to the user identification so as to enable the user terminal to display the test questions;
And the data receiving unit is used for receiving the user answer data corresponding to the test questions returned by the user terminal.
For specific limitations of the professional recommendation device, reference may be made to the above limitation of the subject-based professional recommendation method, and no further description is given here. The respective modules in the above-described professional recommendation apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing answer data of users and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a discipline-based professional recommendation method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 8 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction; extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data; inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result; and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, the processor when executing the computer program further performs the steps of: computing capability evaluation information according to the subject knowledge features, the skill features, the thinking capability features and the personality trait features; and determining a corresponding target recommendation specialty according to the capability evaluation information, and sending the target recommendation specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring each piece of professional characteristic data, and calculating the correlation degree between each piece of professional characteristic data and the capability evaluation information; and determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminal corresponding to the user identifier for display.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining answer data of a historical user and corresponding historical recommendation professions; extracting corresponding historical subject knowledge features, historical skill features, historical thinking ability features and historical personality trait features from the historical user answer data; and taking the historical discipline knowledge features, the historical skill features, the historical thinking ability features and the historical personality trait features as inputs of the neural network model, training the historical recommendation professions as labels of the neural network model, and obtaining the volunteer professional recommendation model when training is completed.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining elective course subjects corresponding to the user identifications according to the professional recommendation instruction, obtaining the target number of test questions from a preset question library according to the elective course subjects, and sending the test questions to the user terminals corresponding to the user identifications so that the user terminals display the test questions; and receiving user answer data corresponding to the test questions returned by the user terminal.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction; extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data; inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into the volunteer professional recommendation model to obtain an output recognition result; and obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, the computer program when executed by the processor further performs the steps of: computing capability evaluation information according to the subject knowledge features, the skill features, the thinking capability features and the personality trait features; and determining a corresponding target recommendation specialty according to the capability evaluation information, and sending the target recommendation specialty to a user terminal corresponding to the user identifier for display.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring each piece of professional characteristic data, and calculating the correlation degree between each piece of professional characteristic data and the capability evaluation information; and determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminal corresponding to the user identifier for display.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining answer data of a historical user and corresponding historical recommendation professions; extracting corresponding historical subject knowledge features, historical skill features, historical thinking ability features and historical personality trait features from the historical user answer data; and taking the historical discipline knowledge features, the historical skill features, the historical thinking ability features and the historical personality trait features as inputs of the neural network model, training the historical recommendation professions as labels of the neural network model, and obtaining the volunteer professional recommendation model when training is completed.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining elective course subjects corresponding to the user identifications according to the professional recommendation instruction, obtaining the target number of test questions from a preset question library according to the elective course subjects, and sending the test questions to the user terminals corresponding to the user identifications so that the user terminals display the test questions; and receiving user answer data corresponding to the test questions returned by the user terminal.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A subject-based professional recommendation method, the method comprising:
receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction;
Extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data;
inputting the subject knowledge features, the skill features, the thinking ability features and the personality trait features into a volunteer professional recommendation model to obtain an output recognition result;
obtaining a corresponding recommended specialty according to the output identification result, and sending the recommended specialty to a user terminal corresponding to the user identifier for display; and
Computing capability assessment information based on the discipline knowledge features, the skill features, the thinking capability features, and the personality trait features;
Determining corresponding target recommendation professions according to the capability evaluation information, matching the target recommendation professions with the recommendation professions, taking the professions with the same matching as comprehensive recommendation professions, and sending the comprehensive recommendation professions to user terminals corresponding to the user identifiers for display.
2. The method of claim 1, wherein the determining the corresponding capability recommendation specialty according to the capability assessment information, and transmitting the capability recommendation specialty to the user terminal corresponding to the user identifier for display, comprises:
acquiring each piece of professional characteristic data, and calculating the correlation degree between each piece of professional characteristic data and the capability evaluation information;
And determining a preset number of target recommendation professions according to the correlation degree, and sending the target recommendation professions to the user terminal corresponding to the user identifier for display.
3. The method of claim 1, wherein the step of training the volunteer specialized recommendation model comprises:
obtaining answer data of a historical user and corresponding historical recommendation professions;
Extracting corresponding historical subject knowledge features, historical skill features, historical thinking ability features and historical personality trait features from the historical user answer data;
And taking the historical discipline knowledge features, the historical skill features, the historical thinking ability features and the historical personality trait features as inputs of a neural network model, training the historical recommendation profession as a label of the neural network model, and obtaining the volunteer professional recommendation model when training is completed.
4. The method according to claim 1, wherein the obtaining, according to the professional recommendation instruction, the answer data of the user corresponding to the user identifier includes:
Acquiring elective course subjects corresponding to the user identification according to the professional recommendation instruction, acquiring a target number of test questions from a preset question bank according to the elective course subjects, and transmitting the test questions to a user terminal corresponding to the user identification so as to enable the user terminal to display the test questions;
And receiving user answer data corresponding to the test questions returned by the user terminal.
5. A subject-based professional recommendation device, the device comprising:
The data acquisition module is used for receiving a professional recommendation instruction, wherein the professional recommendation instruction carries a user identifier, and acquiring user answer data corresponding to the user identifier according to the professional recommendation instruction;
The feature extraction module is used for extracting corresponding subject knowledge features, skill features, thinking ability features and personality trait features from the user answer data;
The feature recognition module is used for inputting the subject knowledge feature, the skill feature, the thinking ability feature and the personality trait feature into a volunteer professional recommendation model to obtain an output recognition result;
The recommending module is used for obtaining a corresponding recommending specialty according to the output identification result, and sending the recommending specialty to a user terminal corresponding to the user identifier for displaying; and
The information calculation module is used for calculating capability evaluation information according to subject knowledge features, skill features, thinking capability features and personality trait features;
The target recommendation module is used for determining corresponding target recommendation professions according to the capability evaluation information, matching the target recommendation professions with the recommendation professions, taking the professions with the same matching as comprehensive recommendation professions, and sending the comprehensive recommendation professions to the user terminals corresponding to the user identifiers for display.
6. The apparatus of claim 5, wherein the target recommendation module comprises:
The correlation calculation unit is used for acquiring each piece of professional characteristic data and calculating the correlation between each piece of professional characteristic data and the capability evaluation information;
And the specialty display unit is used for determining a preset number of target recommendation specialty according to the correlation degree, and sending the target recommendation specialty to the user terminal corresponding to the user identifier for display.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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