Course push method, device, computer equipment, storage medium and program product

Abstract

The present application relates to a course recommendation method, device, computer equipment, storage medium and computer program product. The method includes: determining the user of the course to be recommended, and obtaining the position information of the user; determining the ability level of each position ability corresponding to the user according to the position information; selecting the target course corresponding to each ability level; obtaining the historical preference information corresponding to the user, and sorting the target courses according to the historical preference information; pushing the sorted target courses to the user. This method first selects the corresponding target courses according to the ability level of each position of the user, then sorts the target courses according to the historical preference information of the user, and finally pushes the sorted target courses to the user. In this way, the courses are first selected according to the ability level of the user, and then sorted according to the user's preference information, so that the pushed courses are more accurate.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a course push method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] Employee job skills training is a necessary means to enhance employee work ability and improve the company's work efficiency. Arranging reasonable and effective training courses for each employee can not only meet the employee's training needs and quickly improve employee work skills, but also minimize the loss of working time caused by training.

[0003] Currently, there are generally three forms of employee training arrangements: mandatory requirements for employees at specific job levels to participate, line managers issuing training tasks, and employees voluntarily participating through self-evaluation. There is a lack of scientific and targeted training course recommendations, which results in the training courses that employees participate in not meeting their actual needs and poor training results. Summary of the invention

[0004] Based on this, it is necessary to provide a course push method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of course recommendations in response to the above technical problems.

[0005] In a first aspect, the present application provides a course recommendation method, the method comprising:

[0006] Determine the user to whom the course is to be recommended and obtain the user's job information;

[0007] Determine the ability level of each position ability corresponding to the user according to the position information;

[0008] Select target courses corresponding to each of the competency levels;

[0009] Acquire historical preference information corresponding to the user, and sort the target courses according to the historical preference information;

[0010] The sorted target courses are pushed to the user.

[0011] In one embodiment, after pushing the sorted target courses to the user, the following steps are performed:

[0012] Obtaining the user's preference evaluation information after the target course is completed;

[0013] The obtaining of the historical preference information corresponding to the user includes:

[0014] Obtaining preference evaluation information of each completed course corresponding to the user;

[0015] The historical preference information is calculated based on each of the preference evaluation information.

[0016] In one embodiment, obtaining the user's preference evaluation information after the target course is completed includes:

[0017] After the target course is finished, a corresponding preference questionnaire is pushed to the user, wherein the preference questionnaire includes at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index;

[0018] A completed preference questionnaire is obtained, and the preference questionnaire is parsed to obtain preference evaluation information.

[0019] In one embodiment, the sorting of the target courses according to the historical preference information includes:

[0020] Determining whether each of the historical preference information has a target weight, where the target weight is adjusted by the user;

[0021] When the historical preference information has a target weight, a first recommendation index of the target course is calculated according to the historical preference information and the target weight, and the target courses are sorted according to the first recommendation index;

[0022] When the historical preference information does not have a target weight, a default weight is obtained, a second recommendation index of the target course is calculated according to the historical preference information and the default weight, and the target courses are sorted according to the second recommendation index.

[0023] In one embodiment, pushing the sorted target courses to the user includes:

[0024] Obtain the sorted target courses corresponding to each of the ability levels;

[0025] The sorted target courses are pushed to the user according to the ability level.

[0026] In one embodiment, determining the ability level of each position ability corresponding to the user according to the position information includes:

[0027] Determine a job capability calculation model corresponding to the job information, wherein the job capability calculation model includes job capability dimensions and grade calculation rules corresponding to each of the job capability dimensions;

[0028] Acquire the work data corresponding to the user according to the level calculation rule;

[0029] The ability level of each position ability is obtained according to the work data and the corresponding level calculation rules.

[0030] In a second aspect, the present application further provides a course recommendation device, the device comprising:

[0031] A job information determination module is used to determine the user of the course to be recommended and obtain the job information of the user;

[0032] A capability level determination module, used to determine the capability level of each position capability corresponding to the user according to the position information;

[0033] A target course selection module, used for selecting target courses corresponding to each of the competency levels;

[0034] A sorting module, used to obtain the historical preference information corresponding to the user, and sort the target courses according to the historical preference information;

[0035] The push module is used to push the sorted target courses to the user.

[0036] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in any one of the above-mentioned embodiments when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method in any one of the above-mentioned embodiments.

[0038] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the method in any one of the above-mentioned embodiments when executed by a processor.

[0039] The above-mentioned course push method, device, computer equipment, storage medium and computer program product first select the corresponding target courses according to the ability level of each position of the user, then sort the target courses according to the user's historical preference information, and finally push the sorted target courses to the user. In this way, the courses are first selected according to the user's ability level and then sorted according to the user's preference information, so that the pushed courses are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A diagram showing an application environment of a course push method in an embodiment;

[0041] Figure 2 A schematic diagram of a process flow of a course push method in one embodiment;

[0042] Figure 3 A flowchart of a course recommendation feedback step in one embodiment;

[0043] Figure 4 A flowchart of the steps of sorting target courses in one embodiment;

[0044] Figure 5 A flowchart of the capability level calculation steps in one embodiment;

[0045] Figure 6 It is a structural diagram of a job competency model module in each embodiment;

[0046] Figure 7 It is a structural diagram of the storage structure of the job course library module in one embodiment;

[0047] Figure 8 It is a structural block diagram of a course pushing device in one embodiment;

[0048] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The course recommendation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0051] The terminal 102 may send a course push request to the server 104, or the server 104 may obtain the user of the course to be pushed according to a scheduled task, and obtain the user's position information, so as to determine the ability level of each position ability corresponding to the user according to the position information; select the target course corresponding to each ability level; obtain the user's corresponding historical preference information, and sort the target courses according to the historical preference information; and push the sorted target courses to the user, for example, to the user's terminal 102.

[0052] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0053] In one embodiment, Figure 2 As shown, a course recommendation method is provided, which is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0054] S202: Determine the user to whom the course is to be recommended, and obtain the user's job position information.

[0055] Specifically, the user to be recommended a course can be determined by the employee name and / or employee number sent by the terminal. In other embodiments, the employee name and / or employee number can also be directly input to the server. Position information refers to the user's position in the company, such as R&D position, testing position, administrative position or management position, etc.

[0056] The server obtains the user's job information by querying the employee's name and / or work number. For example, the server performs a fuzzy query or a precise query based on the employee's name and / or work number, determines the best employee, and then reads the job information of the best employee. Preferably, when querying by name, a fuzzy query is used to determine the corresponding employee. If there are multiple employees with the same name, the user can select based on the basic information of the employee with the same name, or score the employees with the same name based on the information of the queryer to obtain the employees whose scores meet the requirements. For example, if the queryer and one of the employees with the same name belong to the same department, it is preferred to determine that the employee is the user of the course to be recommended, and obtain the job information of the employee.

[0057] In one of the embodiments, in response to the user's selection, the server determines whether the user can learn new courses based on the number of courses the user is currently studying and has not studied, so as to avoid the situation where the user selects too many courses but does not study any of them.

[0058] S204: Determine the ability level of each position ability corresponding to the user according to the position information.

[0059] Among them, job competence is the competence required for each pre-set job, and a job should have at least one dimension of job competence, and each job competence includes at least one competence level. It should be noted that the job competences required for different jobs can be different, the same, or partially the same, and this is not limited here. For example, a software testing job requires five dimensions of competence, including test theory level, test design ability, test development ability, test execution ability, and non-functional testing ability. Each job competence can be divided into five levels. Each level can also be divided into different grade levels. For example, the total score of each job competence dimension is 20 points, and every 4 points is divided into one level, so there are 5 levels, and each level has 4 grade levels.

[0060] The server can determine the corresponding job capability based on the job information, then obtain the user's work data, and calculate the capability level corresponding to each job capability based on the work data.

[0061] S206: Select target courses corresponding to each ability level.

[0062] Specifically, the server pre-sets target courses for the competency levels corresponding to various job competencies. For example, each job competency corresponds to multiple competency levels, and each competency level corresponds to multiple target courses. Therefore, after calculating the competency level, the server selects the corresponding target course according to the job competency and the corresponding competency level to achieve the first screening of the user's recommended courses.

[0063] S208: Obtain historical preference information corresponding to the user, and sort the target courses according to the historical preference information.

[0064] Specifically, the historical preference information is generated based on the user's historical preferences for course selection or historical course evaluation information, wherein the historical preference information can be determined based on employee training preferences, including at least one of the instructor category preference dimension, the teaching format preference dimension, the class length preference dimension, and the instructor level preference dimension.

[0065] The server sorts the target courses corresponding to each ability level according to the historical preference information, for example, calculates the score of each target course according to the dimensions in the historical preference information and the weights corresponding to the dimensions, and then sorts the target courses according to the calculated scores.

[0066] S210: Push the sorted target courses to the user.

[0067] Specifically, after sorting the target courses, the server pushes the target courses to the user.

[0068] In a preferred embodiment, the server can push the sorted target courses in the form of a legend, for example, the legend shows the ability level corresponding to each position ability, and then gives the sorted target courses at the ability level. Or when the user clicks on the ability level, it jumps to the corresponding page, which displays the sorted target courses for the user to choose. After selecting a course corresponding to a position ability, you can continue to select other courses corresponding to position abilities until all courses corresponding to each position ability are selected, for example, the number of courses selected for each position ability meets the requirements.

[0069] The above-mentioned course push method first selects the corresponding target courses according to the ability level of each position of the user, then sorts the target courses according to the user's historical preference information, and finally pushes the sorted target courses to the user. In this way, the courses are first selected according to the user's ability level and then sorted according to the user's preference information, so that the pushed courses are more accurate.

[0070] In one of the embodiments, after the sorted target courses are pushed to the user, it includes: obtaining the user's preference evaluation information after the target course is completed; obtaining the user's corresponding historical preference information, including: obtaining the preference evaluation information of each completed course corresponding to the user; and calculating the historical preference information based on each preference evaluation information.

[0071] In one of the embodiments, obtaining the user's preference evaluation information after the target course ends includes: after the target course ends, pushing a corresponding preference questionnaire to the user, the preference questionnaire including at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index; obtaining the completed preference questionnaire, and parsing the preference questionnaire to obtain the preference evaluation information.

[0072] The preference evaluation information may be obtained in the form of a preference questionnaire. In other embodiments, the user's preference evaluation information may be calculated by collecting the user's behavior in the course, so that the user does not need to fill out the corresponding preference questionnaire, thereby improving efficiency. The preference evaluation information calculated by collecting the user's behavior may be calculated based on the user's class duration and micro-expressions during class.

[0073] In this embodiment, the preference evaluation information is explained by the operation of a preference questionnaire, wherein the preference questionnaire may include at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index, wherein the teaching format index includes face-to-face courses (whose user behavior can be obtained through monitoring equipment) and online courses. The lecturer type index includes internal lecturers and external lecturers. The course duration index can be classified according to the time of the course, for example, the course duration exceeds a preset length, such as 2 hours, and the course duration is less than or equal to the preset length. The lecturer level index is classified according to the level of the lecturer, for example, the lecturer is at level 1-2, the lecturer is at level 3-4, and so on.

[0074] In this embodiment, the server obtains the preference questionnaire filled in by the user, and then reads the score given by the user in the preference questionnaire as the preference evaluation information. Optionally, the server can parse the preference questionnaire, and since the preference questionnaires are the same, the server can determine the evaluation information given by the user based on the position information of each option in the preference questionnaire. For example, for one dimension, the position of each evaluation score is the same, and the evaluation score of each dimension is determined based on the selected position in the preference questionnaire.

[0075] Among them combined Figure 3 As shown, the server also recommends courses and provides feedback. The server recommends target courses to users in order of priority, so that users can refer to the priority order to select corresponding courses. After the employee completes the training, the questionnaire is pushed to the user to obtain the employee's evaluation information on the training course, which is stored in the job course library module and used for calculating the course training preference in the next recommendation.

[0076] Specifically, the server obtains the recommended courses and priorities for employees in each competency dimension from the course screening and scoring module; recommends courses in each competency dimension to employees in order of priority; employees then choose to sign up for corresponding courses and participate in training; pushes training questionnaires to employees within 1 working day after the end of the training period; stores the questionnaire information provided by employees in the job course library module for use in the next recommendation and calculation of course training preferences.

[0077] In one of the embodiments, the target courses are sorted according to the historical preference information, including: determining whether each historical preference information has a target weight, the target weight being adjusted by the user; when the historical preference information has a target weight, calculating a first recommendation index of the target course according to the historical preference information and the target weight, and sorting the target courses according to the first recommendation index; when the historical preference information does not have a target weight, obtaining a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sorting the target courses according to the second recommendation index.

[0078] Combination Figure 4 As shown, Figure 4 This is a flow chart of the target course sorting step in an embodiment. In this embodiment, the server first obtains the competency level of the employee's job competency; for each job competency that the employee needs to master, the server selects the target course that meets the employee's current competency level from the job course library.

[0079] For target courses that meet the same competency level requirements, the employee training preference score for each target course is calculated. The calculation rule for the employee training preference score s is: s = a*r1+b*r2+c*r3+d*r4, where a is the lecturer category score, b is the teaching method score, c is the class length score, d is the lecturer level score, r1, r2, r3, and r4 are the weights of each score, that is, the default weights. Preferably, the initial setting is r1 = 0.3, r2 = 0.3, r3 = 0.2, and r4 = 0.2. The server also provides a separate interface to allow employees or their line managers to adjust the weight parameters. Users can adjust according to actual conditions to obtain the target weight, so that when calculating the first recommendation index corresponding to each target course, the target weight can be obtained for calculation, and the target courses are sorted according to the first recommendation index. When there is no target weight, the second recommendation index corresponding to each target course is calculated according to the default weight, so that the target courses are sorted according to the second recommendation index.

[0080] The calculation method of the lecturer type score a is: calculate the average score a1 of internal lecturers and the average score a2 of external lecturers in the employee's historical course evaluation. If the current course is taught by an internal lecturer, then a=a1, otherwise a=a2.

[0081] The calculation method of teaching format score b is as follows: calculate the average score b1 of face-to-face courses and the average score b2 of online courses in the employee's historical course evaluation. If the current course is a face-to-face course, then b=b1, otherwise b=b2.

[0082] The calculation method of the course length score c is: calculate the average score c1 of the courses with a duration of less than 2 hours in the employee's historical course evaluation, and the average score c2 of the courses with a duration of more than 2 hours. If the current course duration is less than 2 hours, then c=c1, otherwise c=c2.

[0083] The calculation method of the lecturer level score d is: calculate the average score d1 of the lecturer level below a certain level and the average score d2 of the lecturer level above a certain level in the employee's historical course evaluation. If the lecturer level of the current course is below a certain level, then d = d1, otherwise d = d2.

[0084] Finally, the server prioritizes courses of the same competency level in descending order of the courses’ employee training preference scores.

[0085] In the above embodiment, the recommendation index of the corresponding target course is calculated based on the historical preference information and the corresponding weight.

[0086] In one of the embodiments, pushing the sorted target courses to the user includes: obtaining the sorted target courses corresponding to each ability level; and pushing the sorted target courses to the user according to the ability level.

[0087] The server may sort the target courses corresponding to the ability level of each position ability, and then push the sorted target courses for each ability level to the user.

[0088] Specifically, the server can push the sorted target courses in the form of a legend, for example, the legend shows the ability level corresponding to each position ability, and then gives the sorted target courses at the ability level. Or when the user clicks on the ability level, it jumps to the corresponding page, which displays the sorted target courses for the user to choose. After selecting a course corresponding to a position ability, you can continue to select courses corresponding to other position abilities until all courses corresponding to each position ability are selected, for example, the number of courses selected for each position ability meets the requirements.

[0089] In one of the embodiments, the ability level of each job ability corresponding to the user is determined based on the job information, including: determining a job ability calculation model corresponding to the job information, the job ability calculation model including job ability dimensions and level calculation rules corresponding to each job ability dimension; obtaining work data corresponding to the user according to the level calculation rules; and obtaining the ability level of each job ability according to the work data and the corresponding level calculation rules.

[0090] Specifically, combined Figure 5 As shown, Figure 5 The present invention is a flowchart of the steps of calculating the competency level in an embodiment. In this embodiment, the position competency model module stores the competency models and calculation rules of different positions, analyzes the specific job responsibilities and requirements of each position, establishes a position competency model including five dimensions and five levels, divides the position competency of each position into five dimensions, and divides the competency of each dimension into five levels. Taking the software testing position as an example, the competency is divided into five dimensions, namely, test theory level, test design capability, test development capability, test execution capability, and non-functional testing capability. Each dimension has a total score of 20 points, and each 4 points is divided into a level, for a total of 5 levels.

[0091] On this basis, the job competency model calculation rules are defined to calculate the specific scores of each dimension of employee competency. The data required in the calculation rules generally include theoretical knowledge assessment scores, workload output, work performance compliance, etc. For example, the test theory level ability score of a software testing position is obtained by calculating the average score of the employee's relevant theoretical test scores in the past quarter and converting it into a 20-point system. The job competency model module structure is as follows: Figure 6 shown.

[0092] The job course library module stores all the training courses and course information that are currently available and planned, including the target positions of the courses, the job competency levels of the courses, course instructor information, teaching methods, employee training history, employee evaluation history, etc. The storage structure of the job course library module is as follows: Figure 7 shown.

[0093] In this way, when the server calculates the ability level, it can include: the user inputs the name / employee number of the employee who needs a recommended course; obtains the corresponding employee's position information; according to the employee's position, obtains the position ability model and calculation rules corresponding to the employee's position from the position ability model module; according to the position ability model calculation rules, obtains the data required for the rule calculation (mainly the employee's daily performance data, such as the scores of theoretical examinations, historical workload, historical performance contribution, etc.), and calculates the scores of each ability dimension of the employee. Specifically, the calculation logic that is common to each position ability generally includes the average score of the theoretical examination, and other position abilities need to be customized for specific positions and pre-stored in the system database; the level of the employee in each ability is obtained based on the employee's scores in each ability dimension.

[0094] In the above embodiment, an employee job competency model is established, and the actual work competency level of the employee is calculated based on the employee's daily work data and matched with the job competency model to find the specific capabilities that need to be improved and the corresponding level. Then, courses that meet the requirements are selected from the course library, and similar courses are scored based on the employee's training preferences, so as to obtain a list of courses that are most suitable for employee participation.

[0095] In order to enable those skilled in the art to fully understand the present application, practical applications are used as examples for illustration, which generally include a job competency model module, a job course library module, an employee job competency calculation module, a course screening and scoring module, and a course recommendation and feedback module.

[0096] The job competency model module stores the competency models and calculation rules of different jobs, analyzes the specific job responsibilities and requirements of each job, and establishes a job competency model with five dimensions and five levels. The job competency of each job is divided into five dimensions, and the competency of each dimension is divided into five levels. Taking the software testing job as an example, the competency is divided into five dimensions: test theory level, test design capability, test development capability, test execution capability, and non-functional testing capability. Each dimension has a total score of 20 points, and each 4 points is divided into a level, for a total of 5 levels.

[0097] On this basis, the calculation rules of the job competency model are defined to calculate the specific scores of each dimension of employee competency. The data required in the calculation rules generally include theoretical knowledge assessment scores, workload output, work performance compliance, etc. For example, the test theory level ability score of a software testing position is obtained by calculating the average score of the employee's relevant theoretical test scores in the past quarter and converting it into a 20-point system.

[0098] The job course library module stores all current and planned training courses and course information, including the target positions of the courses, the job competency levels of the courses, course instructor information, teaching methods, employee historical training participation, employee historical evaluation information, etc.

[0099] The employee job competency calculation module calculates the employee's job competency level according to the following process, which includes the following steps:

[0100] (1) The user enters the name / employee number of the employee for whom a course is to be recommended.

[0101] (2) Obtain the job information of the corresponding employee.

[0102] (3) According to the employee's position, obtain the position competency model and calculation rules corresponding to the employee's position from the position competency model module.

[0103] (4) According to the calculation rules of the job competency model, obtain the data required for the rule calculation (mainly employees' daily performance data, such as scores in theoretical exams, historical workload, historical performance contributions, etc.), and calculate the scores of employees in various competency dimensions.

[0104] (5) Based on the employee’s scores in each competency dimension, the employee’s level in each competency dimension is determined.

[0105] The course screening and scoring module screens employee recommended courses according to the following process, which specifically includes the following steps:

[0106] (1) Obtain employee job competency levels.

[0107] (2) For each skill that an employee needs to master, select courses from the job course library that match the employee’s current skill level.

[0108] (3) For courses that meet the same competency level requirements, calculate the employee training preference score for each course. The calculation rule for the employee training preference score s is: s = a*r1+b*r2+c*r3+d*r4, where a is the instructor category score, b is the teaching method score, c is the class length score, d is the instructor level score, r1, r2, r3, and r4 are the weights of each score, and the initial setting is r1 = 0.3, r2 = 0.3, r3 = 0.2, and r4 = 0.2. The system provides a separate interface to allow employees or their line managers to adjust the weight parameters, and users can adjust them according to actual conditions.

[0109] The calculation method of the lecturer type score a is: calculate the average score a1 of internal lecturers and the average score a2 of external lecturers in the employee's historical course evaluation. If the current course is taught by an internal lecturer, then a=a1, otherwise a=a2.

[0110] The calculation method of teaching format score b is as follows: calculate the average score b1 of face-to-face courses and the average score b2 of online courses in the employee's historical course evaluation. If the current course is a face-to-face course, then b=b1, otherwise b=b2.

[0111] The calculation method of the course length score c is: calculate the average score c1 of the courses with a duration of less than 2 hours in the employee's historical course evaluation, and the average score c2 of the courses with a duration of more than 2 hours. If the current course duration is less than 2 hours, then c=c1, otherwise c=c2.

[0112] The calculation method of the lecturer level score d is: calculate the average score d1 of the lecturer level below a certain level and the average score d2 of the lecturer level above a certain level in the employee's historical course evaluation. If the lecturer level of the current course is below a certain level, then d = d1, otherwise d = d2.

[0113] (4) Prioritize courses of the same competency level in descending order of their employee training preference scores.

[0114] The course recommendation and feedback module is mainly responsible for course recommendation and feedback, recommending courses to users in order of priority, so that users can refer to the priority order to select the corresponding courses. After the employee completes the training, the questionnaire is pushed to the user to obtain the employee's evaluation information on the training course, which is stored in the job course library module and used to calculate the course training preference in the next recommendation. The specific steps may include the following:

[0115] (1) Obtain employees’ recommended courses and priorities for each competency dimension from the course screening and scoring module.

[0116] (2) Recommend courses for each competency dimension to employees in order of priority.

[0117] (3) Employees choose corresponding courses, register, and participate in training.

[0118] (4) Send the training questionnaire to employees within 1 working day after the training period ends.

[0119] (5) The questionnaire information provided by employees is stored in the job course library module for use in the next recommendation and calculation of course training preferences.

[0120] In the above embodiment, an employee job competency model is established, and the actual work competency level of the employee is calculated based on the employee's daily work data and matched with the job competency model to find the specific capabilities that need to be improved and the corresponding level. Then, courses that meet the requirements are selected from the course library, and similar courses are scored based on the employee's training preferences, so as to obtain a list of courses that are most suitable for employee participation.

[0121] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0122] Based on the same inventive concept, the embodiment of the present application also provides a course recommendation device for implementing the course recommendation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more course recommendation device embodiments provided below can refer to the limitations of the course recommendation method above, and will not be repeated here.

[0123] In one embodiment, Figure 8 As shown, a course recommendation device is provided, including: a job information determination module 801, an ability level determination module 802, a target course selection module 803, a sorting module 804 and a push module 805, wherein:

[0124] The job information determination module 801 is used to determine the user of the course to be recommended and obtain the user's job information;

[0125] A capability level determination module 802 is used to determine the capability level of each position capability corresponding to the user according to the position information;

[0126] The target course selection module 803 is used to select target courses corresponding to each ability level;

[0127] The sorting module 804 is used to obtain the historical preference information corresponding to the user and sort the target courses according to the historical preference information;

[0128] The push module 805 is used to push the sorted target courses to the user.

[0129] In one embodiment, the above device further comprises:

[0130] Evaluation information acquisition module, used to obtain the user's preferred evaluation information after the target course is completed;

[0131] The sorting module 804 includes:

[0132] An evaluation information acquisition unit, used to acquire the preference evaluation information of each completed course corresponding to the user;

[0133] The calculation unit is used to calculate the historical preference information according to each preference evaluation information.

[0134] In one of the embodiments, the evaluation information acquisition module is also used to push a corresponding preference questionnaire to the user after the target course is completed, the preference questionnaire including at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index; obtain the completed preference questionnaire, and parse the preference questionnaire to obtain preference evaluation information.

[0135] In one embodiment, the sorting module 804 includes:

[0136] A weight judgment unit, used to judge whether each historical preference information has a target weight, and the target weight is adjusted by the user;

[0137] A sorting unit is used to, when there is a target weight in the historical preference information, calculate a first recommendation index for the target course based on the historical preference information and the target weight, and sort the target courses according to the first recommendation index; when there is no target weight in the historical preference information, obtain a default weight, calculate a second recommendation index for the target course based on the historical preference information and the default weight, and sort the target courses according to the second recommendation index.

[0138] In one of the embodiments, the push module 805 is further used to obtain the sorted target courses corresponding to each ability level; and push the sorted target courses to the user according to the ability level.

[0139] In one embodiment, the capability level determination module 802 includes:

[0140] A model determination unit, used to determine a job capability calculation model corresponding to the job information, wherein the job capability calculation model includes job capability dimensions and grade calculation rules corresponding to each job capability dimension;

[0141] A work data acquisition unit, used to acquire work data corresponding to the user according to the level calculation rule;

[0142] The capability level calculation unit is used to obtain the capability level of each position based on work data and corresponding level calculation rules.

[0143] Each module in the above-mentioned course recommendation device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0144] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store job information, historical preference information, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a course recommendation method is implemented.

[0145] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: determining the user to whom the course is to be recommended, and obtaining the user's job information; determining the ability level of each job ability corresponding to the user based on the job information; selecting target courses corresponding to each ability level; obtaining the user's corresponding historical preference information, and sorting the target courses based on the historical preference information; and pushing the sorted target courses to the user.

[0147] In one embodiment, after the processor executes the computer program and pushes the sorted target course to the user, it includes: obtaining the user's preference evaluation information after the target course is completed; the processor executes the computer program and obtains the user's corresponding historical preference information, including: obtaining the preference evaluation information of each completed course corresponding to the user; and calculating the historical preference information based on each preference evaluation information.

[0148] In one embodiment, the processor executes a computer program to obtain the user's preference evaluation information after the target course ends, including: after the target course ends, pushing a corresponding preference questionnaire to the user, the preference questionnaire including at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index; obtaining the completed preference questionnaire, and parsing the preference questionnaire to obtain the preference evaluation information.

[0149] In one embodiment, the processor executes a computer program to sort target courses according to historical preference information, including: determining whether each historical preference information has a target weight, where the target weight is adjusted by the user; when the historical preference information has a target weight, calculating a first recommendation index for the target course based on the historical preference information and the target weight, and sorting the target courses according to the first recommendation index; when the historical preference information does not have a target weight, obtaining a default weight, calculating a second recommendation index for the target course based on the historical preference information and the default weight, and sorting the target courses according to the second recommendation index.

[0150] In one embodiment, the pushing of sorted target courses to users is implemented when the processor executes a computer program, including: obtaining sorted target courses corresponding to each ability level; and pushing the sorted target courses to users according to the ability level.

[0151] In one embodiment, the method implemented when a processor executes a computer program to determine the ability level of each job ability corresponding to a user based on job information includes: determining a job ability calculation model corresponding to the job information, the job ability calculation model including job ability dimensions and level calculation rules corresponding to each job ability dimension; obtaining work data corresponding to the user based on the level calculation rules; and obtaining the ability level of each job ability based on the work data and the corresponding level calculation rules.

[0152] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: determining the user to whom the course is to be recommended, and obtaining the user's job information; determining the ability level of each job ability corresponding to the user based on the job information; selecting target courses corresponding to each ability level; obtaining the user's corresponding historical preference information, and sorting the target courses based on the historical preference information; and pushing the sorted target courses to the user.

[0153] In one embodiment, after the computer program is executed by the processor and the sorted target courses are pushed to the user, the steps include: obtaining the user's preference evaluation information after the target course is completed; obtaining the user's corresponding historical preference information when the computer program is executed by the processor, including: obtaining the preference evaluation information of each completed course corresponding to the user; and calculating the historical preference information based on each preference evaluation information.

[0154] In one embodiment, the computer program, when executed by a processor, implements obtaining the user's preference evaluation information after the target course ends, including: after the target course ends, pushing a corresponding preference questionnaire to the user, the preference questionnaire including at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index; obtaining the completed preference questionnaire, and parsing the preference questionnaire to obtain the preference evaluation information.

[0155] In one embodiment, the computer program implemented when being executed by a processor sorts target courses according to historical preference information, including: determining whether each historical preference information has a target weight, where the target weight is adjusted by the user; when the historical preference information has a target weight, calculating a first recommendation index of the target course according to the historical preference information and the target weight, and sorting the target courses according to the first recommendation index; when the historical preference information does not have a target weight, obtaining a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sorting the target courses according to the second recommendation index.

[0156] In one embodiment, the computer program implemented by the processor when executing the program pushes the sorted target courses to the user, including: obtaining the sorted target courses corresponding to each ability level; and pushing the sorted target courses to the user according to the ability level.

[0157] In one embodiment, the computer program implemented when executed by the processor determines the ability level of each job ability corresponding to the user based on the job information, including: determining a job ability calculation model corresponding to the job information, the job ability calculation model including job ability dimensions and level calculation rules corresponding to each job ability dimension; obtaining the work data corresponding to the user according to the level calculation rules; and obtaining the ability level of each job ability according to the work data and the corresponding level calculation rules.

[0158] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the following steps: determining the user to whom a course is to be recommended, and obtaining the user's job information; determining the ability level of each job ability corresponding to the user based on the job information; selecting target courses corresponding to each ability level; obtaining the user's corresponding historical preference information, and sorting the target courses based on the historical preference information; and pushing the sorted target courses to the user.

[0159] In one embodiment, after the computer program is executed by the processor and the sorted target courses are pushed to the user, the steps include: obtaining the user's preference evaluation information after the target course is completed; obtaining the user's corresponding historical preference information when the computer program is executed by the processor, including: obtaining the preference evaluation information of each completed course corresponding to the user; and calculating the historical preference information based on each preference evaluation information.

[0160] In one embodiment, the computer program, when executed by a processor, implements obtaining the user's preference evaluation information after the target course ends, including: after the target course ends, pushing a corresponding preference questionnaire to the user, the preference questionnaire including at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index; obtaining the completed preference questionnaire, and parsing the preference questionnaire to obtain the preference evaluation information.

[0161] In one embodiment, the computer program implemented when being executed by a processor sorts target courses according to historical preference information, including: determining whether each historical preference information has a target weight, where the target weight is adjusted by the user; when the historical preference information has a target weight, calculating a first recommendation index of the target course according to the historical preference information and the target weight, and sorting the target courses according to the first recommendation index; when the historical preference information does not have a target weight, obtaining a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sorting the target courses according to the second recommendation index.

[0162] In one embodiment, the computer program implemented by the processor when executing the program pushes the sorted target courses to the user, including: obtaining the sorted target courses corresponding to each ability level; and pushing the sorted target courses to the user according to the ability level.

[0163] In one embodiment, the computer program implemented when executed by the processor determines the ability level of each job ability corresponding to the user based on the job information, including: determining a job ability calculation model corresponding to the job information, the job ability calculation model including job ability dimensions and level calculation rules corresponding to each job ability dimension; obtaining the work data corresponding to the user according to the level calculation rules; and obtaining the ability level of each job ability according to the work data and the corresponding level calculation rules.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A course recommendation method, characterized in that: The method comprises: Determine the user to whom the course is to be recommended and obtain the user's job information; Determine whether the user can learn new courses based on the number of courses the user is currently learning and has not learned; In the case where the user can learn new courses, determining the ability level of each position ability corresponding to the user according to the position information; Selecting target courses corresponding to the competence levels, including selecting corresponding target courses based on job competence and corresponding competence levels, wherein the competence levels corresponding to the job competences and the target courses corresponding to each competence level are pre-set in the server; Acquire historical preference information corresponding to the user, and sort the target courses corresponding to each ability level according to the historical preference information; Pushing the sorted target courses to the user; Determining the ability level of each position ability corresponding to the user according to the position information includes: Determine a job capability calculation model corresponding to the job information, wherein the job capability calculation model includes job capability dimensions and grade calculation rules corresponding to each of the job capability dimensions; Acquire the work data corresponding to the user according to the level calculation rule; The ability level of each position ability is obtained according to the work data and the corresponding level calculation rules.

2. The method according to claim 1, characterized in that After pushing the sorted target courses to the user, the method includes: Obtaining the user's preference evaluation information after the target course is completed; The obtaining of the historical preference information corresponding to the user includes: Obtaining preference evaluation information of each completed course corresponding to the user; The historical preference information is calculated based on each of the preference evaluation information.

3. The method according to claim 2, characterized in that The obtaining of the user's preference evaluation information after the target course is completed includes: After the target course is finished, a corresponding preference questionnaire is pushed to the user, wherein the preference questionnaire includes at least one of a teaching format index, a lecturer type index, a course duration index, and a lecturer level index; A completed preference questionnaire is obtained, and the preference questionnaire is parsed to obtain preference evaluation information.

4. The method according to claim 1, characterized in that: Sorting the target courses corresponding to each ability level according to the historical preference information includes: Determining whether each of the historical preference information has a target weight, where the target weight is adjusted by the user; When the historical preference information has a target weight, a first recommendation index of the target course is calculated according to the historical preference information and the target weight, and the target courses are sorted according to the first recommendation index; When the historical preference information does not have a target weight, a default weight is obtained, a second recommendation index of the target course is calculated according to the historical preference information and the default weight, and the target courses are sorted according to the second recommendation index.

5. The method according to claim 1, characterized in that The pushing the sorted target courses to the user comprises: Obtain the sorted target courses corresponding to each of the ability levels; The sorted target courses are pushed to the user according to the ability level.

6. A course recommendation device, characterized in that: The device comprises: A job information determination module is used to determine the user of the course to be recommended and obtain the job information of the user; A capability level determination module, used to determine whether the user can learn new courses based on the number of courses that the user is currently learning and has not learned of the courses to be recommended; if the user can learn new courses, determine the capability level of each position capability corresponding to the user according to the position information; A target course selection module is used to select target courses corresponding to each of the competence levels, including selecting corresponding target courses based on job competence and corresponding competence levels, wherein the competence levels corresponding to each job competence and the target courses corresponding to each competence level are pre-set in the server; A sorting module, used for obtaining the historical preference information corresponding to the user, and sorting the target courses corresponding to each ability level according to the historical preference information; A push module, used for pushing the sorted target courses to the user; The capability level determination module comprises: A model determination unit, used to determine a job capability calculation model corresponding to the job information, wherein the job capability calculation model includes job capability dimensions and grade calculation rules corresponding to each of the job capability dimensions; A work data acquisition unit, used for acquiring the work data corresponding to the user according to the level calculation rule; The capability level calculation unit is used to obtain the capability level of each position capability according to the work data and the corresponding level calculation rules.

7. The device according to claim 6, characterized in that The device also includes: An evaluation information acquisition module, used to acquire the user's preference evaluation information after the target course is completed; The sorting module comprises: An evaluation information acquisition unit, used to acquire the preference evaluation information of each completed course corresponding to the user; A calculation unit is used to calculate the historical preference information according to each of the preference evaluation information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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