CN114662920A - Course pushing method, device, computer equipment, storage medium and program product - Google Patents
Course pushing method, device, computer equipment, storage medium and program product Download PDFInfo
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Abstract
The application relates to a course recommendation method, a course recommendation device, a computer device, a storage medium and a computer program product. The method comprises the following steps: determining a user of a course to be recommended, and acquiring the post information of the user; determining the capability level of each post capability corresponding to the user according to the post information; selecting a target course corresponding to each of the capability levels; acquiring historical preference information corresponding to the user, and sequencing the target courses according to the historical preference information; and pushing the sorted target courses to the user. By adopting the method, the corresponding target courses are selected according to the capability levels of all posts of the user, then the target courses are sorted according to the historical preference information of the user, and finally the sorted target courses are pushed to the user, so that the pushed courses are selected according to the capability levels of the user and then sorted according to the preference information of the user, and the pushed courses are more accurate.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a course pushing method, an apparatus, a computer device, a storage medium, and a computer program product.
Background
The staff post capability training is a necessary means for improving the working capability of staff and the working efficiency of a company, reasonable and effective training courses are arranged for each staff, the staff training requirements can be met, the staff working skills can be quickly improved, and the working time loss caused by training can be reduced to the maximum extent.
Currently, there are generally three forms of staff training arrangements: the method has the advantages that employees at a specific post level are required to participate forcibly, a straight line manager issues a training task, the employees evaluate and participate voluntarily, scientific and targeted training course recommendation is lacked, training courses participated by the employees are inconsistent with actual requirements, and the training effect is poor.
Disclosure of Invention
In view of the above, it is desirable to provide a course pushing method, apparatus, computer device, computer readable storage medium and computer program product capable of improving accuracy of course recommendation.
In a first aspect, the present application provides a course recommendation method, including:
determining a user of a course to be recommended, and acquiring the post information of the user;
determining the capability level of each post capability corresponding to the user according to the post information;
selecting a target course corresponding to each of the capability levels;
acquiring historical preference information corresponding to the user, and sequencing the target courses according to the historical preference information;
and pushing the sorted target courses to the user.
In one embodiment, after pushing the ranked target courses to the user, the method includes:
acquiring preference evaluation information of the user after the target course is finished;
the obtaining of the historical preference information corresponding to the user includes:
acquiring preference evaluation information of each finished course corresponding to the user;
and calculating historical preference information according to the preference evaluation information.
In one embodiment, the obtaining preference evaluation information of the user after the target course is finished includes:
after the target course is finished, pushing a corresponding preference questionnaire to the user, wherein the preference questionnaire comprises at least one of a lecture form index, an instructor type index, a course duration index and an instructor level index;
and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
In one embodiment, the sorting the target courses according to the historical preference information includes:
judging whether each piece of historical preference information has a target weight, wherein the target weight is obtained by adjusting 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 sequencing the target course according to the first recommendation index;
and when the historical preference information does not have the target weight, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target course according to the second recommendation index.
In one embodiment, the pushing the ranked target courses to the user includes:
obtaining the sorted target courses corresponding to the capacity levels;
and pushing the sorted target courses to the user according to the capability level.
In one embodiment, the determining, according to the post information, the capability level of each post capability corresponding to the user includes:
determining a post capability calculation model corresponding to the post information, wherein the post capability calculation model comprises post capability dimensions and a grade calculation rule corresponding to each post capability dimension;
acquiring working data corresponding to the user according to the grade calculation rule;
and calculating rules according to the working data and the corresponding levels to obtain the capability levels of the capability of each post.
In a second aspect, the present application further provides a course recommending apparatus, comprising:
the post information determining module is used for determining a user of a course to be recommended and acquiring post information of the user;
the capacity level determining module is used for determining the capacity level of each post capacity corresponding to the user according to the post information;
the target course selection module is used for selecting target courses corresponding to the capacity levels;
the sequencing module is used for acquiring historical preference information corresponding to the user and sequencing the target courses according to the historical preference information;
and the pushing module is used for pushing the sorted target courses to the user.
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 embodiments when executing the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method in any of the above-described embodiments.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the method in any of the embodiments described above.
According to the course pushing method, the device, the computer equipment, the storage medium and the computer program product, the corresponding target courses are selected according to the ability levels of all posts of the user, then the target courses are sequenced according to the historical preference information of the user, and finally the sequenced target courses are pushed to the user, so that the pushed courses are more accurate by selecting according to the ability levels of the user and then sequencing according to the preference information of the user.
Drawings
FIG. 1 is a diagram of an application environment of a course pushing method in one embodiment;
FIG. 2 is a flowchart illustrating a course pushing method according to an embodiment;
FIG. 3 is a flowchart illustrating the course recommendation feedback step in one embodiment;
FIG. 4 is a flowchart of a target course ordering step in one embodiment;
FIG. 5 is a flow diagram that illustrates the steps of calculating a capability level, in one embodiment;
FIG. 6 is a block diagram of a post capability model module in accordance with one embodiment;
FIG. 7 is a block diagram that illustrates the structure of the post curriculum library module storage structure in one embodiment;
FIG. 8 is a block diagram of a course pushing apparatus in one embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The course recommendation method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server.
The terminal 102 may send a course pushing request to the server 104, or the server 104 may obtain a user to be pushed a course according to a timing task or the like, and obtain the post information of the user, so as to determine the capability level of each post capability corresponding to the user according to the post information; selecting target courses corresponding to each capability level; acquiring historical preference information corresponding to a user, and sequencing target courses according to the historical preference information; and pushing the sorted target courses to the user, for example, sending the sorted target courses to the terminal 102 of the user.
The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, as shown in fig. 2, a course recommending method is provided, which is exemplified by the method applied to the server in fig. 1, and includes the following steps:
s202: and determining a user of the course to be recommended, and acquiring the post information of the user.
Specifically, the user to recommend the course may be determined by the employee name and/or job number sent by the terminal, and in other embodiments, the employee name and/or job number may also be directly input to the server. The post information refers to the post where the user is located in the company, such as a development post, a testing post, an administrative post, or a management post, and the like.
The server queries according to the employee name and/or the job number to acquire the post information of the user, for example, after the server performs fuzzy query or accurate query according to the employee name and/or the job number, the server determines the optimal employee, and then reads the post information of the optimal employee. Preferably, when the name is queried, the corresponding employee is determined in a fuzzy query mode, and if the names of a plurality of employees are the same, the user can select the employee with the same name according to basic information of the employees with the same name, or the employee with the same name is scored according to information of the querier so as to obtain the employee with the score meeting the requirement. For example, if the inquirer and one of the employees with the same name belong to the same department, it is preferable to determine that the employee is the user of the course to be recommended, and acquire the position information of the employee.
In one embodiment, for the selection of the user, the server determines whether the user can learn a new course according to the number of courses currently being learned and not learned by the user, so as to avoid the situation that the user selects too many courses but does not learn any course.
S204: and determining the capability level of each post capability corresponding to the user according to the post information.
The post capability is the capability required to be possessed by each preset post, and one post should have at least one dimension of post capability, and each post capability comprises at least one capability level. It should be noted that the required position capability of different positions may be different, the same or partially the same, and is not limited herein. For example, a software test station, which needs to test the capability of five dimensions of theoretical level, test design capability, test development capability, test execution capability, and non-functional test capability, each station capability can be divided into 5 levels. Each level may also be divided into different level levels, for example, 20 total degrees for each station capability, 5 levels for each 4 levels, and 4 level levels for each level.
The server can determine the corresponding post capability according to the post information, then obtain the working data of the user, and calculate the capability level corresponding to each post capability according to the working data.
S206: target courses corresponding to the respective capability levels are selected.
Specifically, the server has preset target courses of capability levels corresponding to each station capability, for example, each station capability corresponds to multiple capability levels, and each capability level corresponds to multiple target courses. Therefore, after the server calculates the capability level, the server selects the corresponding target course according to the post capability and the corresponding capability level, so as to realize the first screening of the recommended course of the user.
S208: and acquiring historical preference information corresponding to the user, and sequencing the target courses according to the historical preference information.
Specifically, the historical preference information is generated according to the preference of the user for selecting the courses historically or the historical evaluation information of the courses, wherein the historical preference information can be determined according to the training preference of the staff and comprises at least one of a lecturer category preference dimension, a lecture form preference dimension, a class time length preference dimension and a lecturer level preference dimension.
The server ranks the target courses corresponding to each capability level according to the historical preference information, for example, calculates the score of each target course according to the dimension and the weight corresponding to the dimension in the historical preference information, and ranks the target courses according to the calculated scores.
S210: and pushing the sorted target courses to the user.
Specifically, after the target courses are sorted, the server pushes the target courses to the user.
In a preferred embodiment, the server may push the sorted target courses in the form of a legend, for example, the capability level corresponding to each station capability is given in the legend, and then the sorted target courses are given at the capability level. Or when the user clicks the capability level, jumping to a corresponding page, displaying the ordered target courses on the page for the user to select, and after the course corresponding to one of the post capabilities is selected, continuing to select other courses corresponding to other post capabilities until the course corresponding to each of the post capabilities is selected to be finished, for example, the number of the course selections corresponding to each of the post capabilities meets the requirement.
According to the course pushing method, the corresponding target courses are selected according to the ability level of each post of the user, then the target courses are sequenced according to the historical preference information of the user, and finally the sequenced target courses are pushed to the user.
In one embodiment, after pushing the sorted target courses to the user, the method includes: acquiring preference evaluation information of a user after a target course is finished; acquiring historical preference information corresponding to a user, wherein the historical preference information comprises: acquiring preference evaluation information of each finished course corresponding to a user; and calculating historical preference information according to the preference evaluation information.
In one embodiment, acquiring preference evaluation information of a user after a target course is finished includes: after the target course is finished, pushing a corresponding preference questionnaire to the user, wherein the preference questionnaire comprises at least one of a teaching form index, an instructor type index, a course duration index and an instructor level index; and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
The preference evaluation information can be acquired in a preference questionnaire form, in other embodiments, the preference evaluation information of the user can be calculated by acquiring the user behavior of the user in the course, so that the user does not need to fill in a corresponding preference questionnaire additionally, the efficiency is improved, and the preference evaluation information acquired by acquiring the user behavior and calculated according to the class duration of the user and the micro-expression during class.
The preference evaluation information is described in the present embodiment with respect to the travel of a preference questionnaire, wherein the preference questionnaire may include at least one of a lecture form index, an instructor type index, a lesson duration index, and an instructor level index, and the lecture form index includes a lecture lesson (whose user behavior may be acquired by a monitoring apparatus) and an online lesson. The instructor-type index includes an internal instructor and an external instructor. The lesson duration index may be classified according to the time of the lesson, such as the lesson duration exceeding a preset length, such as 2 hours, and the lesson duration being less than or equal to the preset length. The instructor level index is classified according to the class of the instructor, such as the instructor at level 1-2, the instructor at level 3-4, and so on.
In this embodiment, the server obtains the preference questionnaire filled by the user, and then reads the score given by the user in the preference questionnaire, which is the preference evaluation information. Optionally, the server may parse the preference questionnaire, where the preference questionnaires are all the same, so the server may determine the evaluation information given by the user according to the location information of each option in the preference questionnaire, for example, for one of the dimensions, the location of each evaluation score is the same, and determine the evaluation score of each dimension according to the selected location in the preference questionnaire.
As shown in fig. 3, the server also performs course recommendation and feedback, and recommends the target course to the user according to the priority order, so that the user can select the corresponding course with reference to the priority order. After the staff complete training, the questionnaire is pushed to the user, the evaluation information of the staff on the training courses is obtained and stored in the post course library module, and the evaluation information is used for calculating course training preference when recommended next time.
Specifically, the server acquires recommended courses and priorities of the employees on each capability dimension from a course screening and grading module; recommending the courses of each ability dimension to the staff according to the priority order; thus, the staff selects the corresponding course to register and participates in training; pushing a training questionnaire to the staff within 1 working day after the training time is finished; and storing the questionnaire information fed back by the staff into a post course library module for use when the course training preference is recommended and calculated next time.
In one embodiment, the sorting of the target courses according to the historical preference information comprises: judging whether each piece of historical preference information has a target weight, wherein the target weight is obtained by adjusting a user; when the historical preference information has the target weight, calculating according to the historical preference information and the target weight to obtain a first recommendation index of the target course, and sequencing the target course according to the first recommendation index; and when the target weight does not exist in the historical preference information, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target course according to the second recommendation index.
FIG. 4, in conjunction with FIG. 4, is a flow diagram of the target course ranking step in an embodiment where the server first obtains the capability level of the employee's post capability; and screening target courses meeting the current capability level of the employee from the post course library for each post capability required to be mastered by the employee.
And calculating the employee training preference score of each target course for the target courses meeting the same capability level requirement. The calculation rule of the employee training preference score s is as follows: the server further provides a separate interface, which allows an employee or a line manager thereof to adjust the weight parameters, and the user can adjust the target weight according to actual conditions, so that when a first recommended index corresponding to each target course is obtained through calculation, the target courses can be obtained for calculation, and the target courses are sorted according to the first recommended index. And when the target weight does not exist, calculating a second recommendation index corresponding to each target course according to the default weight, and sequencing the target courses according to the second recommendation index.
The calculation method of the lecturer type score a comprises the following steps: and calculating the average score of the internal instructor in the employee historical course evaluation as 1, and the average score of the external instructor as 2, wherein if the current course is the internal instructor, a is equal to a1, and otherwise, a is equal to a 2.
The calculation method of the lecture form score b comprises the following steps: calculating the average score of the face lesson b1 and the average score of the online lesson b2 in the employee historical lesson evaluation, wherein if the current lesson is the face lesson, b is b1, otherwise, b is b 2.
The calculation method of the class time length score c comprises the following steps: and calculating the average class time division c1 within 2 hours of the class time length and the average class time division c2 over 2 hours of the class time length in the historical class evaluation of the employee, wherein if the current class time length is within 2 hours, c is c1, otherwise, c is c 2.
The calculation method of the teacher level score d comprises the following steps: in the employee historical course evaluation, the average score d1 of the instructor level below a specific level, the average score d2 of the instructor level above the specific level are calculated, if the instructor level of the current course is below the specific level, d is d1, otherwise d is d 2.
And finally, the server performs priority sequencing on the courses with the same capability level according to the descending order of the employee training preference scores of the courses.
In the above embodiment, the recommendation index of the corresponding target course is calculated according to the historical preference information and the corresponding weight.
In one embodiment, pushing the sorted target courses to the user includes: obtaining the sorted target courses corresponding to each capability level; and pushing the sorted target courses to the user according to the capability level.
The server can sort the target courses corresponding to the capability levels of each post capability, and then push the sorted target courses of each capability level to the user.
Specifically, the server may push the sorted target courses in the form of a legend, for example, the capability level corresponding to each post capability is given in the legend, and then the sorted target courses are given at the capability level. Or when the user clicks the capability level, jumping to a corresponding page, displaying the ordered target courses on the page for the user to select, and after the course corresponding to one of the post capabilities is selected, continuing to select other courses corresponding to other post capabilities until the course corresponding to each of the post capabilities is selected to be finished, for example, the number of the course selections corresponding to each of the post capabilities meets the requirement.
In one embodiment, determining the capability level of each post capability corresponding to the user according to the post information includes: determining a post capability calculation model corresponding to the post information, wherein the post capability calculation model comprises post capability dimensions and a level calculation rule corresponding to each post capability dimension; acquiring working data corresponding to a user according to a grade calculation rule; and calculating rules according to the working data and the corresponding levels to obtain the capability levels of the capability of each post.
Specifically, referring to fig. 5, fig. 5 is a flowchart of a capability level calculation step in an embodiment, in the embodiment, a post capability model module stores capability models and calculation rules of different posts, analyzes specific work duties and requirements of each post, establishes a post capability model including five dimensions and five levels, divides the post capability of each post into five dimensions, divides the capability of each dimension into five levels, and divides the capability into five dimensions including a theoretical level of testing, a design capability of testing, a development capability of testing, an execution capability of testing, and a non-functional testing capability, and divides each dimension into 20 grades, and divides each 4 grade into 5 grades.
On the basis, a position capability model calculation rule is defined and used for calculating the specific score of each dimension capability of the employee. Data required in the calculation rule generally comprise theoretical knowledge assessment results, workload output, work performance standard reaching conditions and the like. For example, the testing theoretical level capability score of the software testing position is obtained by calculating the average score of relevant theoretical examination results of the employee in the last quarter and converting the average score into a 20-score system. The structure of the station capability model module is shown in fig. 6.
The post course library module stores all training courses and course information which are currently existing and planned to be developed, and the training courses and the course information comprise audience posts corresponding to the courses, post capability levels corresponding to the courses, course teacher information, teaching modes, historical training participation conditions of employees, historical employee evaluation information and the like. The station course library module storage structure is shown in figure 7.
Such a server, when computing the power level, may include: the user inputs the employee name/job number of the course to be recommended; acquiring post information of corresponding staff; according to the post to which the employee belongs, acquiring a post capability model and a calculation rule corresponding to the post to which the employee belongs from a post capability model module; according to the post capability model calculation rule, acquiring data (mainly daily performance data of the employee, such as scores of participating in theoretical examinations, historical workload, historical performance contribution and the like) required by rule calculation, calculating scores of all capability dimensions of the employee, specifically, general calculation logic universal for all post capabilities comprises average scores of the theoretical examinations, and other post capabilities need to be customized for specific posts and are stored in a system database in advance; and obtaining the grade of the employee on each ability according to the individual ability dimension scores of the employee.
In the embodiment, the employee post capability model is established, the personal actual working capability level of the employee is calculated according to the daily working data of the employee and is matched with the post capability model, the specific capability and the corresponding level which need to be improved are found, the required courses are screened from the course library, the courses of the same type are graded according to the training preference of the employee, and therefore the course list which is most suitable for the employee to participate is obtained.
In order to make the application well understood by those skilled in the art, the practical application is exemplified and generally includes a post capability model module, a post course library module, an employee post capability calculation module, a course screening and scoring module, and a course recommendation and feedback module.
The system comprises a post capability model module, a data analysis module and a data analysis module, wherein the post capability model module stores capability models and calculation rules of different posts, analyzes specific work duties and requirements of each post, establishes a post capability model containing five dimensions and five levels, divides the post capability of each post into five dimensions, divides the capability of each dimension into five levels, takes a software testing post as an example, divides the capability into five dimensions including a testing theoretical level, a testing design capability, a testing development capability, a testing execution capability and a non-functional testing capability, divides each dimension into 20 grades, divides each dimension into 4 grades and divides each grade into 5 grades.
On the basis, a position capability model calculation rule is defined and used for calculating the specific score of each dimension capability of the employee. Data required in the calculation rule generally comprise theoretical knowledge assessment results, workload output, work performance standard reaching conditions and the like. For example, the testing theoretical level capability score of the software testing position is obtained by calculating the average score of relevant theoretical examination results of the employee in the last quarter and converting the average score into a 20-score system.
The post course library module stores all training courses and course information which are currently existing and planned to be developed, and the training courses and the course information comprise audience posts corresponding to the courses, post capability levels corresponding to the courses, course teacher information, teaching modes, historical training participation conditions of employees, historical employee evaluation information and the like.
The employee post capability calculating module calculates the post capability level of the employee according to the following process and comprises the following steps:
(1) the user enters the employee's name/job number for which the course needs to be recommended.
(2) And acquiring the post information of the corresponding staff.
(3) And acquiring a post capability model and a calculation rule corresponding to the post to which the employee belongs from the post capability model module according to the post to which the employee belongs.
(4) According to the post capability model calculation rule, data (mainly daily performance data of the staff, such as scores of participating in theoretical examinations, historical workloads, historical performance contributions and the like) required by rule calculation is obtained, and each capability dimension score of the staff is calculated.
(5) And obtaining the grade of the employee on each ability according to the scores of the individual abilities of the employee.
The course screening and grading module screens employee recommended courses according to the following procedures, and specifically comprises the following steps:
(1) and acquiring the employee post capability level.
(2) And screening out courses which meet the current capability level of the employee from the post course library for each capability which needs to be mastered by the employee.
(3) And calculating the employee training preference score of each course for the courses meeting the same capability level requirement. The calculation rule of the employee training preference score s is as follows: the system provides a separate interface, allows the staff or the line manager to adjust the weight parameters, and the user can adjust the system according to the actual situation, wherein a is an instructor category score, b is a lecture form score, c is a class time length score, d is an instructor level score, r1, r2, r3 and r4 are each score weight, and the initial settings are r 1-0.3, r 2-0.3, r 3-0.2 and r 4-0.2.
The calculation method of the lecturer type score a comprises the following steps: and calculating the average score of the internal instructor in the employee historical course evaluation as 1, and the average score of the external instructor as 2, wherein if the current course is the internal instructor, a is equal to a1, and otherwise, a is equal to a 2.
The calculation method of the lecture form score b comprises the following steps: calculating the average score b1 of the face lesson and the average score b2 of the online lesson in the employee historical lesson evaluation, wherein if the current lesson is the face lesson, b is 1, otherwise b is 2.
The calculation method of the class time length score c comprises the following steps: and calculating the average class time division c1 within 2 hours of the class time length and the average class time division c2 over 2 hours of the class time length in the historical class evaluation of the employee, wherein if the current class time length is within 2 hours, c is c1, otherwise, c is c 2.
The calculation method of the teacher level score d comprises the following steps: in the employee historical course evaluation, the average score d1 of the instructor level below a specific level, the average score d2 of the instructor level above the specific level are calculated, if the instructor level of the current course is below the specific level, d is d1, otherwise d is d 2.
(4) And carrying out priority ordering on the courses with the same capability level according to the descending order of the employee training preference scores of the courses.
The course recommending and feedback module is mainly responsible for recommending and feeding back courses, and recommending the courses to the user according to the priority sequence, so that the user can conveniently select the corresponding courses according to the priority sequence. After the staff complete training, the questionnaire is pushed to the user, the evaluation information of the staff on the training courses is obtained and stored in the post course library module, and the evaluation information is used for calculating course training preference when recommended next time. The method specifically comprises the following steps:
(1) and acquiring recommended courses and priorities of the employee on each capability dimension from the course screening and grading module.
(2) And recommending the courses of each capability dimension to the employee according to the priority order.
(3) And the staff selects the corresponding course registration and participates in training.
(4) And pushing a training questionnaire to the staff within 1 working day after the training time is finished.
(5) And storing the questionnaire information fed back by the staff into a post course library module for use when the course training preference is recommended and calculated next time.
In the embodiment, the employee post capability model is established, the personal actual working capability level of the employee is calculated according to the daily working data of the employee and is matched with the post capability model, the specific capability and the corresponding level which need to be improved are found, the required courses are screened from the course library, the courses of the same type are graded according to the training preference of the employee, and therefore the course list which is most suitable for the employee to participate is obtained.
It should be understood that, although the steps in the flowcharts related to the embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the above embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides a course recommending apparatus for implementing the course recommending method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitations in one or more of the embodiments of the course recommendation device provided below can be referred to the limitations of the course recommendation method in the foregoing, and are not described herein again.
In one embodiment, as shown in fig. 8, there is provided a course recommending apparatus including: a post information determining module 801, a capability level determining module 802, a target course selecting module 803, an ordering module 804 and a pushing module 805, wherein:
the position information determining module 801 is used for determining a user of a course to be recommended and acquiring position information of the user;
a capability level determining module 802, configured to determine, according to the post information, a capability level of each post capability corresponding to the user;
a target course selection module 803, configured to select a target course corresponding to each capability level;
the sorting module 804 is configured to obtain historical preference information corresponding to a user, and sort the target courses according to the historical preference information;
and a pushing module 805 configured to push the sorted target courses to the user.
In one embodiment, the apparatus further includes:
the evaluation information acquisition module is used for acquiring preference evaluation information of the user after the target course is finished;
the sorting module 804 includes:
the evaluation information acquisition unit is used for acquiring preference evaluation information of each finished course corresponding to the user;
and the calculating unit is used for calculating historical preference information according to the preference evaluation information.
In one embodiment, the evaluation information obtaining module is further configured to push a corresponding preference questionnaire to the user after the target course is finished, where the preference questionnaire includes at least one of a lecture form index, an instructor type index, a course duration index, and an instructor level index; and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
In one embodiment, the sorting module 804 includes:
the weight judging unit is used for judging whether each piece of historical preference information has a target weight, and the target weight is obtained by adjusting by a user;
the sorting unit is used for calculating a first recommendation index of the target course according to the historical preference information and the target weight when the historical preference information has the target weight, and sorting the target course according to the first recommendation index; and when the target weight does not exist in the historical preference information, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target courses according to the second recommendation index.
In one embodiment, the pushing module 805 is further configured to obtain the sorted target courses corresponding to each capability level; and pushing the sorted target courses to the user according to the capability level.
In one embodiment, the capability level determining module 802 includes:
the model determining unit is used for determining a post capability calculation model corresponding to the post information, and the post capability calculation model comprises post capability dimensions and a grade calculation rule corresponding to each post capability dimension;
the working data acquisition unit is used for acquiring working data corresponding to the user according to the grade calculation rule;
and the capability level calculation unit is used for obtaining the capability level of each post capability according to the working data and the corresponding calculation rule of each level.
The modules of the course recommending apparatus can be implemented in whole or in part by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface 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, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store station information, historical preference information, 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 is executed by a processor to implement a course recommendation method.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: determining a user of a course to be recommended, and acquiring the post information of the user; determining the capability level of each post capability corresponding to the user according to the post information; selecting target courses corresponding to the various capability levels; acquiring historical preference information corresponding to a user, and sequencing target courses according to the historical preference information; and pushing the sorted target courses to the user.
In one embodiment, pushing the ranked target lessons to the user as implemented by the processor executing the computer program comprises: acquiring preference evaluation information of a user after a target course is finished; the obtaining of the historical preference information corresponding to the user, which is realized when the processor executes the computer program, includes: acquiring preference evaluation information of each finished course corresponding to a user; and calculating historical preference information according to the preference evaluation information.
In one embodiment, the obtaining of the preference evaluation information of the user after the target course is ended, which is realized when the processor executes the computer program, comprises: after the target course is finished, pushing a corresponding preference questionnaire to the user, wherein the preference questionnaire comprises at least one of a teaching form index, an instructor type index, a course duration index and an instructor level index; and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
In one embodiment, the ranking of target courses based on historical preference information implemented by a processor executing a computer program comprises: judging whether each piece of historical preference information has a target weight, wherein the target weight is obtained by adjusting by a user; when the historical preference information has the target weight, calculating according to the historical preference information and the target weight to obtain a first recommendation index of the target course, and sequencing the target course according to the first recommendation index; and when the target weight does not exist in the historical preference information, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target course according to the second recommendation index.
In one embodiment, pushing the ranked target lessons to the user, as performed by the processor executing the computer program, comprises: obtaining the sorted target courses corresponding to each capability level; and pushing the sorted target courses to the user according to the capability level.
In one embodiment, the determining the capability level of each of the respective post capabilities corresponding to the user based on the post information when the processor executes the computer program comprises: determining a post capability calculation model corresponding to the post information, wherein the post capability calculation model comprises post capability dimensions and a grade calculation rule corresponding to each post capability dimension; acquiring working data corresponding to a user according to a grade calculation rule; and calculating rules according to the working data and the corresponding levels to obtain the capability levels of the capability of each post.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of: determining a user of a course to be recommended, and acquiring post information of the user; determining the capability level of each post capability corresponding to the user according to the post information; selecting target courses corresponding to the various capability levels; acquiring historical preference information corresponding to a user, and sequencing target courses according to the historical preference information; and pushing the sorted target courses to the user.
In one embodiment, pushing the ranked target lessons to the user, when performed by the processor, comprises: acquiring preference evaluation information of a user after a target course is finished; the computer program is implemented by a processor to acquire historical preference information corresponding to a user, and comprises the following steps: acquiring preference evaluation information of each finished course corresponding to a user; and calculating historical preference information according to the preference evaluation information.
In one embodiment, the obtaining of the preference evaluation information of the user after the target course is finished, which is realized when the computer program is executed by the processor, comprises: after the target course is finished, pushing a corresponding preference questionnaire to the user, wherein the preference questionnaire comprises at least one of a teaching form index, an instructor type index, a course duration index and an instructor level index; and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
In one embodiment, the computer program, when executed by a processor, implements ranking target courses based on historical preference information, comprising: judging whether each piece of historical preference information has a target weight, wherein the target weight is obtained by adjusting a user; when the historical preference information has the target weight, calculating according to the historical preference information and the target weight to obtain a first recommendation index of the target course, and sequencing the target course according to the first recommendation index; and when the target weight does not exist in the historical preference information, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target courses according to the second recommendation index.
In one embodiment, pushing the ranked target lessons to the user, as performed by the computer program when executed by the processor, comprises: obtaining the sorted target courses corresponding to each capability level; and pushing the sorted target courses to the user according to the capability level.
In one embodiment, determining the capability level of each of the respective post capabilities corresponding to the user based on the post information when the computer program is executed by the processor comprises: determining a post capability calculation model corresponding to the post information, wherein the post capability calculation model comprises post capability dimensions and a grade calculation rule corresponding to each post capability dimension; acquiring working data corresponding to a user according to a grade calculation rule; and calculating rules according to the working data and the corresponding levels to obtain the capability levels of the capability of each post.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of: determining a user of a course to be recommended, and acquiring the post information of the user; determining the capability level of each post capability corresponding to the user according to the post information; selecting target courses corresponding to the various capability levels; acquiring historical preference information corresponding to a user, and sequencing target courses according to the historical preference information; and pushing the sorted target courses to the user.
In one embodiment, pushing the ranked target lessons to the user, when performed by the processor, comprises: acquiring preference evaluation information of a user after a target course is finished; the computer program is implemented by a processor to acquire historical preference information corresponding to a user, and comprises the following steps: acquiring preference evaluation information of each finished course corresponding to a user; and calculating historical preference information according to the preference evaluation information.
In one embodiment, the obtaining of the preference evaluation information of the user after the target course is finished, which is realized when the computer program is executed by the processor, comprises: after the target course is finished, pushing a corresponding preference questionnaire to the user, wherein the preference questionnaire comprises at least one of a teaching form index, an instructor type index, a course duration index and an instructor level index; and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
In one embodiment, the computer program, when executed by a processor, implements ranking target courses based on historical preference information, comprising: judging whether each piece of historical preference information has a target weight, wherein the target weight is obtained by adjusting a user; when the historical preference information has the target weight, calculating according to the historical preference information and the target weight to obtain a first recommendation index of the target course, and sequencing the target course according to the first recommendation index; and when the target weight does not exist in the historical preference information, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target courses according to the second recommendation index.
In one embodiment, pushing the ranked target lessons to the user, as performed by the computer program when executed by the processor, comprises: obtaining the sorted target courses corresponding to each capability level; and pushing the sorted target courses to the user according to the capability level.
In one embodiment, determining the capability level of each of the respective post capabilities corresponding to the user based on the post information when the computer program is executed by the processor comprises: determining a post capability calculation model corresponding to the post information, wherein the post capability calculation model comprises post capability dimensions and a grade calculation rule corresponding to each post capability dimension; acquiring working data corresponding to a user according to a grade calculation rule; and calculating rules according to the working data and the corresponding levels to obtain the capability levels of the capability of each post.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A course recommendation method, the method comprising:
determining a user of a course to be recommended, and acquiring the post information of the user;
determining the capability level of each post capability corresponding to the user according to the post information;
selecting a target course corresponding to each of the capability levels;
obtaining historical preference information corresponding to the user, and sequencing the target courses according to the historical preference information;
and pushing the sorted target courses to the user.
2. The method of claim 1, wherein after pushing the ranked target lessons to the user, the method comprises:
acquiring preference evaluation information of the user after the target course is finished;
the obtaining of the historical preference information corresponding to the user includes:
acquiring preference evaluation information of each finished course corresponding to the user;
and calculating historical preference information according to the preference evaluation information.
3. The method as claimed in claim 2, wherein the obtaining preference evaluation information of the user after the target course is finished comprises:
after the target course is finished, pushing a corresponding preference questionnaire to the user, wherein the preference questionnaire comprises at least one of a lecture form index, an instructor type index, a course duration index and an instructor level index;
and acquiring the filled-in preference questionnaire, and analyzing the preference questionnaire to obtain preference evaluation information.
4. The method as recited in claim 1, wherein said ranking said target lessons according to said historical preference information comprises:
judging whether each piece of historical preference information has a target weight, wherein the target weight is obtained by adjusting 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 sequencing the target course according to the first recommendation index;
and when the historical preference information does not have the target weight, acquiring a default weight, calculating a second recommendation index of the target course according to the historical preference information and the default weight, and sequencing the target course according to the second recommendation index.
5. The method as claimed in claim 1, wherein the pushing the ranked target courses to the user comprises:
obtaining the sorted target courses corresponding to the capacity levels;
and pushing the sorted target courses to the user according to the capability level.
6. The method according to claim 1, wherein the determining the capability level of each of the post capabilities corresponding to the user according to the post information comprises:
determining a post capability calculation model corresponding to the post information, wherein the post capability calculation model comprises post capability dimensions and a grade calculation rule corresponding to each post capability dimension;
acquiring working data corresponding to the user according to the grade calculation rule;
and calculating rules according to the working data and the corresponding levels to obtain the capability levels of the capability of each post.
7. A course recommending apparatus, said apparatus comprising:
the post information determining module is used for determining a user of a course to be recommended and acquiring the post information of the user;
the capability level determining module is used for determining the capability level of each post capability corresponding to the user according to the post information;
the target course selection module is used for selecting target courses corresponding to the capacity levels;
the sequencing module is used for acquiring historical preference information corresponding to the user and sequencing the target courses according to the historical preference information;
and the pushing module is used for pushing the sorted target courses to the user.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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