Multi-dimensional course recommendation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a multi-dimensional course recommendation method and device, equipment and a storage medium. According to the technical scheme provided by the embodiment of the invention, the current learning stage and the learning level of the target user are determined, and the target course package is determined from the plurality of preset course packages according to the current learning stage, the learning level and the stage attributes and the level attributes of the plurality of preset course packages; according to the target course package, course recommendation is performed on the target user, the difference between the learning target and the learning stage and the learning level is reduced, the recommendation of the course package fully considers the actual situation of the user, and the recommendation of the target course package based on the learning stage and the learning level can effectively reduce the inaccuracy under a single dimension. The curriculum can be recommended to the user in a more targeted manner, and the curriculum recommendation effect is effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular, to a multi-dimensional course recommendation method, device, equipment and storage medium. Background Art

[0002] With the development of computer technology, intelligent learning products such as learning machines provide more and more functions. In addition to learning in the classroom, users can also use intelligent learning products for course learning and question practice, effectively improving the learning effect of users.

[0003] Intelligent learning products can store multiple course packages, and different course packages can be applicable to different users, and targeted learning guidance can be provided to users based on different course packages. At present, the recommendation of course packages is generally based on the user's course selection. The recommendation of course packages does not consider the actual situation of users, the course recommendation lacks pertinence, and the course recommendation effect is poor. Summary of the Invention

[0004] The embodiments of the present application provide a multi-dimensional course recommendation method, device, equipment and storage medium to solve the technical problem that the course recommendation in the related technology lacks pertinence and the course recommendation effect is poor. The recommendation of course packages fully considers the actual situation of users, recommends courses to users more pertinently, and can effectively improve the course recommendation effect.

[0005] In a first aspect, the embodiments of the present application provide a multi-dimensional course recommendation method, including:

[0006] Determine the current learning stage and the learning level of the target user;

[0007] According to the current learning stage and the learning level, and the stage attributes and level attributes of multiple preset course packages, determine a target course package from the multiple preset course packages;

[0008] Recommend courses to the target user according to the target course package.

[0009] Further, the learning stage includes semester stage and vacation stage, and the learning level includes one or more combinations of primary learning level, intermediate learning level and advanced learning level.

[0010] Further, before determining the current learning stage and the learning level of the target user, it further includes:

[0011] Collect the learning data of the target user, where the learning data includes user classroom learning data and / or user practice learning data;

[0012] Calibrate the current learning level of the target user according to the learning data;

[0013] Accordingly, determining the current learning stage and the learning level of the target user includes:

[0014] Determining the current learning stage and the calibrated learning level of the target user.

[0015] Further, before calibrating the current learning level of the target user according to the learning data, it further includes:

[0016] Determining the initial user learning level of the target user according to the initial level setting operation of the target user; and / or

[0017] Conducting a learning level test on the target user, and determining the initial user learning level of the target user according to the learning level test result;

[0018] Accordingly, calibrating the current learning level of the target user according to the learning data includes:

[0019] Calibrating the initial user learning level according to the learning data.

[0020] Further, determining the current learning stage includes:

[0021] Obtaining the current time, school semester arrangement, and holiday time arrangement;

[0022] Determining the current learning stage according to the current time, the school semester arrangement, and the holiday time arrangement.

[0023] Further, before determining the target course package from multiple preset course packages according to the current learning stage, the learning level, the stage attributes and level attributes of multiple preset course packages, it further includes:

[0024] Determining the applicable learning stage and applicable learning level of multiple preset course packages, and labeling the stage attributes and level attributes of multiple preset course packages according to the applicable learning stage and the applicable learning level.

[0025] Further, recommending courses to the target user according to the target course package includes:

[0026] When the stage attribute of the target course package is the holiday stage, recommending courses to the target user according to the target course package based on one or more of the recommendation logics of synchronous courses, consolidating the course foundation, course preview, and course review;

[0027] When the stage attribute of the target course package is the semester stage, based on the recommendation logic focusing on high-frequency special knowledge points, course recommendations are made for the target user according to the target course package;

[0028] When the level attribute of the target course package is the advanced learning level, based on the recommendation logic of recommending extended courses and associated extended question banks, course recommendations are made for the target user according to the target course package.

[0029] In a second aspect, an embodiment of the present application provides a multi-dimensional course recommendation device, including an information acquisition module, a course matching module, and a course recommendation module, where:

[0030] The information acquisition module is configured to determine the current learning stage and the learning level of the target user;

[0031] The course matching module is configured to determine a target course package from multiple preset course packages according to the current learning stage and the learning level, and the stage attribute and the level attribute of the multiple preset course packages;

[0032] The course recommendation module is configured to make course recommendations for the target user according to the target course package.

[0033] In a third aspect, an embodiment of the present application provides a multi-dimensional course recommendation device, including: a memory and one or more processors;

[0034] The memory is used to store one or more programs;

[0035] When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-dimensional course recommendation method as described in the first aspect.

[0036] In a fourth aspect, an embodiment of the present application provides a storage medium storing computer-executable instructions, and the computer-executable instructions are used to execute the multi-dimensional course recommendation method as described in the first aspect when executed by a computer processor.

[0037] By determining the current learning stage and the learning level of the target user, an embodiment of the present application determines a target course package from multiple preset course packages according to the current learning stage and the learning level, and the stage attribute and the level attribute of the multiple preset course packages, and makes course recommendations for the target user according to the target course package, reducing the differences between the learning objectives, the learning stage, and the learning level. The recommendation of the course package fully considers the actual situation of the user. The recommendation of the target course package based on the learning stage and the learning level can effectively reduce the inaccuracy in a single dimension, and can recommend courses to users more specifically, effectively improving the course recommendation effect. Description of the Drawings

[0038] Figure 1 It is a flowchart of a multi-dimensional course recommendation method provided by an embodiment of the present application;

[0039] Figure 2 It is a flowchart of another multi-dimensional course recommendation method provided by an embodiment of the present application;

[0040] Figure 3 It is a schematic diagram of a learning level calibration processing flow provided by an embodiment of the present application;

[0041] Figure 4 It is a schematic diagram of the structure of a multi-dimensional course recommendation device provided by an embodiment of the present application;

[0042] Figure 5 It is a schematic diagram of the structure of a multi-dimensional course recommendation device provided by an embodiment of the present application. Detailed implementation manners

[0043] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the specific embodiments of the present application in detail with reference to the drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the convenience of description, only parts related to the present application are shown in the drawings rather than all content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the above process can be terminated, but there can also be additional steps not included in the drawings. The above process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0044] Figure 1 A flowchart of a multi-dimensional course recommendation method provided by an embodiment of the present application is given. The multi-dimensional course recommendation method provided by the embodiment of the present application can be executed by a multi-dimensional course recommendation device, and the multi-dimensional course recommendation device can be implemented in a hardware and / or software manner and integrated in a multi-dimensional course recommendation device.

[0045] The following describes by taking the multi-dimensional course recommendation device executing the multi-dimensional course recommendation method as an example. Refer to Figure 1 , the multi-dimensional course recommendation method includes:

[0046] S110: Determine the current learning stage and the learning level of the target user.

[0047] Exemplarily, when it is necessary to recommend courses to a target user (such as formulating a student learning plan using an artificial intelligence (AI) tutoring function), the current learning stage and the learning level of the target user can be determined. Among them, the current learning stage can be understood as the learning stage at the current date. The learning stage includes the semester stage and the vacation stage. The learning level includes one or a combination of the primary learning level, the intermediate learning level, and the advanced learning level. The semester stage can be understood as the stage when students have classes. The semester stage can include the autumn semester stage (such as the first semester of an academic year) and the spring semester stage (such as the second semester of an academic year). The vacation stage can include the winter vacation, the summer vacation, and holidays, etc. The semester stage and the vacation stage can be configured according to the needs of the learning arrangement. The learning level of the target user can be understood as the level of the target user's current learning ability. Different learning levels can be represented by scores or grades. For example, when representing the learning level by grades, the corresponding learning levels can be low level, intermediate level, high level, etc., or can also be divided into D+, D-, C+, C-, B+, B-, A+, A-, etc. Through the combination of different learning stages and learning levels in this application, different times and user levels can be flexibly adapted, and more suitable courses can be recommended to users in a multi-dimensional target course package, improving the accuracy of course recommendation.

[0048] In one embodiment, determining the current learning stage can be determining the current date and learning stage arrangement information (such as semester, vacation arrangement information). The learning stage arrangement information can record the time corresponding to different learning stages (including start time and end time), and the current learning stage can be determined according to the learning stage corresponding to the current date in the learning stage arrangement information.

[0049] Optionally, different learning stage arrangement information can also be determined for different regions and schools. According to the positioning information obtained by the multi-dimensional course recommendation device or the positioning information selected by the user, the corresponding learning stage arrangement information can be determined. It can also be determined according to the learning stage arrangement information selection operation of the user or the custom configuration operation according to the learning stage arrangement information of the user, and then the current learning stage can be determined according to the current date and the learning stage arrangement information.

[0050] In one embodiment, the learning level of the target user can be set by the user himself / herself or determined according to the learning level test results of the user terminal. Among them, the learning level can reflect the high or low learning ability of the user. The same learning stage can correspond to different learning levels. For example, for the same learning content, users with a high learning level learn and understand knowledge points faster than users with a low learning level.

[0051] Optionally, the learning situation of the user can be collected in real time, and the learning level of the user can be updated in real time according to the learning situation of the user. For example, when the user first starts learning, the learning ability is poor, and the corresponding learning level is the low-level learning level. After a period of learning, the learning ability increases, and the answering level of the user for course-related questions and the correct rate of exercise answers also gradually increase. The learning level of this user will be updated to the intermediate learning level after monitoring the learning situation for a period of time.

[0052] S120: Determine a target course package from multiple preset course packages according to the current learning stage and learning level, as well as the stage attributes and level attributes of multiple preset course packages.

[0053] In one embodiment, the present application provides multiple preset course packages. The preset course package may include course content, exercises, etc. corresponding to one or more subjects. Different preset course packages may be applicable to different learning stages and learning levels, or may be applicable to the same learning stage and learning level. The stage attributes and level attributes of the preset course package can be configured according to the learning stage and learning level applicable to the preset course package. Optionally, the stage attributes and level attributes of the preset course package can be marked according to the content in the preset course package and in combination with the opinions of educational experts (recommending the learning stage and learning level applicable to the preset course package). The multiple preset course packages can be stored in a multi-dimensional course recommendation device or in a preset server.

[0054] Exemplarily, determine the stage attributes and level attributes of multiple preset course packages, and determine one or more target course packages from multiple preset course packages according to the matching situation between the determined current learning stage and learning level and the stage attributes and level attributes of multiple preset course packages. For example, if the current learning stage is the autumn semester stage and the learning level of the target user is the primary learning level, the preset course package with the determined stage attributes and level attributes of the autumn semester stage and the primary learning level can be used as the target course package.

[0055] In one embodiment, the stage attributes and level attributes of multiple preset course packages can be obtained from the server. After determining the target course package, download the target course package from the server to reduce the occupation of memory space by the preset course package.

[0056] S130: Recommend courses to the target user according to the target course package.

[0057] Exemplarily, after determining the target course package, course recommendations can be made for the target user based on a preset course recommendation strategy and / or a course recommendation strategy configured for the target course package, for example, arranging daily learning tasks, practice tasks, etc. based on the course content and exercises in the target course package. In one embodiment, making course recommendations for the target user according to the target course package can be based on a preset recommendation strategy or through a trained course recommendation model.

[0058] As described above, by determining the current learning stage and the learning level of the target user, and according to the current learning stage and learning level, as well as the stage attributes and level attributes of multiple preset course packages, the target course package is determined from multiple preset course packages, and course recommendations are made for the target user according to the target course package, reducing the differences between the learning objectives and the learning stage and learning level. The recommendation of the course package fully considers the actual situation of the user. The recommendation of the target course package based on the learning stage and learning level can effectively reduce the inaccuracy in a single dimension, and can recommend courses to the user more pertinently, effectively improving the course recommendation effect.

[0059] Based on the above embodiments, Figure 2 The flowchart of another multi-dimensional course recommendation method provided by the embodiment of the present application is given. This multi-dimensional course recommendation method is a concretization of the above multi-dimensional course recommendation method. Refer to Figure 2 and this multi-dimensional course recommendation method includes:

[0060] S210: Determine the applicable learning stages and applicable learning levels of multiple preset course packages, and label the stage attributes and level attributes of multiple preset course packages according to the applicable learning stages and applicable learning levels.

[0061] Exemplarily, obtain multiple preset course packages, determine the applicable learning stages and applicable learning levels of each preset course package, and label the stage attributes and level attributes of the preset course packages according to the applicable learning stages and applicable learning levels.

[0062] Correspondingly, after determining the current learning stage and the learning level of the target user, the corresponding stage attributes and level attributes can be matched in multiple preset course packages according to the current learning stage and learning level, and one or more preset course packages that are matched and hit are determined as the target course package. By labeling the stage attributes and level attributes according to the applicable learning stages and applicable learning levels of the preset course packages, the present application ensures the accurate determination of the target course package and accurately improves the course recommendation effect.

[0063] S220: Determine the current learning stage and the learning level of the target user.

[0064] In a possible embodiment, the multi-dimensional course recommendation method provided by the present application for determining the current learning stage may include: obtaining the current time, school semester arrangement, and holiday time arrangement; and determining the current learning stage according to the current time, school semester arrangement, and holiday time arrangement.

[0065] Exemplarily, when it is necessary to recommend courses to a target user, the current time, school semester arrangement, and holiday time arrangement may be determined. Among them, different regions and / or schools may correspond to different school semester arrangements and holiday time arrangements. The region and / or school corresponding to the target user may be determined, and the school semester arrangement and holiday time arrangement may be determined according to the region and / or school corresponding to the target user.

[0066] In an embodiment, the school semester arrangement provided by the present application may include the school semester start time and the school semester end time. According to the school semester start time and the school semester end time, the time range corresponding to the semester stage of the semester may be determined. The holiday time arrangement may include the holiday start time and the holiday end time. According to the holiday start time and the holiday end time, the time range corresponding to the holiday stage of the holiday may be determined.

[0067] Furthermore, it is determined whether the current time is within the semester stage time range or within the holiday stage time range. If the current time is within the semester stage time range, the current learning stage may be determined as the semester stage. If the current time is within the holiday stage time range, the current learning stage may be determined as the holiday stage. The present application accurately determines the current learning stage according to the current time, school semester arrangement, and holiday time arrangement, ensuring the accurate determination of the target course package.

[0068] In a possible embodiment, as Figure 3 shown in the schematic diagram of a learning level calibration processing flow provided, before determining the current learning stage and the learning level of the target user, the multi-dimensional course recommendation method provided by the present application further includes:

[0069] S201: Collect the learning data of the target user, where the learning data includes user classroom learning data and / or user practice learning data.

[0070] S202: Perform a calibration process on the current learning level of the target user according to the learning data.

[0071] The multi-dimensional course recommendation device provided by this application can calibrate and update the learning level of the target user according to the collection of multi-source data (learning data) of the target user. When it is necessary to recommend courses to the target user, the course recommendation can be made according to the latest calibrated learning level. Exemplarily, the learning data of the target user using the multi-dimensional course recommendation device is collected. The learning data provided by this application includes user classroom learning data and / or user practice learning data. Among them, the user classroom learning data can be understood as the data of the target user using the multi-dimensional course recommendation device to learn classroom content (such as viewing classroom-related texts, pictures, courseware, videos, etc.), and the user practice learning data is the data of the target user using the multi-dimensional course recommendation device to do exercise questions (such as the number of exercise questions, scores, correct rates, etc.).

[0072] In one embodiment, according to a preset time interval and a preset data volume interval of learning data, the current learning level (initial user learning level or the learning level obtained from the previous calibration) of the target user is calibrated based on the collected learning data to obtain the latest calibrated learning level. Correspondingly, when determining the current learning stage and the learning level of the target user, it can be to determine the current learning stage and the learning level of the target user after calibration processing. By calibrating the current learning level of the target user according to the learning data of the target user, this application ensures that the course recommendation conforms to the latest learning level of the target user and improves the accuracy of the course recommendation.

[0073] In a possible embodiment, the initial user learning level of the target user can be set by the target user or determined according to the learning level test of the target user. Based on this, before calibrating the current learning level of the target user according to the learning data, the multi-dimensional course recommendation method provided by this application further includes: determining the initial user learning level of the target user according to the initial level setting operation of the target user; and / or conducting a learning level test on the target user and determining the initial user learning level of the target user according to the learning level test result.

[0074] Exemplarily, when the target user sets the initial user learning level, the target user can initiate an initial level setting operation on the multi-dimensional course recommendation device. For example, an initial level setting interface is provided on the multi-dimensional course recommendation device, and different learning levels are provided on the initial level setting interface for the user to select. The target user can select the corresponding learning level on the initial level setting interface according to his own learning level to trigger the initial level setting operation. After the multi-dimensional course recommendation device detects the initial level setting operation of the target user, it can use the learning level corresponding to the initial level setting operation as the initial user learning level of the target user.

[0075] In one embodiment, when determining the initial user learning level based on the learning level test of the target user, the multi-dimensional course recommendation device may conduct a learning level test on the target user based on one or more learning level test questions and corresponding question answers preset or downloaded from the server. For example, the multi-dimensional course recommendation device may display the learning level test questions on the learning level test answering interface, and the target user may answer the learning level test questions on the learning level test answering interface. After the target user finishes answering the learning level test questions, the multi-dimensional course recommendation device may score the target user's answering answers based on the corresponding question answers of the learning level test questions, and determine the learning level test result according to the scoring result (such as the score and correct rate of the user's answers). Optionally, corresponding learning levels may be preset for different learning level test results. After determining the learning level test result of the target user, the learning level corresponding to the learning level test result may be used as the initial user learning level of the target user.

[0076] Correspondingly, the multi-dimensional course recommendation method provided by this application calibrates the current learning level of the target user according to the learning data. It can be when the initial user learning level has not been calibrated, calibrate the initial user learning level according to the learning data, and when the initial user learning level has been calibrated, calibrate the learning level after the previous calibration according to the learning data. This application determines the initial user learning level of the target user by setting operations based on the initial level of the target user and / or the learning level test result of the target user, ensuring that the course recommendation conforms to the initial learning level of the target user, improving the accuracy of the course recommendation. And in the subsequent learning level calibration, the learning level of the target user is closer to the true learning ability of the target user, effectively improving the accuracy of the course recommendation.

[0077] S230: Determine the target course package from multiple preset course packages according to the current learning stage, learning level, and the stage attributes and level attributes of multiple preset course packages.

[0078] S240: Recommend courses to the target user according to the target course package.

[0079] In a possible embodiment, the multi-dimensional course recommendation method provided by this application recommends courses to the target user according to the target course package, which may include:

[0080] S241: When the stage attribute of the target course package is the holiday stage, recommend courses to the target user according to the target course package based on one or more recommendation logics among synchronous courses, consolidating the course foundation, course preview, and course review.

[0081] S242: When the stage attribute of the target course package is the semester stage, based on the recommendation logic focusing on high-frequency special knowledge points, course recommendations are made for the target user according to the target course package.

[0082] S243: When the level attribute of the target course package is the advanced learning level, based on the recommendation logic of recommending extended courses and associated extended question banks, course recommendations are made for the target user according to the target course package.

[0083] In one embodiment, different recommendation logics can be configured for different stage attributes and level attributes, and course recommendations can be made for users based on the determined recommendation logic and the target course package. Optionally, making course recommendations for the target user according to the recommendation logic and the target course package can be performed through a trained course recommendation model.

[0084] Exemplarily, when the stage attribute of the target course package is the holiday stage, course recommendations can be made for the target user according to the target course package based on one or more of the recommendation logics of synchronous courses (such as being synchronized with the school's teaching progress), consolidating the course foundation (such as strengthening the basic content of the learning course), course preview, and course review.

[0085] When the stage attribute of the target course package is the semester stage, based on the recommendation logic focusing on high-frequency special knowledge points (such as increasing the learning frequency and duration of high-frequency special knowledge points), course recommendations are made for the target user according to the target course package.

[0086] When the level attribute of the target course package is the advanced learning level, based on the recommendation logic of recommending extended courses (such as recommending competition extended courses like Olympiad math classes and New Concept English) and associated extended question banks, course recommendations are made for the target user according to the target course package. Through making targeted course recommendations for the target user according to the stage attribute and level attribute of the target course package, it is possible to more specifically guide the target user's learning and effectively improve the course recommendation effect.

[0087] As described above, by determining the current learning stage and the learning level of the target user, according to the current learning stage and learning level, as well as the stage attributes and level attributes of multiple preset course packages, the target course package is determined from multiple preset course packages, and course recommendations are made for the target user according to the target course package, reducing the differences between the learning goals, learning stage, and learning level. The recommendation of the course package fully considers the actual situation of the user. The recommendation of the target course package based on the learning stage and learning level can effectively reduce the inaccuracy in a single dimension, can more specifically recommend courses to the user, and effectively improve the course recommendation effect. And by calibrating the current learning level of the target user according to the learning data of the target user, it is ensured that the course recommendation conforms to the latest learning level of the target user and improves the accuracy of the course recommendation.

[0088] Figure 4 The structural schematic diagram of a multi - dimensional course recommendation device provided by an embodiment of the present application is given. Refer to Figure 4 , the multi - dimensional course recommendation device includes an information acquisition module 41, a course matching module 42, and a course recommendation module 43.

[0089] Among them, the information acquisition module 41 is used to determine the current learning stage and the learning level of the target user; the course matching module 42 is used to determine the target course package from multiple preset course packages according to the current learning stage and learning level, as well as the stage attributes and level attributes of multiple preset course packages; the course recommendation module 43 is configured to recommend courses to the target user according to the target course package.

[0090] Above, by determining the current learning stage and the learning level of the target user, determining the target course package from multiple preset course packages according to the current learning stage and learning level, as well as the stage attributes and level attributes of multiple preset course packages, and recommending courses to the target user according to the target course package, the difference between the learning goal and the learning stage and learning level is reduced. The recommendation of the course package fully considers the actual situation of the user. The recommendation of the target course package based on the learning stage and learning level can effectively reduce the inaccuracy in a single dimension, and can recommend courses to the user more pertinently, effectively improving the course recommendation effect.

[0091] In a possible embodiment, the learning stage includes a semester stage and a vacation stage, and the learning level includes one or a combination of a primary learning level, an intermediate learning level, and an advanced learning level.

[0092] In a possible embodiment, the multi - dimensional course recommendation device further includes a level calibration module, and the level calibration module is used for:

[0093] Collecting the learning data of the target user, where the learning data includes the user's in - class learning data and / or the user's practice learning data;

[0094] Performing calibration processing on the current learning level of the target user according to the learning data;

[0095] Correspondingly, the information acquisition module 41 determines the current learning stage and the learning level of the target user, including:

[0096] Determining the current learning stage and the calibrated learning level of the target user.

[0097] In a possible embodiment, the multi - dimensional course recommendation device further includes a level setting module, and the level setting module is used for:

[0098] Determining the initial user learning level of the target user according to the initial level setting operation of the target user; and / or

[0099] Conduct a learning level test on the target user, and determine the initial learning level of the target user according to the results of the learning level test;

[0100] Correspondingly, the level calibration module calibrates the current learning level of the target user according to the learning data, including:

[0101] Calibrate the initial learning level of the user according to the learning data.

[0102] In a possible embodiment, the information acquisition module 41 determines the current learning stage, including:

[0103] Obtain the current time, school semester arrangement, and holiday time arrangement;

[0104] Determine the current learning stage according to the current time, school semester arrangement, and holiday time arrangement.

[0105] In a possible embodiment, the multi-dimensional course recommendation device further includes a course package annotation module, and the course package annotation module is used for:

[0106] Determine the applicable learning stages and applicable learning levels of multiple preset course packages, and annotate the stage attributes and level attributes of the multiple preset course packages according to the applicable learning stages and applicable learning levels.

[0107] In a possible embodiment, the course recommendation module 43 recommends courses to the target user according to the target course package, including:

[0108] When the stage attribute of the target course package is the holiday stage, based on one or more of the recommendation logics of synchronous courses, consolidating the course foundation, course preview, and course review, recommend courses to the target user according to the target course package;

[0109] When the stage attribute of the target course package is the semester stage, based on the recommendation logic of focusing on high-frequency special knowledge points, recommend courses to the target user according to the target course package;

[0110] When the level attribute of the target course package is the advanced learning level, based on the recommendation logic of recommending extended courses and associated extended question banks, recommend courses to the target user according to the target course package.

[0111] It should be noted that in the embodiments of the above multi-dimensional course recommendation device, the various units and modules included are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the embodiments of the present application.

[0112] An embodiment of the present application also provides a multi-dimensional course recommendation device, which can integrate the multi-dimensional course recommendation device provided in the embodiment of the present application. Figure 5 It is a schematic structural diagram of a multi-dimensional course recommendation device provided in an embodiment of the present application. Refer to Figure 5 , the multi-dimensional course recommendation device includes: an input device 53, an output device 54, a memory 52, and one or more processors 51; the memory 52 is used to store one or more programs; when the one or more programs are executed by the one or more processors 51, the one or more processors 51 implement the multi-dimensional course recommendation method provided in the above embodiment. Among them, the input device 53, the output device 54, the memory 52, and the processor 51 can be connected through a bus or other means, Figure 5 Taking the connection through the bus as an example.

[0113] The memory 52, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the multi-dimensional course recommendation method provided in any embodiment of the present application (for example, the information acquisition module 41, the course matching module 42, and the course recommendation module 43 in the multi-dimensional course recommendation device). The memory 52 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the device. In addition, the memory 52 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 52 can further include a memory remotely set relative to the processor 51, and these remote memories can be connected to the device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0114] The input device 53 can be used to receive input digital or character information, and generate key signal inputs related to the user settings and function control of the device. The output device 54 can include a display device such as a display screen.

[0115] The processor 51 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 52, that is, implements the above multi-dimensional course recommendation method.

[0116] The above-provided multi-dimensional course recommendation device, device, and computer can be used to execute the multi-dimensional course recommendation method provided in any of the above embodiments, and have corresponding functions and beneficial effects.

[0117] The embodiments of the present application also provide a storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a computer processor, they are used to execute the multi-dimensional course recommendation method provided in the above embodiments. The multi-dimensional course recommendation method includes: determining the current learning stage and the learning level of the target user; determining a target course package from multiple preset course packages according to the current learning stage, the learning level, the stage attributes and the level attributes of the multiple preset course packages; and recommending courses to the target user according to the target course package.

[0118] Storage medium - Any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media such as CD-ROMs, floppy disks or tape drives; computer system memories or random access memories such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memories such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. Additionally, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system that is connected to the first computer system via a network (such as the Internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (such as in different computer systems connected via a network). The storage medium may store program instructions executable by one or more processors (such as embodied as a computer program).

[0119] Of course, for the storage medium storing computer-executable instructions provided in the embodiments of the present application, the computer-executable instructions are not limited to the multi-dimensional course recommendation method provided above, and may also execute related operations in the multi-dimensional course recommendation method provided in any embodiment of the present application.

[0120] The multi-dimensional course recommendation device, equipment and storage medium provided in the above embodiments can execute the multi-dimensional course recommendation method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, reference may be made to the multi-dimensional course recommendation method provided in any embodiment of the present application.

[0121] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments provided here. Various obvious changes, re-adjustments, and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it may also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.

Claims

1. A multi-dimensional course recommendation method, characterized in that Including: Determine the current learning stage and the learning level of the target user; According to the current learning stage and the learning level, as well as the stage attributes and level attributes of multiple preset course packages, determine a target course package from the multiple preset course packages; Recommend courses to the target user according to the target course package.

2. The multi-dimensional course recommendation method according to claim 1, wherein The learning stage includes the semester stage and the vacation stage, and the learning level includes one or a combination of multiple of the primary learning level, the intermediate learning level, and the advanced learning level.

3. The multi-dimensional course recommendation method according to claim 1, characterized in that Before determining the current learning stage and the learning level of the target user, it further includes: Collect the learning data of the target user, where the learning data includes user classroom learning data and / or user practice learning data; Calibrate the current learning level of the target user according to the learning data; Correspondingly, determining the current learning stage and the learning level of the target user includes: Determine the current learning stage and the calibrated learning level of the target user.

4. The multi-dimensional course recommendation method according to claim 3, wherein Before calibrating the current learning level of the target user according to the learning data, it further includes: Determine the initial user learning level of the target user according to the initial level setting operation of the target user; and / or Conduct a learning level test on the target user, and determine the initial user learning level of the target user according to the learning level test result; Correspondingly, calibrating the current learning level of the target user according to the learning data includes: Calibrate the initial user learning level according to the learning data.

5. The multi-dimensional course recommendation method according to claim 1, wherein Determining the current learning stage includes: Obtain the current time, the school semester arrangement, and the vacation time arrangement; Determine the current learning stage according to the current time, the school semester arrangement, and the vacation time arrangement.

6. The multi-dimensional course recommendation method according to claim 1, wherein Before determining a target course package from multiple preset course packages according to the current learning stage, the learning level, and the stage attributes and level attributes of multiple preset course packages, it further includes: Determine the applicable learning stage and applicable learning level of multiple preset course packages, and label the stage attributes and level attributes of multiple preset course packages according to the applicable learning stage and applicable learning level.

7. The multi-dimensional course recommendation method according to claim 1, wherein Recommending courses to the target user according to the target course package includes: When the stage attribute of the target course package is the vacation stage, based on one or more recommendation logics such as synchronous courses, consolidating the course foundation, course preview, and course review, recommend courses to the target user according to the target course package; When the stage attribute of the target course package is the semester stage, based on the recommendation logic of focusing on high-frequency special knowledge points, recommend courses to the target user according to the target course package; When the level attribute of the target course package is the advanced learning level, based on the recommendation logic of recommending extended courses and associated extended question banks, recommend courses to the target user according to the target course package.

8. A multi-dimensional course recommendation device, characterized in that, Including an information acquisition module, a course matching module, and a course recommendation module, where: The information acquisition module is configured to determine the current learning stage and the learning level of the target user; The course matching module is configured to determine a target course package from multiple preset course packages according to the current learning stage and the learning level, as well as the stage attributes and level attributes of the multiple preset course packages; The course recommendation module is configured to recommend courses to the target user according to the target course package.

9. A multi-dimensional course recommendation device, characterized in that, Comprising: A memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-dimensional course recommendation method according to any one of claims 1-7.

10. A storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the multi-dimensional course recommendation method according to any one of claims 1-7 when executed by a computer processor.

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