CN109740048B - Course recommendation method and device - Google Patents
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Abstract
The embodiment of the invention discloses a course recommending method and device, relates to the technical field of networks, and can generate recommended courses suitable for students through basic information data, so that the students can recommend the suitable courses to the students in advance when accessing an enterprise online learning platform, and the learning efficiency of the students is improved. The method comprises the following steps: acquiring basic information data; generating at least one first course recommendation list according to the basic information data, wherein each first course recommendation list is generated by one or more items of data in the basic information data; removing courses in at least one first course recommendation list according to a removal strategy to generate at least one second course recommendation list, wherein the first course recommendation list and the second course recommendation list are in one-to-one correspondence; and performing duplication checking and over-threshold processing on at least one second course recommendation list to generate a final course recommendation list. The embodiment of the invention is applied to the network learning system.
Description
Technical Field
The embodiment of the invention relates to the technical field of networks, in particular to a course recommendation method and device.
Background
The enterprise online learning platform is based on the Internet technology, adopts an open online learning platform mode, takes learning resources as a core, meets various training scene requirements of enterprises, constructs an enterprise internal training ecosystem, and helps the enterprises to realize talent lead. With the popularization and deep application of the internet, the enterprise online learning platform becomes an important way for internal education and knowledge sharing. User behavior data is one of the main bases for guiding platform production and operation, and how to perform effective data analysis is a main problem faced by platform operation. The modeling process of the behavior characteristics of the network trainees is to analyze the behavior of the trainees, acquire and maintain the preferences of the trainees and the like, and finally form a model for reflecting the individual needs, knowledge backgrounds or preferences of the trainees. The method comprises the steps of obtaining interesting favor, demand, all interactive behaviors and other data of a student, analyzing, comprehensively summarizing to obtain a computable and formatted student behavior feature model, continuously recording changes of student behaviors, and changing the process of the student behavior feature model along with the changes of the preference of the student.
The enterprise online learning platform is provided with a plurality of levels of administrator users which are respectively responsible for student learning behavior management and platform operation in a certain range, and the enterprise online learning platform needs to know information such as learning frequency, learning progress and learning hot spots of students and recommend proper courses to the proper students. Therefore, demands are made on student portrayal and course recommendation. The student portrait is an effective tool for outlining the appeal and the design direction of target students and contact students, and is widely applied to various fields. For example, the Baidu mobile statistics starts from the requirements of mobile developers, and provides comprehensive analysis visual reports, agile development support and digital management and promotion support in three aspects of insights, learners, product optimization and operation promotion. The mobile statistics can help developers to solve the problems that attributes of students are more and more complex, behaviors of the students are more and more changeable, the product generation period is faster and faster, the popularization cost is higher and higher. However, the enterprise online learning platform is different from the social internet platform, has the characteristics of relatively fixed student scope, relatively centralized course resources and the like, and has specific student portrait and recommendation requirements. At present, research aiming at an enterprise online learning platform is less, and a proper course cannot be recommended to a student.
Disclosure of Invention
The embodiment of the invention provides a course recommending method and device, which can generate recommended courses suitable for students through basic information data, so that the students can recommend the suitable courses to the students in advance when accessing an enterprise online learning platform, and the learning efficiency of the students is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a course recommendation method is provided, the method comprising: acquiring basic information data, wherein the basic information data comprises: the post attribute of the student, the age of the student, the learning course of the student, the time length of each learning course of the student, the release time of the course, the current time and the keywords of the current strategic direction of the enterprise; generating at least one first course recommendation list according to the basic information data, wherein each first course recommendation list is generated by one or more items of data in the basic information data; removing courses in at least one first course recommendation list according to a removal strategy to generate at least one second course recommendation list, wherein the first course recommendation list and the second course recommendation list are in one-to-one correspondence; and performing duplication checking and over-threshold processing on at least one second course recommendation list to generate a final course recommendation list.
In the method, firstly, basic information data such as post attributes of students, ages of the students, learning courses of the students, the time length of each course learned by the students, the release time of the courses, the current time, keywords of the current strategic direction of an enterprise and the like are obtained; generating at least one first course recommendation list according to one or more items of data in the basic information data; then, removing courses in at least one first course recommendation list according to a removal strategy to generate at least one second course recommendation list, wherein the first course recommendation list and the second course recommendation list are in one-to-one correspondence; and performing duplication checking and over-threshold processing on at least one second course recommendation list to generate a final course recommendation list. According to the embodiment of the invention, the recommended course suitable for the student can be generated through the basic information data, so that the suitable course can be recommended to the student in advance when the student accesses the online learning platform of the enterprise, and the learning efficiency of the student is improved.
In a second aspect, there is provided a course recommending apparatus, comprising: an acquisition unit configured to acquire basic information data, the basic information data including: the post attribute of the student, the age of the student, the learning course of the student, the time length of each learning course of the student, the release time of the course, the current time and the keywords of the current strategic direction of the enterprise; the processing unit is used for generating at least one first course recommendation list according to the basic information data acquired by the acquisition unit, and each first course recommendation list is generated by one or more items of data in the basic information data; the processing unit is further used for eliminating the courses in the at least one first course recommendation list according to the elimination strategy and generating at least one second course recommendation list, wherein the first course recommendation list and the second course recommendation list are in one-to-one correspondence; and the processing unit is also used for carrying out duplication checking and super-threshold processing on at least one second course recommendation list to generate a final course recommendation list.
It can be understood that, the course recommending apparatus provided above is used for executing the method corresponding to the first aspect provided above, and therefore, the beneficial effects that can be achieved by the course recommending apparatus can refer to the beneficial effects of the method corresponding to the first aspect above and the corresponding scheme in the following detailed description, which are not described herein again.
In a third aspect, there is provided a course recommending apparatus, which comprises a processor and a memory, wherein the memory is coupled to the processor and stores necessary program instructions and data of the course recommending apparatus, and the processor is configured to execute the program instructions stored in the memory, so that the course recommending apparatus executes the course recommending method according to the first aspect.
In a fourth aspect, there is provided a computer storage medium having stored therein computer program code which, when run on a course recommending apparatus according to the third aspect, causes the course recommending apparatus to perform the method of the first aspect.
In a fifth aspect, there is provided a computer program product storing the above computer software instructions, which, when run on the course recommending apparatus according to the third aspect, causes the course recommending apparatus to execute the program according to the above first aspect.
Drawings
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a flowchart illustrating a course recommendation method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a process of generating a first course recommendation list according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of generating a first course recommendation list according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a process for performing a duplicate checking and super-threshold processing on a course recommendation list according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a course recommending apparatus according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a course recommending apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a course recommending apparatus according to another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
It should be noted that, in the embodiments of the present invention, "of", "corresponding" and "corresponding" may be sometimes used in combination, and it should be noted that, when the difference is not emphasized, the intended meaning is consistent.
For the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", and the like are used for distinguishing the same items or similar items with basically the same functions and actions, and those skilled in the art can understand that the words "first", "second", and the like are not limited in number or execution order.
The learning recommendation of the enterprise online learning platform is beneficial to the students to improve the course selection efficiency and the accurate learning. The enterprise online learning platform is provided with a plurality of levels of administrator users, and is respectively responsible for user learning behavior management and platform operation in a certain range, and needs to know information such as learning frequency, learning progress and learning hot spots of the users and recommend proper courses to the proper users. The user portrait and recommendation method have different emphasis points in different application fields, for example, the user portrait in the marketing field mainly emphasizes the consumption habits of users, and the user portrait mainly emphasizes the viewing preferences of users in the video recommendation field, so that the user portrait and recommendation method are required to be designed according to the characteristics of each application scene. Through retrieval, the prior related technologies mainly focus on the fields of electronic commerce and search, and for example amazon, ali and Baidu etc. propose many implementation methods in the field. The invention discloses a method and a system for recommending course based on student history and real-time learning state parameters, which are less in invention technology disclosed in the field of online education, and a relevant application scene is ' CN106528656A ', the method and the system for recommending course resources based on student history and real-time learning state parameters ', the invention patent provides a course resource recommending method and a system suitable for a network education platform, mass course resources on the network education platform are integrated into a tree-shaped data structure, a user portrait model is formed by comprehensively considering professional knowledge systems and the academic characteristics of mass schools, on the basis, classification mode recognition is realized by using the learning history and the learning state of individual schools, and further, personalized course recommendation is generated from a big-data course resource library according to the classification of students and the relevance of courses. However, the method is not suitable for the enterprise online learning platform. At present, research aiming at an enterprise online learning platform is less, and a proper course cannot be recommended to a student.
Based on the above technical background and the problems existing in the prior art, referring to fig. 1, an embodiment of the present invention provides a course recommendation method, including:
s1, acquiring basic information data, wherein the basic information data comprises: the post attribute of the student, the age of the student, the study course of the student, the time length of each course learned by the student, the release time of the course, the current time and the keywords of the current strategic direction of the enterprise.
And S2, generating at least one first course recommendation list according to the basic information data, wherein each first course recommendation list is generated by one or more items of data in the basic information data.
In one implementation, referring to fig. 2, step S2 specifically includes:
s211, determining the total number of learners of each course in the same post according to the post attributes of the student and the learning courses of the student.
S212, determining the hot courses of the same post according to the total number of learners of each course of the same post, and counting the number of the hot courses of the same post.
And S213, if the number of the hot courses on the same post is larger than a first preset threshold, counting the age of the student of each hot course on the same post according to the hot courses on the same post, the learning courses of the students and the ages of the students.
S214, determining the average age of the students of each hot course in the same post according to the ages of the students of each hot course in the same post.
S215, determining an age deviation value according to the following formula, wherein the age deviation value is the age deviation value of each student of the learning hotspot course on the same post:
Di＝|CYi-Y0|；
wherein D isiIndicates the age deviation value, CYiRepresents the average age of each hot course student (i ═ 1, 2, …, n, n represents the number of hot courses in the same position), Y0Representing the student age of each hot course.
S216, counting hot courses corresponding to the age deviation value smaller than or equal to a second preset threshold value to serve as a first course recommendation list.
In one implementation, referring to fig. 3, step S2 further includes the following steps:
and S221, if the time length of each course learned by the student is greater than the preset learning time length of each course, determining that the student completes the course learning, and counting the number of students completing the course learning and the number of students completing all the course learning.
For example, the preset learning time period of each course may be set to 80% of the total learning time period of each course.
S222, determining the total number of learners of each course according to the learning courses of the student, and determining the hot courses and the number of the hot courses according to the total number of learners of each course.
And S223, if the number of the hot courses is larger than a third preset threshold, counting the number of the learners of each course, the total learning time of all the learners of each course, the number of the learners of all the courses and the total learning time of all the learners of all the courses according to the learning courses of the learners and the learning time of each course of the learners.
And S224, normalizing the number of the students learning in each course and the number of the students finishing learning in all the courses to generate the normalized learning finishing number of each course.
For example, the number of learning completion for a course is equal to the number of students learning for the course/the number of students completing all learning for the course.
And S225, normalizing the number of the learners of each course and the number of the learners of all the courses to generate the normalized number of the learners of each course.
For example, the number of normalized learners for a given lesson is the number of learners for that lesson/the number of learners for all lessons.
S226, normalizing the total learning time length of all the students of each course and the total learning time length of all the students of all the courses to generate a normalized learning time length of each course.
For example, the normalized learning duration of a certain course is the total learning duration of all the students of the course/the total learning duration of all the students of all the courses.
S227, according to the formula TjAcquiring the aging value T of each course 1/(the current time-j, the release time of the course +1)jWherein j is 1, 2, …, m is the number of all courses.
S228, the number of learning completion of the normalization for each course, the number of learning people of each course, the learning time length of each course and the aging value T of each coursejAnd performing weighting processing to generate a weighted value of each course.
For example, a weighted value α × a learning completion number of the lesson + β × a learning population of the lesson + γ × a learning time length of the lesson + η × an aging value of the lesson; wherein, alpha + beta + gamma + eta is 1, 0 is less than or equal to alpha, beta, gamma, eta is less than or equal to 1.
In one implementation, after step S228, the method further includes: s2281, arranging the weighted values of all courses from large to small, taking the weighted value of the top t positions to generate a weighted value list, and generating a first course recommendation list according to the courses corresponding to the weighted values in the weighted value list.
In one implementation, after step S228, the method further includes: s2282, removing the courses corresponding to the weighted values of the courses according to keywords of the current strategic direction of the enterprise, generating the weighted values of the strategic courses, sequencing the weighted values of the strategic courses, and selecting the courses corresponding to the weighted values of the first k strategic courses to generate a first course recommendation list.
In one implementation, the generating at least one first course recommendation list according to the basic information data may further include the following steps:
s231, according to formula TjAcquiring the aging value T of each course 1/(the current time-j, the release time of the course +1)jWherein j is 1, 2, …, m is the number of all courses.
S232, aging value T for all coursesjSorting from big to small, and selecting the first p aging values TjAnd generating the first course recommendation list by the corresponding course.
S3, removing the courses in the at least one first course recommendation list according to the removal strategy, and generating at least one second course recommendation list, wherein the first course recommendation list and the second course recommendation list are in one-to-one correspondence.
Wherein, the exclusion strategy specifically comprises: and excluding the courses with the similarity proportion to the courses already learned by the student exceeding a first preset proportion from at least one first course recommendation list.
And S4, carrying out duplication checking and super-threshold processing on at least one second course recommendation list to generate a final course recommendation list.
Referring to step S4, referring to fig. 4, the specific implementation is as follows:
and S41, generating at least one third course recommendation list for the same courses in each second course recommendation list, and counting the number of the courses in all the third course recommendation lists.
And S42, counting the courses in all the third course recommendation lists to generate a final course recommendation list if the number of the courses in all the third course recommendation lists does not exceed a fifth preset threshold.
And if the number of the courses in all the third course recommendation lists exceeds a fifth preset threshold, removing the corresponding number of the courses from each third course recommendation list according to a second preset proportion according to the ranking of the courses in each third course recommendation list, and generating at least one third course recommendation list with the corresponding number removed.
And S43, counting all the courses with the corresponding number of the third course recommendation lists removed to generate a final course recommendation list.
In an exemplary scenario, the three first course recommendation lists generated according to steps S1, S211 to S228, S2281, and S2282, and the final course recommendation list generated according to steps S3 and S4 are mainly course recommendations standardized for the student population.
In another exemplary scheme, the four first course lists generated according to steps S1, S211 to S228, S2281, S2282, S231, and S232 are combined with the two first course recommendation lists generated based on the student preference method and the collaborative filtering method in the prior art, and the final course recommendation list generated according to steps S3 and S4 is mainly a course recommendation personalized for students.
It should be noted that, learning recommendation is performed according to the preferences submitted by the student and the personal learning history preferences based on the student preference method, and the first course recommendation list is generated according to the recommendation result. The preference submitted by the student means that the student logs in a learning platform for the first time and submits preference information according to the prompt of an enterprise online learning platform; the personal learning history preference refers to user behavior portrait information formed by the system according to browsing and learning history courses of students on an enterprise online learning platform. And performing learning recommendation according to a system filtering method based on a collaborative filtering method, and generating a first course recommendation list according to a recommendation result. Collaborative filtering refers to recommending information of interest to a student by using the preferences of a group with a certain interest cast and common experience. The collaborative filtering algorithm is applied to the global Internet field in a large number due to the excellent speed and robustness of the collaborative filtering algorithm. The implementation of the prior art is referred based on the concrete implementation modes of the student preference method and the collaborative filtering method, and details are not repeated here.
In the method, firstly, basic information data such as post attributes of students, ages of the students, learning courses of the students, the time length of each course learned by the students, the release time of the courses, the current time, keywords of the current strategic direction of an enterprise and the like are obtained; generating at least one first course recommendation list according to one or more items of data in the basic information data; then, removing courses in at least one first course recommendation list according to a removal strategy to generate at least one second course recommendation list, wherein the first course recommendation list and the second course recommendation list are in one-to-one correspondence; and performing duplication checking and over-threshold processing on at least one second course recommendation list to generate a final course recommendation list. According to the embodiment of the invention, the recommended course suitable for the student can be generated through the basic information data, so that the suitable course can be recommended to the student in advance when the student accesses the online learning platform of the enterprise, and the learning efficiency of the student is improved.
Referring to fig. 5, a course recommending apparatus 50 according to an embodiment of the present invention includes:
an obtaining unit 501, configured to obtain basic information data, where the basic information data includes: the post attribute of the student, the age of the student, the study course of the student, the time length of each course learned by the student, the release time of the course, the current time and the keywords of the current strategic direction of the enterprise.
The processing unit 502 is configured to generate at least one first course recommendation list according to the basic information data acquired by the acquiring unit 501, where each first course recommendation list is generated by one or more items of data in the basic information data.
The processing unit 502 is further configured to exclude courses in at least one first course recommendation list according to an exclusion policy, and generate at least one second course recommendation list, where the first course recommendation list and the second course recommendation list are in one-to-one correspondence.
The processing unit 502 is further configured to perform duplicate checking and super-threshold processing on at least one second course recommendation list, and generate a final course recommendation list.
In an exemplary scheme, the processing unit 502 is specifically configured to determine the total number of learners of each course in the same position according to the position attributes of the trainee and the learning courses of the trainee, which are acquired by the acquiring unit 501.
The processing unit 502 is further configured to determine the hot courses in the same post according to the total number of learners of each course in the same post, and count the number of the hot courses in the same post.
The processing unit 502 is further configured to, when it is determined that the number of the hot courses on the same post is greater than the first preset threshold, count the student age of each hot course on the same post according to the hot courses on the same post, the learning course of the student acquired by the acquiring unit 501, and the student age acquired by the acquiring unit 501.
The processing unit 502 is further configured to determine an average age of the trainees of each hot-spot course in the same position according to the ages of the trainees of each hot-spot course in the same position.
The processing unit 502 is further configured to determine an age deviation value according to the following formula, where the age deviation value is an age deviation value of each student of the same post learning hotspot course:
Di＝|CYi-Y0|；
wherein D isiIndicates the age deviation value, CYiRepresents the average age of each hot course student (i ═ 1, 2, …, n, n represents the number of hot courses in the same position), Y0Representing the student age of each hot course.
The processing unit 502 is further configured to count hot courses corresponding to the age deviation value being less than or equal to a second preset threshold as a first course recommendation list.
In an exemplary scheme, the processing unit 502 is specifically configured to determine that a duration of learning each course by a student is greater than a preset learning duration of each course, determine that the student completes the course learning, and count the number of students completing the learning of each course and the number of students completing all the courses.
The processing unit 502 is further configured to determine a total learner rank of each course according to the learner's learning course, and determine a hot course and a number of the hot courses according to the total learner rank of each course.
The processing unit 502 is further configured to determine that the number of the hot courses is greater than a third preset threshold, and then count the number of learning people of each course, the total learning time of all the students of each course, the number of learning people of all the courses, and the total learning time of all the students of all the courses according to the learning courses of the students and the learning time of each course acquired by the acquiring unit 501.
The processing unit 502 is further configured to perform normalization processing on the number of students learning in each course and the number of students completing learning in all courses to generate a normalized learning completion number for each course.
The processing unit 502 is further configured to perform normalization processing on the number of learners of each course and the number of learners of all the courses to generate a normalized number of learners of each course.
The processing unit 502 is further configured to perform normalization processing on the total learning time lengths of all the students of each course and the total learning time lengths of all the students of all the courses to generate a normalized learning time length of each course.
A processing unit 502, further configured to obtain a formula Tj1/(issue of current time-j course)Time +1) obtaining the aging value T of each coursejWhere j is 1, 2, …, and m is the number of all courses.
The processing unit 502 is further configured to determine the number of learning completion units for each course, the number of learning units for each course, the learning duration for each course, and the aging value T for each coursejAnd performing weighting processing to generate a weighted value of each course.
In an exemplary scenario, the processing unit 502 is further configured to arrange the weighted value of each course from large to small, and take the weighted value t-top to generate a weighted value list, and generate the first course recommendation list according to the course corresponding to the weighted value in the weighted value list.
In an exemplary scenario, the processing unit 502 is further configured to exclude the course corresponding to the weighted value of each course according to the keywords of the current strategic direction of the enterprise, generate a weighted value of the strategic course, sort the weighted values of the strategic courses, and select the course corresponding to the weighted value of the top k strategic courses to generate the first course recommendation list.
In an exemplary scenario, the exclusion policy specifically includes: and excluding the courses with the similarity proportion to the courses already learned by the student exceeding a first preset proportion from at least one first course recommendation list.
In an exemplary scenario, the processing unit 502 is specifically configured to generate at least one third course recommendation list for the same course in each second course recommendation list, and count the number of courses in all the third course recommendation lists.
The processing unit 502 is further configured to determine that the number of the courses in all the third course recommendation lists does not exceed a fifth preset threshold, and count the courses in all the third course recommendation lists to generate a final course recommendation list.
The processing unit 502 is further configured to determine that the number of courses in all the third course recommendation lists exceeds a fifth preset threshold, remove corresponding number of courses from each third course recommendation list according to a second preset proportion according to the ranking of the courses in each third course recommendation list, and generate at least one third course recommendation list from which the corresponding number is removed.
The processing unit 502 is further configured to count all the courses with the corresponding number of the third course recommendation lists removed to generate a final course recommendation list.
Since the course recommending apparatus in the embodiment of the present invention can be applied to implement the method embodiment, the technical effects obtained by the course recommending apparatus can also refer to the method embodiment, and the details of the embodiment of the present invention are not repeated herein.
In the case of an integrated unit, fig. 6 shows a schematic view of a possible structure of the course recommending apparatus 50 according to the above embodiment. The course recommending apparatus 50 includes: a processing module 601, a communication module 602, and a storage module 603. The processing module 601 is used for controlling and managing the actions of the course recommending apparatus 50, for example, the processing module 601 is used for supporting the course recommending apparatus 50 to execute the processes S211 to S228, S2281, S2282, S3 and S4 in fig. 3. The communication module 602 is used to support communication of the course recommender 50 with other entities. The storage module 603 is used for storing program codes and data of the course recommending apparatus 50.
The processing module 601 may be a processor or a controller, and may be, for example, a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an application-specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others. The communication module 602 may be a transceiver, a transceiving circuit or a communication interface, etc. The storage module 603 may be a memory.
When the processing module 601 is a processor as shown in fig. 7, the communication module 602 is a transceiver as shown in fig. 7, and the storage module 603 is a memory as shown in fig. 7, the course recommending apparatus 50 according to the embodiment of the present application can be a course recommending apparatus 50 as described below.
Referring to fig. 7, the course recommending apparatus 50 includes: a processor 701, a transceiver 702, a memory 703 and a bus 704.
The processor 701, the transceiver 702 and the memory 703 are connected to each other through a bus 704; the bus 704 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The processor 701 may be a general-purpose Central Processing Unit (CPU), a microprocessor, an Application-Specific Integrated Circuit (ASIC), or one or more ics for controlling the execution of programs in accordance with the present invention.
The Memory 703 may be a Read-Only Memory (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, optical disk storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these. The memory may be self-contained and coupled to the processor via a bus. The memory may also be integral to the processor.
The memory 703 is used for storing application program codes for executing the present application, and is controlled by the processor 701 to execute. The transceiver 702 is configured to receive content input from an external device, and the processor 701 is configured to execute application codes stored in the memory 703, so as to implement the course recommendation method described in the embodiment of the present application.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the present application are all or partially generated upon loading and execution of computer program instructions on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or can comprise one or more data storage devices, such as a server, a data center, etc., that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The embodiment of the present invention further provides a computer program product, which can be directly loaded into the memory and contains software codes, and the computer program product can be loaded and executed by the computer to implement the course recommendation method.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. A course recommendation method, the method comprising:
acquiring basic information data, wherein the basic information data comprises: the post attribute of the student, the age of the student, the learning course of the student, the time length of each learning course of the student, the release time of the course, the current time and the keywords of the current strategic direction of the enterprise;
generating at least one first course recommendation list according to the basic information data, wherein each first course recommendation list is generated by one or more items of data in the basic information data;
removing courses in the at least one first course recommendation list according to a removal strategy to generate at least one second course recommendation list, wherein the first course recommendation list is in one-to-one correspondence with the second course recommendation list;
performing duplicate checking and super-threshold processing on the at least one second course recommendation list to generate a final course recommendation list;
the generating at least one first course recommendation list according to the basic information data specifically includes:
determining the total number of learners of each course on the same post according to the post attributes of the student and the learning courses of the student;
determining hot courses of the same post according to the total number of learners of each course of the same post, and counting the number of the hot courses of the same post;
if the number of the hot courses on the same post is larger than a first preset threshold value, counting the student age of each hot course on the same post according to the hot courses on the same post, the learning courses of the students and the student ages;
determining the average age of the trainees of each hot course in the same post according to the ages of the trainees of each hot course in the same post;
determining an age bias value according to the following formula, wherein the age bias value is an age bias value of each student of the same position learning hotspot course:
Di＝|CYi-Y0|；
wherein D isiIndicates the age deviation value, CYiRepresenting the average age of each hot course student, i is 1, 2, …, n, n represents the number of hot courses in the same position, Y0Representing the student age of each of the hot courses;
and counting the hot course corresponding to the age deviation value smaller than or equal to a second preset threshold value to be used as the first course recommendation list.
2. The course recommendation method as claimed in claim 1, wherein said generating at least one first course recommendation list according to said basic information data specifically comprises:
if the time length of each course learned by the student is greater than the preset learning time length of each course, determining that the student completes the course learning, and counting the number of students completing the course learning and the number of students completing all the course learning;
determining the total number of learners of each course according to the learning courses of the student, and determining hot courses and the number of the hot courses according to the total number of learners of each course;
if the number of the hot courses is larger than a third preset threshold, counting the number of the learning people of each course, the total learning time of all the students of each course, the number of the learning people of all the courses and the total learning time of all the students of all the courses according to the learning courses of the students and the learning time of each course of the students;
normalizing the number of the students who learn in each course and the number of the students who finish learning in all the courses to generate a normalized learning finishing number of each course;
normalizing the number of the learners of each course and the number of the learners of all the courses to generate a normalized number of the learners of each course;
normalizing the total learning time length of all the students of each course and the total learning time length of all the students of all the courses to generate a normalized learning time length of each course;
according to the formula TjAcquiring the aging value T of each course 1/(the current time-j, the release time of the course +1)jWherein j is 1, 2, …, m, m is the number of all courses;
the number of learning completion of one learning for each course, the number of learning population for each course, the learning duration for each course, and the aging value T for each coursejAnd performing weighting processing to generate a weighted value of each course.
3. The course recommending method according to claim 2, wherein said learning completion number for each course, said learning population for each course, said learning time length for each course, and said aging value T for each coursejPerforming a weighting process to generate a weighting value for each course, and then:
and arranging the weighted value of each course from big to small, generating a weighted value list by taking the weighted value of the top t, and generating the first course recommendation list according to the course corresponding to the weighted value in the weighted value list.
4. The course recommending method according to claim 2, wherein said learning completion number for each course, said learning population for each course, said learning time length for each course, and said aging value T for each coursejPerforming a weighting process to generate a weight for each courseValues, thereafter also including:
and removing the course corresponding to the weighted value of each course according to the keywords of the current strategic direction of the enterprise, generating the weighted value of the strategic course, sequencing the weighted values of the strategic course, and selecting the course corresponding to the weighted value of the strategic course at the top k positions to generate the first course recommendation list.
5. The course recommendation method of claim 1, wherein the exclusion policy specifically comprises: and excluding the courses with the similarity proportion to the courses already learned by the student exceeding a first preset proportion from the at least one first course recommendation list.
6. The course recommendation method of claim 1, wherein said performing a duplicate checking and super-threshold processing on said second course recommendation list to generate a final course recommendation list comprises:
comparing the courses in each second course recommendation list, removing the same courses, generating at least one third course recommendation list, and counting the number of the courses in all the third course recommendation lists;
if the number of the courses in all the third course recommendation lists does not exceed a fifth preset threshold value, counting the courses in all the third course recommendation lists to generate a final course recommendation list;
if the number of the courses in all the third course recommendation lists exceeds a fifth preset threshold, removing the corresponding number of the courses from each third course recommendation list according to a second preset proportion according to the ranking of the courses in each third course recommendation list, and generating at least one third course recommendation list with the corresponding number removed;
and counting all the courses with the corresponding number of the third course recommendation lists removed to generate a final course recommendation list.
7. A course recommending apparatus, comprising:
an acquisition unit configured to acquire basic information data, the basic information data including: the post attribute of the student, the age of the student, the learning course of the student, the time length of each learning course of the student, the release time of the course, the current time and the keywords of the current strategic direction of the enterprise;
the processing unit is used for generating at least one first course recommendation list according to the basic information data acquired by the acquisition unit, and each first course recommendation list is generated by one or more items of data in the basic information data;
the processing unit is further configured to exclude courses in the at least one first course recommendation list according to an exclusion policy, and generate at least one second course recommendation list, where the first course recommendation list and the second course recommendation list are in one-to-one correspondence;
the processing unit is further configured to perform duplicate checking and super-threshold processing on the at least one second course recommendation list to generate a final course recommendation list;
the processing unit is specifically configured to determine a total number of learners of each course on the same post according to the post attributes of the trainee and the learning courses of the trainee, which are acquired by the acquisition unit;
the processing unit is further used for determining the hot courses on the same post according to the total number of learners of each course on the same post, and counting the number of the hot courses on the same post;
the processing unit is further configured to, when it is determined that the number of the hot-spot courses on the same post is greater than a first preset threshold, count the student age of each hot-spot course on the same post according to the hot-spot course on the same post, the learning course of the student acquired by the acquiring unit, and the student age acquired by the acquiring unit;
the processing unit is further used for determining the average age of each hot-spot course student in the same post according to the ages of the students in each hot-spot course in the same post;
the processing unit is further configured to determine an age deviation value according to the following formula, where the age deviation value is an age deviation value of each student of the same-position learning hotspot course:
Di＝|CYi-Y0|；
wherein D isiIndicates the age deviation value, CYiRepresenting the average age of each hot course student, i is 1, 2, …, n, n represents the number of hot courses in the same position, Y0Representing the student age of each of the hot courses;
the processing unit is further configured to count the hot course corresponding to the age deviation value being less than or equal to a second preset threshold value as the first course recommendation list.
8. The course recommending apparatus of claim 7, comprising:
the processing unit is specifically configured to determine that the learning duration of each course of the student is greater than the preset learning duration of each course, determine that the student completes the course learning, and count the number of students who complete the learning of each course and the number of students who complete the learning of all courses;
the processing unit is further used for determining the total number of learners of each course according to the learning courses of the student, and determining the hot courses and the number of the hot courses according to the total number of learners of each course;
the processing unit is further configured to determine that the number of the hot courses is greater than a third preset threshold, and count the number of learning people of each course, the total learning time of all the students of each course, the number of learning people of all the courses, and the total learning time of all the students of all the courses according to the learning courses of the students and the learning time of each course acquired by the acquisition unit;
the processing unit is further configured to perform normalization processing on the number of students learning in each course and the number of students completing learning in all courses to generate a normalized learning completion number of each course;
the processing unit is further used for carrying out normalization processing on the number of the learners of each course and the number of the learners of all the courses to generate a normalized number of the learners of each course;
the processing unit is further configured to perform normalization processing on the total learning duration of all the students in each course and the total learning duration of all the students in all the courses to generate a normalized learning duration of each course;
the processing unit is also used for processing the data according to a formula TjAcquiring the aging value T of each course 1/(the current time-j, the release time of the course +1)jWherein j is 1, 2, …, m is the number of all courses;
the processing unit is further used for counting the number of completed learning in one-to-one mode of each course, the number of learning people in one-to-one mode of each course, the learning duration in one-to-one mode of each course and the aging value T of each coursejAnd performing weighting processing to generate a weighted value of each course.
9. The course recommending apparatus of claim 8, comprising:
the processing unit is further configured to arrange the weighted values of each course from large to small, take the weighted value of t place before ranking to generate a weighted value list, and generate the first course recommendation list according to the course corresponding to the weighted value in the weighted value list.
10. The course recommending apparatus of claim 8, comprising:
the processing unit is further configured to exclude the course corresponding to the weighted value of each course according to the keywords in the current strategic direction of the enterprise, generate a weighted value of strategic courses, sort the weighted values of the strategic courses, and select the first k courses corresponding to the weighted values of the strategic courses to generate the first course recommendation list.
11. The course recommending apparatus of claim 7, wherein said exclusion policy specifically comprises: and excluding the courses with the similarity proportion to the courses already learned by the student exceeding a first preset proportion from the at least one first course recommendation list.
12. The course recommending apparatus of claim 7, comprising:
the processing unit is specifically configured to generate at least one third course recommendation list for the same course in each second course recommendation list, and count the number of courses in all the third course recommendation lists;
the processing unit is further configured to determine that the number of courses in all the third course recommendation lists does not exceed a fifth preset threshold, and count the courses in all the third course recommendation lists to generate a final course recommendation list;
the processing unit is further configured to determine that the number of courses in all the third course recommendation lists exceeds a fifth preset threshold, remove a corresponding number of courses from each third course recommendation list according to a second preset ratio according to the ranking of the courses in each third course recommendation list, and generate at least one third course recommendation list from which the corresponding number is removed;
and the processing unit is also used for counting all the courses with the third course recommendation lists with the corresponding quantity removed to generate a final course recommendation list.
13. A course recommending apparatus, characterized in that said course recommending apparatus comprises a processor and a memory, said memory is used for coupling with said processor and storing the necessary program instructions and data of said course recommending apparatus, said processor is used for executing the program instructions stored in said memory, so that said course recommending apparatus executes the course recommending method according to any one of claims 1-6.
14. A computer storage medium, characterized in that the computer storage medium has stored therein computer program code which, when run on a course recommending apparatus according to claim 13, causes the course recommending apparatus to execute the course recommending method according to any one of claims 1-4.
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| CN113077147B (en) * | 2021-03-31 | 2022-06-24 | 国家电网有限公司 | Intelligent student course pushing method and system and terminal equipment |
| CN113052737A (en) * | 2021-04-30 | 2021-06-29 | 成都市教育家网络科技有限公司 | Learning method and system based on forced supervision and intelligent recommendation |
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