CN110610404A - Network course recommendation method, device, system, electronic equipment and storage medium - Google Patents

Network course recommendation method, device, system, electronic equipment and storage medium Download PDF

Info

Publication number
CN110610404A
CN110610404A CN201910859769.1A CN201910859769A CN110610404A CN 110610404 A CN110610404 A CN 110610404A CN 201910859769 A CN201910859769 A CN 201910859769A CN 110610404 A CN110610404 A CN 110610404A
Authority
CN
China
Prior art keywords
course
benchmarking
network
user
attributes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201910859769.1A
Other languages
Chinese (zh)
Inventor
李素粉
杨杰
占静媛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China United Network Communications Group Co Ltd
Original Assignee
China United Network Communications Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China United Network Communications Group Co Ltd filed Critical China United Network Communications Group Co Ltd
Priority to CN201910859769.1A priority Critical patent/CN110610404A/en
Publication of CN110610404A publication Critical patent/CN110610404A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0641—Shopping interfaces
    • G06Q30/0643—Graphical representation of items or shoppers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Primary Health Care (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The disclosure provides a network course recommendation method, device, system, electronic equipment and storage medium. The method comprises the following steps: in response to a request for viewing a network course sent by a user through a user terminal, acquiring attributes of the user, acquiring learned history data of each network course from a preset database, determining benchmarking student information according to historical data, determining a candidate set to be recommended according to network courses learned by the benchmarking students, determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student and the candidate set to be recommended, pushing the target recommended course to the user terminal, so that the user terminal displays the target recommended course, determines a candidate set to be recommended according to the benchmarking student information, determining a target recommended course from the set of recommended candidates according to the attributes of the user and the attributes of the benchmarking trainees, the network course matching with the user requirement can be accurately determined, the network course can be recommended to the user based on the user requirement, and the technical effect of improving the recommendation success rate is achieved.

Description

Network course recommendation method, device, system, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a method, an apparatus, a system, an electronic device, and a storage medium for recommending network courses.
Background
With the development and popularization of internet technology, the recommendation of network information becomes a focus of attention of people, such as recommendation of network courses.
In the prior art, massive resources of network courses on a network platform are integrated, a user is portrayed according to historical data of the user relative to the network courses (such as interests and hobbies of the user), and the network courses are pushed to the user according to the portrayal.
In the course of the inventors' realization of the present disclosure, it was found that at least the following problems exist: because the network course is increased at a high speed, different users have different requirements on the network course, and the network course in which the user is interested is not necessarily the network course that the user needs to learn at present, the recommendation method in the prior art reduces the recommendation success rate.
Disclosure of Invention
The disclosure provides a network course recommendation method, device, system, electronic equipment and storage medium, which are used for solving the problem of low recommendation success rate in the prior art.
On one hand, the disclosed embodiment provides a network course recommendation method, which is used for responding to a request sent by a user through a user terminal for watching a network course, acquiring the attribute of the user, and acquiring learned historical data of each network course from a preset database;
determining the information of the benchmark student according to the historical data, wherein the information of the benchmark student comprises the attribute of the benchmark student and the network course learned by the benchmark student, and the learning parameter of the benchmark student is larger than a preset parameter threshold;
determining a candidate set to be recommended according to the network courses learned by the benchmarking trainees;
determining a target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the candidate set to be recommended;
and pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course.
In some embodiments, the learning parameters include a learning number and a learning network course number, the parameter threshold includes a number threshold and a number threshold, and the learning parameters being greater than the preset parameter threshold include:
the learning times are larger than the time threshold value, and the learning network course number is larger than the number threshold value.
In some embodiments, after the determining a candidate set to recommend from the network lesson learned by the benchmarking learner, the method further comprises:
determining the average learning duration and the average learning completion amount of each network course in the candidate set to be recommended;
determining the learning amount according to the average learning duration and the average learning completion amount;
deleting the network courses with the learning amount smaller than a preset learning amount threshold value from the candidate set to be recommended;
and the step of determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student and the candidate set to be recommended comprises the following steps:
and determining the target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the deleted candidate set to be recommended.
In some embodiments, the determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student, and the candidate set to be recommended includes:
determining similarity of the attributes of the user and the attributes of the benchmarking trainees;
selecting the network courses of the benchmarking trainees with the similarity larger than a preset similarity threshold from the candidate set to be recommended;
and determining the selected network course as a target recommended course.
In some embodiments, the attributes of the user include at least one of a gender, a post, a title, an age, and a professional category of the user;
and, the attributes of the benchmarking trainee include at least one of gender, post, job title, age, and professional category of the benchmarking trainee.
On the other hand, the embodiment of the present disclosure further provides an apparatus for recommending network courses, where the apparatus includes:
the acquisition module is used for responding to a request sent by a user through a user terminal for watching the network courses, acquiring the attributes of the user and acquiring learned historical data of each network course from a preset database;
the first determining module is used for determining the benchmarking staff information according to the historical data, wherein the benchmarking staff information comprises the attributes of the benchmarking staff and the network courses learned by the benchmarking staff, and the learning parameters of the benchmarking staff are larger than a preset parameter threshold;
the second determination module is used for determining a candidate set to be recommended according to the network courses learned by the benchmarking student and determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student and the candidate set to be recommended;
and the pushing module is used for pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course.
In some embodiments, the learning parameters include a number of learning and a number of learning network courses, and the parameter thresholds include a number threshold and a number threshold;
and the learning times is larger than the time threshold, and the learning network course number is larger than the number threshold.
In some embodiments, the apparatus further comprises:
the third determining module is used for determining the average learning duration and the average learning completion amount of each network course in the candidate set to be recommended and determining the learning amount according to the average learning duration and the average learning completion amount;
the deleting module is used for deleting the network courses of which the learning amount is smaller than a preset learning amount threshold value from the candidate set to be recommended;
and the second determining module is specifically used for determining the target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the candidate set to be recommended after deletion processing.
In some embodiments, the second determining module is specifically configured to determine a similarity between the attributes of the user and the attributes of the benchmarking trainee; selecting the network courses of the benchmarking trainees with the similarity larger than a preset similarity threshold from the candidate set to be recommended; and determining the selected network course as a target recommended course.
In some embodiments, the attributes of the user include at least one of a gender, a post, a title, an age, and a professional category of the user;
and, the attributes of the benchmarking trainee include at least one of gender, post, job title, age, and professional category of the benchmarking trainee.
The embodiment of the disclosure provides a new network course recommendation method, which comprises the following steps: the method comprises the steps of responding to a request of a user for watching a network course sent by a user terminal, obtaining attributes of the user, obtaining learned historical data of each network course from a preset database, determining benchmarking student information according to the historical data, wherein the benchmarking student information comprises the attributes of benchmarking students and the network courses learned by the benchmarking students, learning parameters of the benchmarking students are larger than a preset parameter threshold value, determining a candidate set to be recommended according to the network courses learned by the benchmarking students, determining a target recommended course according to the attributes of the user, the attributes of the benchmarking students and the candidate set to be recommended, pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course, determining the candidate set to be recommended according to the information of the benchmarking students, determining the target recommended course from the candidate set with the benchmarking according to the attributes of the user and the attributes of the benchmarking students, the network courses matched with the user requirements are accurately determined, the target recommended network courses are pushed to the user terminal, the user can select corresponding network courses from the target recommended courses through the user terminal to watch the corresponding network courses, accordingly, the network courses are recommended to the user based on the user requirements, the recommended network courses are representative, and the technical effect of improving the recommendation success rate is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic diagram of an application scenario of a network course recommendation method according to an embodiment of the present disclosure;
fig. 2 is a schematic view of a display interface of a user terminal according to an embodiment of the disclosure;
FIG. 3 is a flowchart illustrating a method for recommending network courses according to an embodiment of the disclosure;
fig. 4 is a schematic view of a display interface of a user terminal according to another embodiment of the present disclosure;
FIG. 5 is a flowchart illustrating a method for recommending network courses according to another embodiment of the present disclosure;
FIG. 6 is a flowchart illustrating a method for determining a target recommended course according to attributes of a user, attributes of a benchmarking student, and a candidate set to be recommended, according to an embodiment of the present disclosure;
FIG. 7 is a block diagram of a network course recommendation device according to an embodiment of the disclosure;
FIG. 8 is a block diagram of a network course recommending apparatus according to another embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure;
reference numerals: 10. the system comprises a user, 20, a user terminal, 3, a recommendation platform, 1, an acquisition module, 2, a first determination module, 3, a second determination module, 4, a push module, 5, a third determination module, 6 and a deletion module.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The network course recommendation method provided by the embodiment of the disclosure can be applied to the scene shown in fig. 1.
In the application scenario shown in fig. 1, the user 10 may download the APP corresponding to the recommendation platform 30 through the user terminal 20, and after the downloading is completed, an icon of the APP corresponding to the recommendation platform 30 may be displayed on the user terminal 20.
The recommendation platform 30 may be a platform designed by an enterprise based on enterprise culture, or may be a video player such as a favorite video player or a Tencent video player.
In some embodiments, when user 10 has a need to learn a network course, user 10 may trigger a request to view the network course by clicking on an icon of the APP on user terminal 20. The recommendation platform 30 recommends the network lesson to the user terminal 20 upon receiving a request to view the network lesson. The user terminal 20 displays the network courses recommended by the recommendation platform 30, as shown in fig. 2. When the user 10 starts playing the network lesson by clicking the network lesson displayed on the user terminal 20. Of course, the user 10 may also select the network course by voice.
When the user 10 logs in the recommendation platform 30 through the user terminal 20 for the first time, registration may be performed, and corresponding registration information (including attributes in the following text) may be filled in, where the registration information includes, but is not limited to, the age, professional type, gender, department of the enterprise to which the user belongs, a cell phone number, an identification number, and the like of the user 10. The recommendation platform 30 stores the registration information.
In some embodiments, the recommendation platform 30 completes the registration of the user 10 based on the received registration information. In order to distinguish different users, different identifications can be respectively allocated to the different users, and the identifications can be determined according to mobile phone numbers of the different users or identification numbers of the different users.
Moreover, in some embodiments, to facilitate management of network lessons, to save computation and the like during recommendation, the recommendation platform 30 may also assign different identifications to each network lesson.
The following describes the technical solutions of the present disclosure and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present disclosure will be described below with reference to the accompanying drawings.
In one aspect, the embodiment of the present disclosure provides a network course recommendation method suitable for the foregoing scenario.
Referring to fig. 3, fig. 3 is a flowchart illustrating a network course recommendation method according to an embodiment of the disclosure.
As shown in fig. 3, the method includes:
s101: the method comprises the steps of responding to a request sent by a user through a user terminal for watching network courses, obtaining attributes of the user, and obtaining learned historical data of each network course from a preset database.
In some embodiments, a main body performing the network course recommending method of the present disclosure may be a network course recommending device, and the network course recommending device may specifically be a recommending platform (as shown in fig. 1), a server, or the like.
In connection with the application scenario shown in fig. 1, in this step, the user 10 triggers a request to view a network course by clicking on the APP icon of the recommendation platform 30 on the user terminal 20. Of course, the user 10 may also trigger the user terminal 20 to send a request for viewing the network course to the recommendation platform 30 by means of a voice instruction.
The recommendation platform 30 includes a database, in which the attributes of each user (i.e., each student), each network course, and historical data of each network course learned by each user (including the user 10 and other users), such as learning time, learning times, and the like, are stored.
In some embodiments, the user 10 starts the APP of the recommendation platform 30 through the user terminal 20, the recommendation platform 30 may obtain a terminal number of the user terminal 20 (it may be understood that terminal numbers corresponding to different user terminals are different), and determine the attribute of the user 10 corresponding to the user terminal 20 according to the terminal number.
In other embodiments, when the user logs in the recommendation platform 30 for the first time, the user name and password are required to be filled in at the same time when the registration information is filled in, so as to facilitate subsequent login. When the user 10 logs in again, that is, the user 10 starts the APP of the recommendation platform 30 through the user terminal 20, the recommendation platform 30 generates a login instruction, and pushes the login instruction to the user terminal 20, and the user terminal 20 displays a login interface according to the login instruction, as shown in fig. 4. The user 10 inputs a user name and a password on the user terminal 20 and formally starts the recommendation platform 30. The recommendation platform 30 determines whether the user 10 is a legal user (i.e. whether the user name and the password input by the user 10 at this time are the same as those set during registration) according to the user name and the password fed back by the user terminal 20, and if so, the recommendation platform 30 extracts the attribute of the user 10 from the registration information according to the user name of the user 10.
S102: and determining the benchmarking student information according to the historical data, wherein the benchmarking student information comprises the attributes of the benchmarking student and the network courses learned by the benchmarking student, and the learning parameters of the benchmarking student are larger than the preset parameter threshold.
Wherein the parameter threshold can be set based on the requirement.
In this step, specifically, the trainees whose learning parameters are greater than the parameter threshold are determined, the part of the trainees is determined as the benchmarking trainee, the attributes of the benchmarking trainee corresponding to the benchmarking trainee and the network course learned by the benchmarking trainees are determined, and the determined attributes of the benchmarking trainees and the network course learned by the benchmarking trainees are determined as the benchmarking trainee information.
Exemplarily, m trainees are total, wherein the learning parameters of the n trainees are larger than the parameter threshold value, and then the n trainees are determined as the benchmarking trainees. The attributes of each of the n benchmarking students are extracted from the database, and the network courses learned by each benchmarking student are extracted.
In some embodiments, the attributes of the user include at least one of a gender, a post, a title, an age, and a professional category of the user;
and the attributes of the benchmarking trainee include at least one of gender, post, job title, age, and professional category of the benchmarking trainee.
S103: and determining a candidate set to be recommended according to the network courses learned by the benchmarked students.
Based on the above example, in some embodiments, all network courses for n benchmarks may be added to the candidate set to be recommended.
Of course, in other embodiments, all the network courses of the N benchmarking students may also be sorted, and the N network courses which are learned by the N benchmarking students most frequently are selected from the sorted lists and added to the candidate set to be recommended.
Of course, in other embodiments, the learning durations of all networks of the N benchmarking trainees may also be ranked, and the N network courses with the longest learning duration are selected from the ranking and added to the candidate set to be recommended.
S104: and determining a target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the candidate set to be recommended.
In this step, a target recommended course is selected from the candidate set to be recommended according to the attributes of the user and the attributes of the benchmarking trainees.
S105: and pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course.
With reference to the application scenario shown in fig. 1, when the recommendation platform 30 determines a target recommended course, the determined target recommended course is pushed to the user terminal 20, and the user terminal 20 displays the target recommended course, as shown in fig. 2.
It is worth mentioning that the method is particularly suitable for users with a low learning amount.
The embodiment of the disclosure provides a new network course recommendation method, which comprises the following steps: the method comprises the steps of responding to a request of a user for watching a network course sent by a user terminal, obtaining attributes of the user, obtaining learned historical data of each network course from a preset database, determining benchmarking student information according to the historical data, wherein the benchmarking student information comprises the attributes of benchmarking students and the network courses learned by the benchmarking students, learning parameters of the benchmarking students are larger than a preset parameter threshold value, determining a candidate set to be recommended according to the network courses learned by the benchmarking students, determining a target recommended course according to the attributes of the user, the attributes of the benchmarking students and the candidate set to be recommended, pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course, determining the candidate set to be recommended according to the information of the benchmarking students, determining the target recommended course from the candidate set with the benchmarking according to the attributes of the user and the attributes of the benchmarking students, the network courses matched with the user requirements are accurately determined, the target recommended network courses are pushed to the user terminal, the user can select corresponding network courses from the target recommended courses through the user terminal to watch the corresponding network courses, accordingly, the network courses are recommended to the user based on the user requirements, the recommended network courses are representative, and the technical effect of improving the recommendation success rate is achieved.
In some embodiments, the learning parameters include a learning number and a learning network course number, the parameter threshold includes a number threshold and a number threshold, and the learning parameter being greater than the preset parameter threshold includes:
the learning times are larger than the times threshold value, and the learning network course number is larger than the number threshold value.
That is, if the number of times of learning of a student is greater than the number threshold, and the number of learning network courses of the student is greater than the number threshold, the student can be determined as a benchmarking student.
Exemplarily show，LNiNumber of learning for student i, M1 is number threshold, CNiFor the number of learning network courses of student i, M2 is a number threshold. If LNiGreater than M1, and CNiGreater than M2, the trainee i is determined to be a benchmarking trainee.
In some embodiments, based on the history learning record of the user, the corresponding parameter threshold is determined according to the history learning amount of the user, if the history learning amount of the user is low, the user is a student with low learning amount (the corresponding threshold may be set based on the requirement, and the user with the history learning amount lower than the threshold is determined as the student with low learning amount), M1 may be determined as the lower limit value of the time threshold, M11 may be set as the upper limit value of the time threshold, and the learning time of the benchmarking student is between M1 and M11.
In some embodiments, if a student completes a certain percentage of the network course (e.g., P ═ 80%) of the duration of the network course, the student is considered to complete the learning of the network course. Such as, orderWherein, JiThe number of learning network courses for student i (who has learned but not necessarily completed the network courses),wherein LTi，jLearning duration, CT, for student i on network course jjIs the session duration of network session j.
Referring to fig. 5, fig. 5 is a flowchart illustrating a network course recommending method according to another embodiment of the disclosure.
As shown in fig. 5, the method includes:
s201: the method comprises the steps of responding to a request sent by a user through a user terminal for watching network courses, obtaining attributes of the user, and obtaining learned historical data of each network course from a preset database.
For the description of S201, reference may be made to S101, which is not described herein again.
S202: and determining the benchmarking student information according to the historical data, wherein the benchmarking student information comprises the attributes of the benchmarking student and the network courses learned by the benchmarking student, and the learning parameters of the benchmarking student are larger than the preset parameter threshold.
The description of S202 can refer to S102, which is not described herein again.
S203: and determining a candidate set to be recommended according to the network courses learned by the benchmarked students.
The description of S203 may refer to S103, which is not described herein again.
S203': and determining the average learning duration and the average learning completion amount of each network course in the candidate set to be recommended.
S204': and determining the learning amount according to the average learning time length and the average learning completion amount.
S205': and deleting the network courses with the learning amount smaller than a preset learning amount threshold value from the candidate set to be recommended.
Exemplarily, a CTTjFor average learning duration, CTC, of course jjIs the average learning completion amount for course j. Wherein the content of the first and second substances,wherein the content of the first and second substances,wherein, STi，jNetwork course C1 for each benchmarking student pairjThe learning time period of (c).
In some embodiments, a student is considered to complete the network course learning if the student completes a certain proportion of P network course duration (e.g., P ═ 80%).
In order to ensure that the water-soluble organic acid,wherein the content of the first and second substances,wherein LTi，jFor the definition of (1), reference is made to the above examples, which are not described herein again.
Make the study volumeWherein, alpha + beta is 1, 0 is less than or equal to alpha, and beta is less than or equal to 1. According to the learning amount C1jAnd taking the maximum N network courses as target recommended courses.
S204: and determining a target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the deleted candidate set to be recommended.
In this step, a target recommended course is selected from the candidate set to be recommended that has been subjected to deletion processing, based on the attributes of the user and the attributes of the benchmarking trainees.
S205: and pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course.
The description of S205 can refer to S105, and is not repeated here.
As can be seen in conjunction with fig. 6, in some embodiments, S104 includes:
s1041: the similarity of the attributes of the user and the attributes of the benchmarks student is determined.
Based on the above examples, the attributes of the user include, but are not limited to, the user's age, gender, professional type, business, sub-organization of business, and the attributes of the benchmarking agent also include, but are not limited to, the age, gender, professional type, business, sub-organization of business.
Determining similarity is to determine attribute value (or called benchmarking value) AC of user and benchmarking studentjWherein, in the step (A),wherein k is the number of attributes, and if the user and the benchmarking trainee have the same attributes, then Ak1, otherwise, Ak＝0。
S1042: and selecting the network courses of the benchmarking trainees with the similarity greater than a preset similarity threshold from the candidate set to be recommended.
S1043: and determining the selected network course as a target recommended course.
In some embodiments, the first m network courses with the largest learning amount are selected from the selected network courses to be determined as the target recommended course.
According to another aspect of the disclosed embodiment, the disclosed embodiment further provides a network course recommending device.
Referring to fig. 7, fig. 7 is a block diagram of a network course recommendation device according to an embodiment of the disclosure.
As shown in fig. 7, the apparatus includes:
the system comprises an acquisition module 1, a storage module and a learning module, wherein the acquisition module is used for responding to a request sent by a user through a user terminal for watching network courses, acquiring the attributes of the user and acquiring learned historical data of each network course from a preset database;
the first determining module 2 is configured to determine benchmarking staff information according to the historical data, where the benchmarking staff information includes attributes of the benchmarking staff and network courses learned by the benchmarking staff, and learning parameters of the benchmarking staff are greater than a preset parameter threshold;
the second determining module 3 is used for determining a candidate set to be recommended according to the network courses learned by the benchmarking student, and determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student and the candidate set to be recommended;
and the pushing module 4 is configured to push the target recommended course to the user terminal, so that the user terminal displays the target recommended course.
In some embodiments, the learning parameters include a number of learning and a number of learning network courses, and the parameter thresholds include a number threshold and a number threshold;
and the learning times is larger than the time threshold, and the learning network course number is larger than the number threshold.
As can be seen in conjunction with fig. 8, in some embodiments, the apparatus further comprises:
a third determining module 5, configured to determine an average learning duration and an average learning completion amount of each network course in the candidate set to be recommended, and determine a learning amount according to the average learning duration and the average learning completion amount;
a deleting module 6, configured to delete, from the candidate set to be recommended, the network course whose learning amount is smaller than a preset learning amount threshold;
and the second determining module 3 is specifically configured to determine the target recommended course according to the attribute of the user, the attribute of the benchmarking student, and the candidate set to be recommended after the deletion processing.
In some embodiments, the second determining module is specifically configured to determine a similarity between the attributes of the user and the attributes of the benchmarking trainee; selecting the network courses of the benchmarking trainees with the similarity larger than a preset similarity threshold from the candidate set to be recommended; and determining the selected network course as a target recommended course.
In some embodiments, the attributes of the user include at least one of a gender, a post, a title, an age, and a professional category of the user;
and, the attributes of the benchmarking trainee include at least one of gender, post, job title, age, and professional category of the benchmarking trainee.
According to another aspect of the embodiments of the present disclosure, there is also provided a network course pushing system, including: a user terminal and a network course recommending apparatus as described in any of the above embodiments, wherein,
and the user terminal is used for sending a request for watching the network course to the network course recommending device and displaying the target recommended course pushed by the network course recommending device. .
According to another aspect of the embodiments of the present disclosure, there is also provided an electronic device, including: a memory, a processor;
a memory for storing processor-executable instructions;
wherein, when executing the instructions in the memory, the processor is configured to implement the method of any of the embodiments above.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
As shown in fig. 9, the electronic device includes a memory and a processor, and the electronic device may further include a communication interface and a bus, wherein the processor, the communication interface, and the memory are connected by the bus; the processor is used to execute executable modules, such as computer programs, stored in the memory.
The Memory may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Via at least one communication interface, which may be wired or wireless), the communication connection between the network element of the system and at least one other network element may be implemented using the internet, a wide area network, a local network, a metropolitan area network, etc.
The bus may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
The memory is used for storing a program, and the processor executes the program after receiving an execution instruction.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The steps of the method disclosed in connection with the embodiments of the present disclosure may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
According to another aspect of the embodiments of the present disclosure, there is also provided a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor, are configured to implement the method according to any one of the embodiments.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
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 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 apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, 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.
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 embodiments of the present disclosure.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be substantially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should also be understood that, in the embodiments of the present disclosure, 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 on the implementation process of the embodiments of the present disclosure.
While the present disclosure has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (13)

1. A method for web course recommendation, the method comprising:
responding to a request sent by a user through a user terminal for watching network courses, acquiring the attributes of the user, and acquiring learned historical data of each network course from a preset database;
determining the information of the benchmark student according to the historical data, wherein the information of the benchmark student comprises the attribute of the benchmark student and the network course learned by the benchmark student, and the learning parameter of the benchmark student is larger than a preset parameter threshold;
determining a candidate set to be recommended according to the network courses learned by the benchmarking trainees;
determining a target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the candidate set to be recommended;
and pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course.
2. The method as claimed in claim 1, wherein the learning parameters include learning times and learning network course number, the parameter threshold includes a time threshold and a number threshold, and the learning parameters are greater than the preset parameter threshold include:
the learning times are larger than the time threshold value, and the learning network course number is larger than the number threshold value.
3. The method of claim 1, wherein after determining the candidate set to be recommended from the network lessons learned by the benchmarking agent, the method further comprises:
determining the average learning duration and the average learning completion amount of each network course in the candidate set to be recommended;
determining the learning amount according to the average learning duration and the average learning completion amount;
deleting the network courses with the learning amount smaller than a preset learning amount threshold value from the candidate set to be recommended;
and the step of determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student and the candidate set to be recommended comprises the following steps:
and determining the target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the deleted candidate set to be recommended.
4. The method of any of claims 1-3, wherein the determining a target recommended course based on the user's attributes, the benchmarking agent's attributes, and the candidate set to be recommended comprises:
determining similarity of the attributes of the user and the attributes of the benchmarking trainees;
selecting the network courses of the benchmarking trainees with the similarity larger than a preset similarity threshold from the candidate set to be recommended;
and determining the selected network course as a target recommended course.
5. The method according to any one of claims 1 to 3,
the attributes of the user include at least one of a gender, a post, a title, an age, and a professional category of the user;
and, the attributes of the benchmarking trainee include at least one of gender, post, job title, age, and professional category of the benchmarking trainee.
6. An apparatus for web lesson recommendation, the apparatus comprising:
the acquisition module is used for responding to a request sent by a user through a user terminal for watching the network courses, acquiring the attributes of the user and acquiring learned historical data of each network course from a preset database;
the first determining module is used for determining the benchmarking staff information according to the historical data, wherein the benchmarking staff information comprises the attributes of the benchmarking staff and the network courses learned by the benchmarking staff, and the learning parameters of the benchmarking staff are larger than a preset parameter threshold;
the second determination module is used for determining a candidate set to be recommended according to the network courses learned by the benchmarking student and determining a target recommended course according to the attributes of the user, the attributes of the benchmarking student and the candidate set to be recommended;
and the pushing module is used for pushing the target recommended course to the user terminal so that the user terminal can display the target recommended course.
7. The apparatus of claim 6,
the learning parameters comprise learning times and learning network course number, and the parameter threshold comprises a time threshold and a number threshold;
and the learning times is larger than the time threshold, and the learning network course number is larger than the number threshold.
8. The apparatus of claim 6, further comprising:
the third determining module is used for determining the average learning duration and the average learning completion amount of each network course in the candidate set to be recommended and determining the learning amount according to the average learning duration and the average learning completion amount;
the deleting module is used for deleting the network courses of which the learning amount is smaller than a preset learning amount threshold value from the candidate set to be recommended;
and the second determining module is specifically used for determining the target recommended course according to the attributes of the user, the attributes of the benchmarking trainees and the candidate set to be recommended after deletion processing.
9. The apparatus according to any one of claims 6 to 8,
the second determining module is specifically configured to determine similarity between the attribute of the user and the attribute of the benchmarking trainee; selecting the network courses of the benchmarking trainees with the similarity larger than a preset similarity threshold from the candidate set to be recommended; and determining the selected network course as a target recommended course.
10. The apparatus according to any one of claims 6 to 8,
the attributes of the user include at least one of a gender, a post, a title, an age, and a professional category of the user;
and, the attributes of the benchmarking trainee include at least one of gender, post, job title, age, and professional category of the benchmarking trainee.
11. A network course pushing system, said system comprising: a user terminal and a network course recommending apparatus according to any one of claims 6 to 10, wherein,
and the user terminal is used for sending a request for watching the network course to the network course recommending device and displaying the target recommended course pushed by the network course recommending device.
12. An electronic device, comprising: a memory, a processor;
a memory for storing the processor-executable instructions;
wherein the processor, when executing the instructions in the memory, is configured to implement the method of any of claims 1 to 5.
13. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1 to 5.
CN201910859769.1A 2019-09-11 2019-09-11 Network course recommendation method, device, system, electronic equipment and storage medium Pending CN110610404A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910859769.1A CN110610404A (en) 2019-09-11 2019-09-11 Network course recommendation method, device, system, electronic equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910859769.1A CN110610404A (en) 2019-09-11 2019-09-11 Network course recommendation method, device, system, electronic equipment and storage medium

Publications (1)

Publication Number Publication Date
CN110610404A true CN110610404A (en) 2019-12-24

Family

ID=68892880

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910859769.1A Pending CN110610404A (en) 2019-09-11 2019-09-11 Network course recommendation method, device, system, electronic equipment and storage medium

Country Status (1)

Country Link
CN (1) CN110610404A (en)

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112102123A (en) * 2020-08-31 2020-12-18 湖北美和易思教育科技有限公司 Student knowledge plane testing method and device based on data matching
CN112749340A (en) * 2021-01-19 2021-05-04 敖客星云(北京)科技发展有限公司 Course recommendation method and system based on big data, mobile terminal and storage medium
CN113065060A (en) * 2021-02-18 2021-07-02 山东师范大学 Deep learning-based education platform course recommendation method and system
CN113240563A (en) * 2021-05-31 2021-08-10 中国联合网络通信集团有限公司 Course pushing method and device and server
WO2021159832A1 (en) * 2020-02-14 2021-08-19 北京大米科技有限公司 Online interaction control method, device, storage medium, and electronic apparatus
CN113656705A (en) * 2021-08-31 2021-11-16 中国平安财产保险股份有限公司 Course recommendation method, device, equipment and storage medium based on artificial intelligence
CN113689315A (en) * 2021-09-01 2021-11-23 中国联合网络通信集团有限公司 Course pushing method and device and server
CN114461914A (en) * 2022-02-11 2022-05-10 江苏灵狐软件科技有限公司 Professional course pushing method and system based on course platform database
CN114661195A (en) * 2022-04-18 2022-06-24 北京高途云集教育科技有限公司 Method and device for creating network course, computer equipment and storage medium
CN116235165A (en) * 2020-08-14 2023-06-06 西门子股份公司 Method and device for intelligently providing recommendation information
CN116384840A (en) * 2023-05-29 2023-07-04 湖南工商大学 Course recommendation method and related equipment
CN117372219A (en) * 2023-12-05 2024-01-09 青岛理工大学 Student course recommendation method and device and electronic equipment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106021363A (en) * 2016-05-10 2016-10-12 百度在线网络技术（北京）有限公司 Course recommendation method and device
US20180253769A1 (en) * 2015-08-20 2018-09-06 Suli YE Method and apparatus for selecting and recommending presentation objects on electronic distribution platforms
CN109740861A (en) * 2018-12-12 2019-05-10 中国联合网络通信集团有限公司 A kind of learning data analysis method and device
CN109766492A (en) * 2018-12-24 2019-05-17 中国联合网络通信集团有限公司 Learn recommended method, device, equipment and readable medium
CN109933730A (en) * 2019-03-29 2019-06-25 沈阳师范大学 A kind of excellent class of the master of education of middle and primary schools recommends resource application method and system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180253769A1 (en) * 2015-08-20 2018-09-06 Suli YE Method and apparatus for selecting and recommending presentation objects on electronic distribution platforms
CN106021363A (en) * 2016-05-10 2016-10-12 百度在线网络技术（北京）有限公司 Course recommendation method and device
CN109740861A (en) * 2018-12-12 2019-05-10 中国联合网络通信集团有限公司 A kind of learning data analysis method and device
CN109766492A (en) * 2018-12-24 2019-05-17 中国联合网络通信集团有限公司 Learn recommended method, device, equipment and readable medium
CN109933730A (en) * 2019-03-29 2019-06-25 沈阳师范大学 A kind of excellent class of the master of education of middle and primary schools recommends resource application method and system

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021159832A1 (en) * 2020-02-14 2021-08-19 北京大米科技有限公司 Online interaction control method, device, storage medium, and electronic apparatus
CN116235165A (en) * 2020-08-14 2023-06-06 西门子股份公司 Method and device for intelligently providing recommendation information
CN112102123B (en) * 2020-08-31 2022-09-13 武汉美和易思数字科技有限公司 Student knowledge plane testing method and device based on data matching
CN112102123A (en) * 2020-08-31 2020-12-18 湖北美和易思教育科技有限公司 Student knowledge plane testing method and device based on data matching
CN112749340A (en) * 2021-01-19 2021-05-04 敖客星云(北京)科技发展有限公司 Course recommendation method and system based on big data, mobile terminal and storage medium
CN113065060A (en) * 2021-02-18 2021-07-02 山东师范大学 Deep learning-based education platform course recommendation method and system
CN113065060B (en) * 2021-02-18 2022-11-29 山东师范大学 Deep learning-based education platform course recommendation method and system
CN113240563B (en) * 2021-05-31 2023-06-16 中国联合网络通信集团有限公司 Course pushing method, course pushing device and server
CN113240563A (en) * 2021-05-31 2021-08-10 中国联合网络通信集团有限公司 Course pushing method and device and server
CN113656705A (en) * 2021-08-31 2021-11-16 中国平安财产保险股份有限公司 Course recommendation method, device, equipment and storage medium based on artificial intelligence
CN113656705B (en) * 2021-08-31 2024-02-20 中国平安财产保险股份有限公司 Course recommendation method, device, equipment and storage medium based on artificial intelligence
CN113689315A (en) * 2021-09-01 2021-11-23 中国联合网络通信集团有限公司 Course pushing method and device and server
CN113689315B (en) * 2021-09-01 2024-02-13 中国联合网络通信集团有限公司 Course pushing method, course pushing device and server
CN114461914A (en) * 2022-02-11 2022-05-10 江苏灵狐软件科技有限公司 Professional course pushing method and system based on course platform database
CN114661195A (en) * 2022-04-18 2022-06-24 北京高途云集教育科技有限公司 Method and device for creating network course, computer equipment and storage medium
CN116384840A (en) * 2023-05-29 2023-07-04 湖南工商大学 Course recommendation method and related equipment
CN116384840B (en) * 2023-05-29 2023-08-22 湖南工商大学 Course recommendation method and related equipment
CN117372219A (en) * 2023-12-05 2024-01-09 青岛理工大学 Student course recommendation method and device and electronic equipment

Similar Documents

Publication Publication Date Title
CN110610404A (en) Network course recommendation method, device, system, electronic equipment and storage medium
CN109218390B (en) User screening method and device
US20160307131A1 (en) Method, apparatus, and system for controlling delivery task in social networking platform
US9501580B2 (en) Method and apparatus for automated selection of interesting content for presentation to first time visitors of a website
US11372805B2 (en) Method and device for information processing
US20080235375A1 (en) Social networking online community
CN107391108B (en) Notification bar information correction method and device and electronic equipment
US20190317950A1 (en) Interest tag determining method, computer device, and storage medium
CN113240563B (en) Course pushing method, course pushing device and server
US20170169062A1 (en) Method and electronic device for recommending video
CN110569334A (en) method and device for automatically generating comments
US20140278782A1 (en) System and Method for Statistically Determining Bias in Online Survey Results
US20160034705A1 (en) Stochastic privacy
CN110708360A (en) Information processing method and system and electronic equipment
CN108171537B (en) User experience assessment method and device, electronic equipment and storage medium
US11580482B1 (en) Content relevance in a social networking system using population-representative human rater pool
CN107526589A (en) Page display method and device
WO2022001397A1 (en) Interaction qualification level determination method, apparatus, and device, and storage medium
CN113254139A (en) Online education course information display method and device, terminal and storage medium
KR100904691B1 (en) Apparatus and method for providing question and answer services
US20160203724A1 (en) Social Classroom Integration And Content Management
CN110852338A (en) User portrait construction method and device
CN109831417A (en) Method, apparatus, server and the storage medium of anti-harassment processing account number
CN111885139B (en) Content sharing method, device and system, mobile terminal and server
Hajarian et al. Evaluating the impact of different user engagement elements on software applications

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20191224