Detailed Description
The method aims at solving the problem that learning courses cannot be pushed in a targeted manner in a video training conference. The application provides a course recommendation method, which comprises the steps of obtaining historical parameter data of a user, wherein the historical parameter data comprises an active time length, an interaction time, subjective scores after meeting and a meeting starting time period when the user participates in each meeting, determining an interesting content label of the user according to the historical parameter data and preset content labels of the meeting, wherein the interesting content label is a preset content label corresponding to the maximum value obtained by scoring the preset content label according to the historical parameter data, and pushing a target course matched with the interesting content label to the user from a current training meeting. The target course can be determined by combining the historical parameter data of the user, so that the targeted course recommendation is realized.
In order that the above-described aspects may be better understood, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a hardware running environment according to an embodiment of the present invention.
It should be noted that fig. 1 may be a schematic structural diagram of a hardware operating environment of the intelligent terminal.
As shown in fig. 1, the intelligent terminal may include a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the configuration of the intelligent terminal shown in fig. 1 is not limiting of the intelligent terminal, and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a course recommendation program may be included in the memory 1005 as one type of storage medium. The operating system is a program for managing and controlling intelligent terminal hardware and software resources, a course recommendation program and other software or program running.
In the intelligent terminal shown in fig. 1, the user interface 1003 is mainly used for connecting the terminal and performing data communication with the terminal, the network interface 1004 is mainly used for a background server and performing data communication with the background server, and the processor 1001 may be used for calling a course recommendation program stored in the memory 1005.
In this embodiment, the intelligent terminal comprises a memory 1005, a processor 1001 and a course recommendation program stored on the memory and executable on the processor, wherein:
when the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are performed:
Acquiring historical participation data of a user, wherein the historical participation data comprises an active time length, interaction times, subjective scores after meeting and a meeting starting time period when participating in each meeting;
Determining an interesting content label of the user according to the historical parameter data and preset content labels of the conferences, wherein the interesting content label is a preset content label corresponding to the maximum value obtained by scoring the preset content label according to the historical parameter data;
pushing a target course matching the content tag of interest from a current training conference to the user.
When the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are also performed:
Determining all users pushing the target course;
Recommending the virtual rooms of the target courses to all users so that the users can enter the virtual rooms through the shortcut entries of the target courses.
When the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are also performed:
Determining a virtual room with highest current activity and real-time hot spot questions in the virtual room;
Pushing the real-time hot topic to a conference initiating terminal of the current training conference so that the conference initiating terminal can determine adjustment suggestions of conference contents of the current training conference based on the real-time hot topic;
or pushing the real-time hot topic to a receiving conference terminal of the current training conference, and establishing a dialog box between users at the receiving conference terminal so that the receiving conference terminal can conduct discussion of the real-time hot topic based on the dialog box.
When the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are also performed:
optimizing the score of the corresponding historical parameter data by adopting the weight value of each historical parameter data to obtain the target score of each historical parameter data;
Determining the score of a preset content label of the conference associated with each historical parameter data according to the weighted value of the target score of each historical parameter data;
And determining the preset content label corresponding to the maximum value in the score as the interested content label of the user.
When the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are also performed:
Generating a judgment matrix according to the scores of the historical parameter data;
And performing root method operation by adopting the judgment matrix to obtain weight values corresponding to the historical parameter data.
When the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are also performed:
Determining the maximum characteristic root value in the characteristic vector of each historical parameter data;
determining a consistency index and a random consistency index based on the maximum feature root value and the historical parameter data;
determining a consistency ratio according to the ratio of the consistency index to the random consistency index;
And when the consistency ratio is smaller than a preset value, judging that the weight values are consistent, and executing the step of optimizing the corresponding scores of the historical reference data by adopting the weight values of the historical reference data to obtain target scores of the historical reference data.
When the processor 1001 calls the course recommendation program stored in the memory 1005, the following operations are also performed:
Acquiring the active time length, the interaction times and subjective scores after meeting when the user participates in each meeting, and the meeting starting time period;
and determining historical participation data of the user according to the active duration, the interaction times, the subjective scores after the conference and the average value of the conference starting time period.
The technical solution of the present application will be described below by way of examples.
As shown in fig. 2, in a first embodiment of the present application, the course recommendation method of the present application includes the steps of:
Step S110, historical participation data of the user is obtained, wherein the historical participation data comprises the active time length, the interaction times, the subjective scores after the conference and the conference starting time period when the user participates in each conference.
In this embodiment, the history participation data is information when the user participates in the history training conference. The historical participation data at least comprises conference identifications of each historical training conference participated by the user, active time span of the user in each historical training conference, interaction Frequency of the user in each historical training conference, subjective score of the user after each historical training conference is finished, conference starting time period of the user in each historical training conference, and the like. Historical meeting data for each historical training meeting attended by a user attending a current training meeting may be obtained. The historical participant data may be bound to the user account number and the meeting identification. The historical participation data can be uploaded to the cloud for storage after the user participation is finished, or stored in a local database after the user participation is finished. Historical participant data for the user may be obtained from a cloud or local database based on the user identification.
Step S120, determining an interesting content tag of the user according to the historical participation data and preset content tags of the attended conference, where the interesting content tag is a preset content tag corresponding to a maximum value obtained by scoring the preset content tag according to the historical participation data.
In this embodiment, each historical training conference has a corresponding preset content tag, and the preset content tag is determined according to the conference subject content of the historical training conference. And establishing a preset content Tag system through the conference subject content, so that each training conference has a corresponding preset content Tag. And establishing an association relationship between a preset content label corresponding to the historical training conference of the user and historical parameter data of the historical training conference. For example TagN [ Span, frequency, score, time ].
In this embodiment, after obtaining the history participation data of the history training conference in which the user participates, the content tag of interest of the user is determined according to the history participation data and the preset content tag of the conference in which the user participates. Specifically, the scoring values of the preset content tags of all conferences attended by the user are calculated according to the historical parameter data, the preset content tag corresponding to the maximum scoring value is obtained from the scoring values of the preset content tags of all conferences, and the preset content tag corresponding to the maximum scoring value is determined to be the interested content tag of the user.
Step S130, pushing the target course matched with the interesting content label from the current training conference to the user.
In this embodiment, after obtaining the content tag of interest of the user, a target course associated with the content tag of interest is acquired. And pushing the target course associated with the content tag of interest to the user in the current training session. Therefore, the effect of recommending the corresponding target course according to the characteristics of each user in the current training conference is achieved.
Specifically, after the interesting content tag of the user is determined, when a neutral situation occurs in the video training conference in the sharing process, or when a training conference is detected to be suspended, or when the current broadband is detected to be reduced, and the like, the video conference training recommendation module acquires a target course matched with the interesting content tag of the user from the cloud. Specifically, the video conference training recommendation module obtains a target course matched with the interesting content label of the user from the cloud end, so as to push the target course to the user. Referring to fig. 4, in the conference in which different users participate, the preset content labels are different, so that the purpose of pushing the target course according to the user characteristics is achieved.
In this embodiment, referring to fig. 4, after pushing the target course, a recommended course sub-screen may be displayed on the current meeting display interface. If the user is interested in the target course, the target course playing button is clicked, and the secondary screen is unfolded on the main interface of the current video conference to play the target course, so that the user can conduct selective learning training under the condition that the current video conference is not affected. How the user refuses to click the target course play button, the secondary screen is hidden.
In the above technical solution of the present embodiment, because the technical solution of determining the content label of interest of the user according to the historical parameter data of the user and the preset content label of the meeting attended by the user is adopted, and then the target course associated with the content label of interest of the user is pushed in the current training meeting, the target course can be determined by combining the characteristics of different users, so as to realize targeted course recommendation.
In one embodiment, the step of obtaining historical parameter data of the user specifically includes the steps of:
Step S111, acquiring the active time length, the interaction times, the subjective scores after the meeting and the meeting starting time period when the user participates in each meeting;
Step S112, determining historical participant data of the user according to the active duration, the interaction times, the subjective scores after the conference and the average value of the conference starting time period.
In this embodiment, the active time of the user corresponding to the historical training conference, the interaction times of the user, the post-conference score of the user, and the conference start time associated with the user may be stored in the cloud server or the local server after the end of the historical training conference. In the current training conference, when the current training conference is in a clamping state, a pause state and the like, the active time, the interaction times, the post-meeting scores and the conference starting time corresponding to the historical training conference related to the account number of the user can be obtained from the cloud server or the local server so as to further determine the historical participation data.
In this embodiment, since the same conference subject matter may exist in all the historic training conferences attended by the same user, that is, the same preset content label exists. Therefore, after the active time of the user corresponding to each historical training conference, the interaction times of the user, the post-meeting scores of the user and the conference starting time associated with the user are obtained, the historical training conferences with the same preset content label are classified. And further determining historical participation data according to the classified historical training conferences under different preset content labels. For example, all historical training conferences under each category may be averaged to determine the user's historical participant data. For example, the user has engaged in a meeting with preset content tab TagA N times, calculated as TagA: [ Span1+ ] + SpanN/N, frequency1+ ] + FrequencyN/N, core1+ ] + ScoreN/N, time1+ ] + TimeN/N, i.e., tagA: [ SpanAVG, frequencyAVG, scoreAVG, timeAVG ]. The preset content labels of the user are calculated in sequence, wherein the preset content labels are shown as TagX: spanAVG, frequencyAVG, scoreAVG, timeAVG. Thus, for a user, there are a total of X preset content tags, each of which contains different historical reference data Span, frequency, score, time.
According to the technical scheme, the historical meeting data of each historical training meeting attended by the user can be determined in a targeted manner according to the related information of the historical training meeting of the user.
In one embodiment, determining the content tag of interest of the user according to the historical participation data and the preset content tag of the attended meeting specifically includes the following steps:
Step S121, optimizing the score of the corresponding historical parameter data by using the weight value of each historical parameter data to obtain the target score of each historical parameter data;
Step S122, determining the score of the preset content label of the meeting associated with each historical parameter data according to the weighted value of the target score of each historical parameter data;
and step S123, determining the preset content label corresponding to the maximum value in the score as the interested content label of the user.
In this embodiment, after the preset content tag is determined, each historical parameter data under the preset content tag may be scored empirically, that is, the score of each historical parameter data is determined. And because the weight values of the historical parameter data are different, after the scores of the historical parameter data are determined, the scores of the corresponding historical parameter data are further optimized by combining the weight values of the historical parameter data, so that the target scores of the historical parameter data are obtained. Each historical reference data has a corresponding weight value, which can be determined according to AHP hierarchy analysis.
In this embodiment, after determining the target score of each historical parameter data, the target scores of each historical parameter data are added to obtain a weighted value, that is, the score corresponding to the preset content tag of the meeting associated with each historical parameter data is determined according to the weighted value of the target score of each historical parameter data.
In this embodiment, after obtaining the scores corresponding to the preset content tags corresponding to the conferences, the preset content tag corresponding to the maximum score is determined as the interested content tag of the user.
According to the technical scheme, the scores of the historical parameter data are optimized by adopting the weight values of the historical parameter data, and then the scores of the preset content labels of the conferences related to the historical parameter data are determined according to the weight values of the target scores obtained after the optimization of the historical parameter data. And determining the preset content label with the maximum score as a technical means of the content label of interest of the user, thereby determining the content label of interest of the user.
In an embodiment, optimizing the score of the corresponding historical parameter data by using the weight value of each historical parameter data, before the step of obtaining the target score of each historical parameter data, includes determining the weight value of each historical parameter data, and specifically includes the following steps:
step S210, generating a judgment matrix according to the score of each historical parameter data;
And step S220, performing a root method operation by adopting the judgment matrix to obtain weight values corresponding to the historical parameter data.
In this embodiment, after determining the preset content tag, each historical parameter data under the preset content tag may be scored empirically, that is, the score of each historical parameter data is determined. Taking Span, frequency, score, time of the four historical reference data of the present application as an example, the following judgment matrix table is finally obtained by scoring the 4 historical reference data (lowest score is 1 and highest score is 5) and combining expert scoring:
| Time | Span | Frequence | Score |
| Time | 1.000 | 0.250 | 0.333 | 0.500 |
| Span | 4.000 | 1.000 | 1.000 | 1.000 |
| Frequence | 3.000 | 1.000 | 1.000 | 1.000 |
| Score | 2.000 | 1.000 | 1.000 | 1.000 |
And after the judgment matrix is obtained, performing root method operation according to the scores of the historical parameter data in the judgment matrix to obtain the weight value corresponding to each historical parameter data. Wherein, the root method operation is AHP analytic hierarchy process operation. The AHP analytic hierarchy process is as follows:
As can be seen from the above table, 4-order judgment matrix is constructed for 4 items of total Time, span, frequency and Score, and analysis is carried out to obtain a feature vector (0.452,1.414,1.316,1.189), and the weight values corresponding to the 4 items of Time, span, frequency and Score are 10.336%,32.352%,30.107% and 27.205%, respectively.
In an embodiment, the method of performing root method operation by using the judgment matrix, after obtaining the weight value corresponding to each historical parameter data, that is, after determining the weight value corresponding to each historical parameter data, further needs to check the consistency of the weight values, which specifically includes the following steps:
step S230, determining the maximum characteristic root value in the characteristic vector of each history reference data;
In this embodiment, after determining the weight value of each history reference data, it is also necessary to determine whether the weight value has consistency. Specifically, after the feature vector of each history reference data is determined, the maximum feature root is determined from the feature vector. For example, the maximum feature root determined by the feature vector in the above embodiment is 4.046.
Step S240, determining a consistency index and a random consistency index based on the maximum characteristic root value and the historical parameter data;
In the present embodiment, after the maximum feature root value is determined, the consistency index (i.e., CI) is further calculated from the maximum feature root value. Wherein, CI= (maximum eigenvalue-n)/(n-1), which is used for consistency check of subsequent weight values, n represents the number of historical reference data. The consistency index is 0.015 by the calculation formula of the consistency index.
In this embodiment, a random uniformity index (i.e., RI) may be determined at the same time as the uniformity index is determined. The random consistency index corresponding to the historical reference data can be searched in a preset random consistency table. Wherein, the random consistency table stores the mapping relation between the number of the historical reference data and the random consistency index. After the number of the historical reference data is determined, a corresponding random consistency index can be determined. For example, when the number of the determined historical reference data is 4, the corresponding search result random consistency index is 0.890.
Step S250, determining a consistency ratio according to the ratio of the consistency index to the random consistency index.
In this embodiment, after the consistency index and the random consistency index are obtained, the consistency ratio (i.e., CR) is further determined according to the ratio between the consistency index and the random consistency index. The uniformity index obtained as described above was 0.015, the random uniformity index was 0.890, and the uniformity ratio obtained was 0.017.
Step S260, when the consistency ratio is smaller than a preset value, judging that the weight values are consistent, executing step S121, and optimizing the corresponding scores of the historical reference data by adopting the weight values of the historical reference data to obtain target scores of the historical reference data;
In this embodiment, after the consistency ratio is obtained, the smaller the consistency ratio is, the better the consistency of the judgment matrix is, the smaller the consistency ratio is, the consistency ratio is smaller than the preset value of 0.1, the judgment matrix meets the consistency test, if the consistency ratio is larger than the preset value of 0.1, the judgment matrix does not have consistency, and the judgment matrix is analyzed again after being properly adjusted. The consistency index obtained by the calculation aiming at the 4-order judgment matrix is 0.015, and the table look-up aiming at the random consistency index is 0.890, so that the consistency ratio obtained by the calculation is 0.017<0.1, which means that the judgment matrix meets consistency test and the calculated weight value has consistency. The target score of the historical participant data is determined based on the score of the historical participant data and the weight value of the historical participant data.
In this embodiment, a weight value corresponding to Span, frequency, score, time is determined by an AHP analytic hierarchy process, and for each preset content tag TagX, the score of the corresponding historical reference data may be optimized by using the weight value to obtain a target score of each historical reference data:
Performance=Span*0.3235+Frequency*0.3011+Score*0.2720+Time*0.1034。
According to the technical scheme, consistency verification of the weight values of the historical parameter data is achieved.
In an embodiment, the course recommendation method further includes:
Step S110, acquiring historical participation data of a user, wherein the historical participation data comprises an active time length, interaction times, subjective scores after the conference and a conference starting time period when the user participates in each conference;
step S120, determining an interested content label of the user according to the historical participation data and preset content labels of the participated conference, wherein the interested content label is a preset content label corresponding to the maximum value obtained by scoring the preset content label according to the historical participation data;
step S130, pushing a target course matched with the interesting content label from the current training conference to the user;
step S140, determining all users pushing the target course;
Step S150, recommending the virtual room of the target course to all the users, so that the users can enter the virtual room through the shortcut entry of the target course.
In this embodiment, by recording the current target course conditions recommended by all users, a recommended course List [ CourseA, courseB,.. CourseN ] of the current training conference can be obtained, where CourseA is the target course of user a, and so on, courseN is the target course of user N. And counting and analyzing the users participating in the target course CourseA, and establishing a virtual room RoomA based on the user, wherein the virtual room is a shortcut entry of the target course. The portal may be a display interface of the target course, or may be a control for linking the display interface of the target course. A shortcut entry for the target course may be displayed on a current video conference display interface of the user's terminal device. All users in room RoomA who are currently engaged in target course CourseA get to user list PeersA A1, A2. And so on, create room RoomN, which corresponds to user list PeersN [ N1, N2,..nn ]. Finally, rooms [ RoomA, roomB, ], roomN ] corresponding to the current N target courses may be generated in real-time. And pushing the virtual room information to each receiving conference terminal through program processing. For the user a, the corresponding receiving conference terminal may display room information [ RoomA, roomB,.. RoomN ] of the current participating course, and the user a is interested in the related content of the recommended course CourseB, may click on the room information of RoomB, and enter the chat interactive interface of RoomB, referring to fig. 5 specifically.
In this embodiment, in the RoomB room, the user a may query other users of the current RoomB for information of the course CourseB, and may also consult other information. The topic of the corresponding target course of each user can reflect the label attribute used by the user, and all users in the conference can be subject-divided according to the topic of the target course of the user and the conference topic attended by the user, so that M communication room topics are divided, wherein M < = N, N is the number of users, namely the number of target courses. In the specific division, the similarity of the subject features of each user can be determined by calculating the feature similarity distance, so that the feature similarity distance is divided into the same room with smaller similarity distance.
According to the technical scheme, training courses are pushed according to the user characteristics in the period of the video conference, and virtual rooms are created according to target courses, so that users participating in recommended course learning can choose to enter different rooms for communication interaction.
In an embodiment, the course recommendation method further includes:
Step S110, acquiring historical participation data of a user, wherein the historical participation data comprises an active time length, interaction times, subjective scores after the conference and a conference starting time period when the user participates in each conference;
step S120, determining an interested content label of the user according to the historical participation data and preset content labels of the participated conference, wherein the interested content label is a preset content label corresponding to the maximum value obtained by scoring the preset content label according to the historical participation data;
step S130, pushing a target course matched with the interesting content label from the current training conference to the user;
step S140, determining all users pushing the target course;
step S150, recommending the virtual rooms of the target courses to all users so that the users can enter the virtual rooms through the shortcut entries of the target courses;
Step S160, determining a virtual room with highest current activity and a real-time hot spot question in the virtual room;
Step S170, pushing the real-time hot topic to a conference initiating terminal of the current training conference so that the conference initiating terminal can determine adjustment suggestions of conference contents of the current training conference based on the real-time hot topic;
or step S180, pushing the real-time hot topic to a receiving conference terminal of the current training conference, and establishing a dialog box between users at the receiving conference terminal, so that the receiving conference terminal can perform discussion of the real-time hot topic based on the dialog box.
In this embodiment, referring to fig. 6, indexes such as the number of people, the speaking amount, the room duration and the like of each recommended room are monitored in real time, and the virtual room of the target course with the highest current activity, such as the activity ActivationA of the room RoomA, is calculated, so that an activity List [ ActivationA, activationB, the.. ActivationM ] of all rooms can be obtained, the room RoomActivationMax with the largest activity is selected, and a real-time hot topic can be determined by counting and analyzing the speaking, comments and the subject of the current room in the room. The real-time hot topic is pushed to a current conference initiating conference terminal, which is the terminal where the conference host is located. The conference moderator can choose to moderately adjust the current conference content according to the current real-time hot topic.
Or, the current real-time hot topic can be pushed to a receiving conference terminal of the current training conference, wherein the receiving conference terminal is a terminal where other users except conference owners participate in the current training conference. And establishing a dialogue box for user dialogue on each receiving conference terminal, so that the user can conduct study discussion communication based on the dialogue box, and the learning efficiency is improved maximally.
According to the technical scheme, the virtual room with highest current activity and the real-time hot spot of the current room are obtained through data statistics and analysis, and the real-time hot spot is pushed to the current training conference initiating conference terminal or the receiving conference terminal by the program, so that the learning efficiency of participants is improved, and the learning atmosphere is active.
Embodiments of the present invention provide embodiments of course recommendation methods, it being noted that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in a different order than that illustrated herein.
As shown in fig. 3, the present application provides a course recommendation system, which includes:
The acquiring module 10 is configured to acquire historical meeting data of a user, where the historical meeting data includes an active duration, an interaction number, a subjective score after a meeting, and a meeting start time period when the user participates in each meeting. In an embodiment, the obtaining module 10 is configured to obtain an active duration, an interaction number, a subjective score after a meeting, and a meeting start time period when the user participates in each meeting, and determine historical parameter data of the user according to the active duration, the interaction number, the subjective score after the meeting, and a mean value of the meeting start time period.
The determining module 20 is configured to determine, according to the historical parameter data and preset content tags of the attended meeting, a content tag of interest of the user, where the content tag of interest is a preset content tag corresponding to a maximum value obtained by scoring the preset content tag according to the historical parameter data. In an embodiment, the determining module 20 is configured to optimize the scores of the corresponding historical parameter data by using the weight values of the historical parameter data to obtain the target scores of the historical parameter data, determine the scores of preset content tags of conferences associated with the historical parameter data by using the weight values of the target scores of the historical parameter data, and determine the preset content tag corresponding to the maximum value of the scores as the interesting content tag of the user. In one embodiment, the determining module 20 is configured to generate a judgment matrix according to the scores of the historical parameter data, and perform a root method operation by using the judgment matrix to obtain the weight value corresponding to the historical parameter data. In an embodiment, the determining module 20 is configured to determine a maximum feature root value in a feature vector of each of the historical reference data, determine a consistency index and a random consistency index based on the maximum feature root value and the historical reference data, determine a consistency ratio according to a ratio of the consistency index to the random consistency index, determine that the weight values have consistency when the consistency ratio is smaller than a preset value, and execute the step of optimizing the scores of the corresponding historical reference data by using the weight values of each of the historical reference data to obtain the target scores of each of the historical reference data.
A pushing module 30, configured to push, from a current training conference, a target course matching the content label of interest to the user.
In one embodiment, after the pushing module 30, a recommending module is further connected, and the recommending module is used for determining all users pushing the target course, and recommending the virtual room of the target course to all users for the users to enter the virtual room through the shortcut entry of the target course.
In an embodiment, after the recommending module, a second pushing module is further connected, where the second pushing module is configured to determine a virtual room with highest current activity and a real-time hot topic in the virtual room, push the real-time hot topic to a conference initiating terminal of the current training conference, so that the conference initiating terminal determines an adjustment suggestion of conference content of the current training conference based on the real-time hot topic, or push the real-time hot topic to a conference receiving terminal of the current training conference, and establish a dialog box between users at the conference receiving terminal, so that the conference receiving terminal can perform discussion of the real-time hot topic based on the dialog box.
The specific implementation manner of the course recommendation system is basically the same as that of each embodiment of the course recommendation method, and is not repeated here.
Based on the same inventive concept, the embodiments of the present application further provide a computer readable storage medium, where a course recommendation program is stored, where the course recommendation program, when executed by a processor, implements each step of the course recommendation method described above, and can achieve the same technical effect, and in order to avoid repetition, will not be repeated herein.
Because the storage medium provided by the embodiment of the present application is a storage medium used for implementing the method of the embodiment of the present application, based on the method introduced by the embodiment of the present application, a person skilled in the art can understand the specific structure and the modification of the storage medium, and therefore, the description thereof is omitted herein. All storage media adopted by the method of the embodiment of the application belong to the scope of protection of the application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.