CN117786226A

CN117786226A - Online learning information recommendation method and related device

Info

Publication number: CN117786226A
Application number: CN202311871151.XA
Authority: CN
Inventors: 刘季飞; 吉登高
Original assignee: Shenzhen Yike Technology Co ltd
Current assignee: Shenzhen Yike Technology Co ltd
Priority date: 2023-12-29
Filing date: 2023-12-29
Publication date: 2024-03-29

Abstract

The embodiment of the application provides an online learning information recommendation method and a related device, wherein the method comprises the following steps: acquiring learning posture information of a target learning user for online learning, and acquiring first core information input by the target learning user; determining reference learning material recommendation information corresponding to the target learning user according to the first core information; determining first preference information according to the learning gesture information; determining target learning material recommendation information from the reference learning material recommendation information according to the first preference information; the target learning material recommendation information is displayed, the learning material recommendation information can be automatically determined and displayed according to the core information input by the students and the posture information of the students, and accuracy in the learning material recommendation is improved.

Description

Online learning information recommendation method and related device

Technical Field

The application relates to the technical field of data processing, in particular to an online learning information recommendation method and a related device.

Background

With the continuous improvement of the education environment, the online education mode is accepted by users more and more, and the online education is rapidly developed. However, in the conventional online education, a teacher usually performs online lectures and students perform online learning. When the online learning is performed by adopting the method, students can only receive teaching knowledge taught by teachers, and the capability in the aspect of autonomous learning cannot be timely mobilized, for example, when learning materials are recommended, the students are usually recommended by teachers and the like, and when the students are recommended, the students are usually recommended by students, so that the accuracy is low when the learning materials are recommended to individual students.

Disclosure of Invention

The embodiment of the application provides an online learning information recommendation method and a related device, which can automatically determine learning information recommendation information according to core information input by students and posture information of the students and display the learning information recommendation information, so that accuracy in learning information recommendation is improved.

A first aspect of an embodiment of the present application provides an online learning information recommendation method, which is applied to an online learning robot, and the method includes:

acquiring learning posture information of a target learning user for online learning, and acquiring first core information input by the target learning user;

determining reference learning material recommendation information corresponding to the target learning user according to the first core information;

determining first preference information according to the learning gesture information;

determining target learning material recommendation information from the reference learning material recommendation information according to the first preference information;

and displaying the recommendation information of the target learning materials.

In this example, by acquiring learning gesture information of a target learning user for online learning, and acquiring first core information input by the target learning user, determining reference learning material recommendation information corresponding to the target learning user according to the first core information, determining first preference information according to the learning gesture information, determining target learning material recommendation information from the reference learning material recommendation information according to the first preference information, and displaying the target learning material recommendation information, learning material recommendation information can be automatically determined and displayed according to the core information input by a student and gesture information of the student, and accuracy in learning material recommendation is improved.

In one possible implementation manner, the determining, according to the first core information, reference learning material recommendation information corresponding to the target learning user includes:

determining first sub-reference learning material recommendation information according to the first core information;

determining current learning association information of the target learning user according to the first core information;

determining associated learning material type information according to the current learning associated information;

and determining the reference learning material recommendation information from the first sub-reference learning material recommendation information according to the associated learning material type information.

In one possible implementation manner, the determining the associated learning material type information according to the current learning associated information includes:

extracting keywords from the current learning associated information to obtain a first keyword set;

performing keyword graph construction on first keywords in the first keyword set to obtain a first keyword graph;

determining association information between every two first keywords in the first keyword set according to the first keyword map to obtain a first association information set, and determining group keyword trend information of the first keyword set according to the first keyword map;

Determining target associated information according to the group keyword trend information and the first associated information set;

and determining the associated learning material type information according to the target associated information.

In one possible implementation manner, the determining the group keyword trend information of the first keyword set according to the first keyword graph includes:

extracting core semantic information corresponding to each first keyword from the first keyword map to obtain a first core semantic information set;

determining semantic association keywords between every two first keywords according to the first core semantic information set to obtain a first semantic association keyword set;

acquiring a target semantic association keyword from the first semantic association keyword set, wherein the target semantic association keyword is a first semantic association keyword with the largest occurrence number corresponding to the first semantic association keyword in the first semantic association keyword set;

and determining trend information corresponding to the target semantic association keywords as the group keyword trend information.

In one possible implementation manner, the determining the first preference information according to the learning gesture information includes:

Determining first attention information according to the learning gesture information;

the first preference information is determined from the first attention information.

A second aspect of the embodiments of the present application provides an online learning information recommendation device, which is applied to an online learning robot, and the device includes:

the device comprises an acquisition unit, a first control unit and a second control unit, wherein the acquisition unit is used for acquiring learning gesture information of a target learning user for online learning and acquiring first core information input by the target learning user;

a first determining unit, configured to determine reference learning material recommendation information corresponding to the target learning user according to the first core information;

a second determining unit configured to determine first preference information according to the learning gesture information;

a third determining unit, configured to determine target learning material recommendation information from the reference learning material recommendation information according to the first preference information;

and the display unit is used for displaying the target learning material recommendation information.

In one possible implementation manner, the first determining unit is configured to:

In one possible implementation manner, in the determining the associated learning material type information according to the current learning associated information, the first determining unit is configured to:

In one possible implementation manner, in the determining the group keyword tendency information of the first keyword set according to the first keyword graph, the first determining unit is configured to:

In one possible implementation manner, the determining the first preference information according to the learning gesture information is used by a second determining unit:

A third aspect of the embodiments of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being interconnected, wherein the memory is configured to store a computer program, the computer program comprising program instructions, the processor being configured to invoke the program instructions to execute the step instructions as in the first aspect of the embodiments of the present application.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform part or all of the steps as described in the first aspect of the embodiments of the present application.

A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of an online learning information recommendation method according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a terminal according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an online learning information recommendation device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

In order to better understand the online learning information recommendation method provided in the embodiments of the present application, a scenario in which the online learning information recommendation method is applied will be briefly described below. When the target learning user performs online learning, the online learning is usually performed through a computer, a mobile phone and other devices, wherein the target learning user can be any user needing online learning, such as students, teachers needing training, trained personnel and the like. The online learning robot may be an application program installed on a computer or a mobile phone device, and when a target learning user views information to be understood in detail during online learning, the target learning user may perform a marking process on the information, for example, perform a marking process by using a mouse, so as to input core information to be understood in detail. In the prior art, after the marking is processed, a target learning user searches related learning materials in the internet, and the like, because the target learning user is in a learning stage, the understanding degree of information may be insufficient, and the learning materials needed to be used cannot be well judged, so that the acquired learning material information is insufficient in accuracy, and further learning is influenced. Therefore, in view of the above problems, the present application provides an online learning information recommendation method, which can determine learning information recommendation information adapted to a target learning user by combining core information input by the target learning user and acquired learning gesture information, and display the learning information recommendation information to the target learning user, so that the target learning user can acquire accurate learning information recommendation information, and efficiency in subsequent learning is improved.

Referring to fig. 1, fig. 1 is a flowchart of an online learning information recommendation method according to an embodiment of the present application. As shown in fig. 1, the method is applied to an online learning robot, and the method includes:

101. acquiring learning posture information of a target learning user for online learning, and acquiring first core information input by the target learning user.

The target learning user may be any user who needs online learning, for example, a student, a teacher who needs training, a trained person, or the like.

The learning posture information may include facial expression information, facial orientation information, line-of-sight direction information, sitting posture information, and the like. The learning posture information acquiring method may be that an online learning image of a target learning user is acquired through a camera, and learning posture information is extracted from the online learning image. Specifically, for example, a plurality of continuous online learning images may be acquired, learning gesture information may be extracted from the plurality of continuous online learning images, and then, clustering processing is performed on line-of-sight direction information in the learning gesture information, so as to obtain line-of-sight direction information after clustering, and other learning gesture information may be categorized by using the number of occurrences, where the number of occurrences is the largest, and the obtained learning gesture information is used as corresponding learning gesture information. For example, two different facial expression information appear in a plurality of consecutive images, and the number of images corresponding to each facial expression information is different, so that facial expression information with a high number of images can be determined as facial expression information in learning posture information, and facial orientation information can be obtained in the same manner. In the specific determination of the facial expression, the facial orientation information, the line-of-sight direction information, and the sitting posture information of the target learning user, the facial expression, the facial orientation information, the line-of-sight direction information, the sitting posture information, and the like of the target learning user may be acquired by a general image processing method.

The first core information input by the target learning user can be acquired by the target learning user through the terminal of online learning, and the target learning user can input the first core information through an input device of the terminal, wherein the input device can be a handwriting board, a mouse and the like. The first core information may be, for example, information that the target learning user needs to search for materials when learning, specifically, for example, "xx process" in "basic principle of xx process", and also, for example, "usage method of word xx", and the like.

102. And determining reference learning material recommendation information corresponding to the target learning user according to the first core information.

The sub-reference learning material recommendation information corresponding to the target learning user can be determined according to the first core information, and the reference learning material recommendation information can be determined from the sub-reference learning material recommendation information according to the associated learning material type information determined by the learning associated information corresponding to the first core information.

The sub-reference learning material recommendation information may be a name and a profile of a related learning paper corresponding to the first core information, a name and a profile of a publication, a name and a profile of a book, or the like.

103. And determining first preference information according to the learning gesture information.

The learning posture information may reflect a learning posture of the target learning user corresponding to the current first core information, for example, since the first core information may be recommended to the target learning user by other learning users, the learning posture of the target learning user corresponding to the first core information may be positive or negative, and thus, different learning postures may indicate a preference of the target learning user for the first core information, for example, the positive learning posture may be that the first preference information is biased to learn learning materials corresponding to the first core information, and the negative learning posture may be that the first preference information is bored to learn materials corresponding to the first core information. For different preference information, some learning material recommendation information matched with the target learning user can be selected from the reference learning material recommendation information.

104. And determining target learning material recommendation information from the reference learning material recommendation information according to the first preference information.

The first preference information may be learning materials biased to learn the first core information, and the negative learning attitude may be learning materials bored to learn the first core information. Therefore, for the different first preference information, the method for determining the target learning material recommendation information from the reference learning material recommendation information corresponds to the specific following steps:

If the first preference information is learning data which is inclined to the first core information, the reference learning data recommendation information can be used for determining target learning data recommendation information, and if the first preference information is learning data which is inclined to the first core information, the reference learning data recommendation information which is highest in matching degree with the first core information in the reference learning data recommendation information can be used for determining target learning data recommendation information. The association degree may be a degree of similarity between each learning material in the reference learning material recommendation information and the first core information, and the degree of similarity is determined as a degree of matching.

105. And displaying the recommendation information of the target learning materials.

The target learning material recommendation information may be displayed at a specific position in the display screen of the terminal, for example, the target learning material recommendation information may be displayed in the lower left corner region, or the like.

In one possible implementation manner, the method for determining the reference learning material recommendation information corresponding to the target learning user according to the first core information includes:

a1, determining first sub-reference learning material recommendation information according to the first core information;

a2, determining current learning association information of the target learning user according to the first core information;

a3, determining associated learning material type information according to the current learning associated information;

a4, determining the reference learning material recommendation information from the first sub-reference learning material recommendation information according to the associated learning material type information.

Searching can be performed in the database according to the first core information to obtain first sub-reference learning material recommendation information corresponding to the first core information, wherein the first sub-reference learning material recommendation information can comprise names of a plurality of learning materials and corresponding profile information. Specifically, for example, a first core keyword may be extracted from the first core information, and the database is searched by the first core keyword to obtain first sub-reference learning material recommendation information corresponding to the first core information.

The method for determining the current learning association information according to the first core information may be: and determining information associated with the first core information in the current online learning courseware, and determining the associated information as current learning associated information. For example, a reference keyword corresponding to the first core keyword is determined in the learning courseware of the current online learning, a paragraph corresponding to the reference keyword is determined as the current learning association information, and the reference keyword corresponding to the first core keyword can be understood as the same or similar to the first core keyword in terms of the semantic meaning. After the current learning associated information is obtained, the learning material information type can be determined according to the current learning associated information, for example, the current learning associated information can be subjected to keyword extraction to obtain a keyword set, a keyword map is constructed according to the keyword set, and finally the associated learning material type information is determined according to the keyword map. Therefore, the related learning material type information can be determined through the keyword graph, and accuracy in determining the related learning material type information is improved.

Sub-reference learning material recommendation information belonging to the associated learning material type corresponding to the associated learning material type information in the first sub-reference learning material recommendation information may be determined as reference learning material recommendation information.

In this example, the first sub-reference learning material recommendation information is determined through the first core information, and the reference learning material recommendation information is determined from the first sub-reference learning material recommendation information according to the associated learning material type information determined by the current learning associated information of the target learning user, so that the reference learning material recommendation information can be determined from multiple dimensions, and accuracy in determining the reference learning material recommendation information is improved.

In one possible implementation manner, the method for determining the associated learning material type information according to the current learning associated information includes:

b1, extracting keywords from the current learning associated information to obtain a first keyword set;

b2, constructing a keyword spectrum of a first keyword in the first keyword set to obtain a first keyword spectrum;

b3, determining association information between every two first keywords in the first keyword set according to the first keyword map to obtain a first association information set, and determining group keyword tendency information of the first keyword set according to the first keyword map;

b4, determining target associated information according to the group keyword trend information and the first associated information set;

And B5, determining the associated learning material type information according to the target associated information.

The first keyword may be extracted from the current learning association information by using a general keyword extraction algorithm to obtain a first keyword set.

And constructing a keyword spectrum of the first keywords in the first keyword set to obtain a first keyword spectrum. The construction method comprises the following steps: and taking the first keyword as a node, connecting lines between every two keywords, wherein the length of each connecting line represents the similarity between the two keywords, and the shorter the connecting line is, the lower the similarity is, and the longer the connecting line is. And marking the association information between the keywords on the connection line, wherein the association information can be, for example, how the two keywords are associated, and can be specifically understood as an association point between the two keywords, and the association point can be, for example, semantic association, paraphrase association, formal association and the like.

The first association information set can be obtained by directly extracting association information between every two first keywords from the first keyword graph.

Group keyword trend cluster analysis and the like can be performed on the first keyword graph, so that group keyword trend information of the first keyword set is obtained. Specifically, for example, core semantic information corresponding to each first keyword may be extracted from the first keyword map to perform clustering processing, so as to obtain group keyword trend information.

The matching degree between each piece of first association information and the group keyword trend information is obtained from the first association information set, and the first association information with the highest matching degree is determined to be the target association information. The matching degree between the first association information and the group keyword trend information can be characterized by the similarity between the first association information and the group keyword trend information, wherein the higher the similarity is, the higher the matching degree is, and the lower the similarity is, the lower the matching degree is.

The method for determining the associated learning material type information according to the target associated information can be as follows: and determining the associated learning material type information corresponding to the target associated information through a mapping relation between the preset associated information and the associated learning material type.

In this example, the first keyword graph may be constructed, the first association information set and the group keyword trend information may be obtained according to the first keyword graph, and finally the association learning material type information may be determined according to the first association information set and the group keyword trend information, so that accuracy in determining the association learning material type information may be improved.

In one possible implementation manner, the method for determining group keyword trend information of the first keyword set according to the first keyword graph includes:

C1, extracting core semantic information corresponding to each first keyword from the first keyword map to obtain a first core semantic information set;

c2, determining semantic association keywords between every two first keywords according to the first core semantic information set to obtain a first semantic association keyword set;

c3, acquiring a target semantic association keyword from the first semantic association keyword set, wherein the target semantic association keyword is a first semantic association keyword with the largest occurrence number corresponding to the first semantic association keyword in the first semantic association keyword set;

and C4, determining trend information corresponding to the target semantic association keywords as the group keyword trend information.

The method for extracting the core semantic information of each first keyword may be to use a general semantic extraction algorithm to perform semantic extraction to obtain initial semantic information, and then perform dehiscence processing on the initial semantic information to obtain the core semantic information. The method for carrying out the dehiscence treatment on the initial semantic information can be as follows: and deleting the non-keywords in the initial semantic information, so that the initial semantic information for deleting the non-keywords is determined to be core semantic information.

The semantic association keyword analysis can be performed on the first core semantic information to obtain a first semantic association keyword, which specifically may be: since the first core semantic information includes a plurality of semantic keywords, a first semantic association keyword may be determined from the plurality of semantic keywords, for example, a semantic keyword having an association relationship in the plurality of semantic keywords may be determined as a first semantic association keyword, and if the plurality of semantic keywords do not have the semantic keyword having the association relationship, one semantic keyword may be randomly selected from the plurality of semantic keywords and determined as the first semantic association keyword. By means of random selection, the probability that each semantic keyword is selected is the same, so that the balance of the occurrence of the subsequent whole semantic association keywords can be guaranteed, and the accuracy of finally determining group keyword trend information is improved.

The method comprises the steps that target semantic association keywords can be obtained from a first semantic association keyword set, wherein the target semantic association keywords are first semantic association keywords with the largest occurrence times corresponding to the first semantic association keywords in the first semantic association keyword set. And determining the trend information corresponding to the target semantic association keywords as group keyword trend information, so that final clustering can be realized, thereby determining the group keyword trend information and improving the accuracy of the group keyword trend information determination.

In one possible implementation manner, the method for determining the first preference information according to the learning gesture information includes:

d1, determining first attention information according to the learning gesture information;

d2, determining the first preference information according to the first attention information.

The learning posture information includes facial expression information, facial orientation, and the like, so that the facial expression information, the facial orientation, and the like can be analyzed and processed to obtain first attention information, which specifically can be: performing attention analysis on the facial expression information to obtain first sub-attention information; performing attention analysis on the face orientation to obtain second sub-attention information; first attention information is determined according to the first sub-attention information and the second sub-attention information.

The method for analyzing the attention of the facial expression information to obtain the first sub-attention information may be: the different facial expression information has the corresponding facial expression, and the first sub-attention information can be obtained according to the facial expression, specifically, for example, the facial expression is an attentive expression, the first sub-attention information can be highly concentrated attention, and the highly concentrated attention indicates that the target learning user may have high interest in the first core information; the facial expression is a lazy expression, the first sub-attention information can be distracted, and the distraction indicates that the interest of the target learning user in the first core information is low; the first sub-attention information may be a medium concentration attention, and since the target learning user's facial expression is an excited expression, it is indicated that the target learning user has a moderate interest in the first core information, between a high interest level and a low interest level, and may have knowledge of the content. The facial expression is a comatose expression, and the first sub-attention information can be distracted, so that the target learning user is indicated to have low interest level in the first core information, and the like.

The attention analysis may be performed according to the face orientation information to obtain second sub-attention information, which may specifically be: in this case, the terminal device is taken as a computer to be described as an example, whether there is an intersection between the face orientation and the display plane of the display of the computer can be obtained to determine whether there is an intersection between the face orientation and the display plane, which can be understood as whether there is an overlapping portion between the face orientation and the display plane, and the display plane can be understood as a display interface of the display, specifically, for example, if the face orientation does not intersect with the display interface (the face and the display interface tend to be parallel), the second sub-attention information can be determined to be low-concentration, and if the face orientation overlaps with the display interface partially (the angle at which the face intersects with the display interface is greater than zero degrees and less than ninety degrees), the second sub-attention information can be determined to be medium-concentration, and if the face orientation is completely perpendicular to the display interface (the face orientation is perpendicular to the display interface), the second sub-attention information can be determined to be high-concentration.

The first sub-attention information and the second sub-attention information may be subjected to fusion processing to obtain the first attention information. The method of fusion processing may be that if the attention value corresponding to the first sub-attention information and the second sub-attention information performs weight calculation, an operation result is obtained, and the attention information corresponding to the operation result is determined as the first attention information.

The different attention information corresponds to different preference information, for example, attention is highly focused and moderately focused, the first preference information may be learning materials corresponding to the first core information, and attention is low, and the first preference information may be learning materials corresponding to the first core information, which is tired of learning.

In this example, the first sub-attention information is obtained by performing attention analysis on the facial expression information, and the second sub-attention information is obtained by performing attention analysis on the facial orientation information, so that attention information can be acquired in multiple dimensions, and finally, the first attention information is determined according to the first sub-attention information and the second sub-attention information, and the first preference information is determined according to the first attention information, thereby improving accuracy of the target learning user in acquiring the first preference information.

In accordance with the foregoing embodiments, referring to fig. 2, fig. 2 is a schematic structural diagram of a terminal provided in an embodiment of the present application, as shown in the fig. 2, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, and the memory is configured to store a computer program, where the computer program includes program instructions, and the processor is configured to invoke the program instructions, where the program includes instructions for performing the following steps;

and displaying the recommendation information of the target learning materials.

The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that, in order to achieve the above-mentioned functions, the terminal includes corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied as hardware or a combination of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the application may divide the functional units of the terminal according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.

In accordance with the foregoing, referring to fig. 3, fig. 3 is a schematic structural diagram of an online learning information recommendation device according to an embodiment of the present application. As shown in fig. 3, the apparatus is applied to an online learning robot, and comprises:

an acquiring unit 301, configured to acquire learning gesture information of a target learning user for online learning, and acquire first core information input by the target learning user;

a first determining unit 302, configured to determine reference learning material recommendation information corresponding to the target learning user according to the first core information;

a second determining unit 303 configured to determine first preference information according to the learning gesture information;

A third determining unit 304, configured to determine target learning material recommendation information from the reference learning material recommendation information according to the first preference information;

and the display unit 305 is used for displaying the target learning material recommendation information.

In one possible implementation manner, the first determining unit 302 is configured to:

In one possible implementation manner, in the determining the associated learning material type information according to the current learning associated information, the first determining unit 302 is configured to:

In one possible implementation manner, in the determining the group keyword tendency information of the first keyword set according to the first keyword graph, the first determining unit 302 is configured to:

In a possible implementation manner, the determining the first preference information according to the learning gesture information is used by the second determining unit 303:

The embodiment of the application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any one of the online learning information recommendation methods described in the above method embodiments.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any one of the online learning information recommendation methods described in the method embodiments above.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.

The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units described above may be implemented either in hardware or in software program modules.

The integrated units, if implemented in the form of software program modules, may be stored in a computer-readable memory for sale or use as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory includes: a U-disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-only memory, random access memory, magnetic or optical disk, etc.

The foregoing has outlined rather broadly the more detailed description of embodiments of the present application, wherein specific examples are provided herein to illustrate the principles and embodiments of the present application, the above examples being provided solely to assist in the understanding of the methods of the present application and the core ideas thereof; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims

1. An online learning information recommendation method, which is applied to an online learning robot, the method comprising:

and displaying the recommendation information of the target learning materials.

2. The online learning information recommendation method of claim 1, wherein the determining reference learning material recommendation information corresponding to the target learning user according to the first core information includes:

3. The online learning information recommendation method of claim 2, wherein the determining associated learning material type information based on the current learning associated information comprises:

4. The online learning information recommendation method of claim 3 wherein the determining group keyword trend information for the first keyword set from the first keyword graph comprises:

5. The online learning information recommendation method of any one of claims 1 to 4, wherein the determining first preference information according to the learning gesture information includes:

6. An online learning information recommendation apparatus, which is applied to an online learning robot, the apparatus comprising:

7. The online learning information recommendation apparatus of claim 6, wherein the first determination unit is configured to:

8. The online learning information recommendation apparatus of claim 7, wherein the first determination unit is configured to, in the determining of the associated learning material type information from the current learning associated information:

9. A terminal comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-5.