CN111859140A - Knowledge recommendation method and device, recommendation equipment and readable storage medium - Google Patents
Knowledge recommendation method and device, recommendation equipment and readable storage medium Download PDFInfo
- Publication number
- CN111859140A CN111859140A CN202010735484.XA CN202010735484A CN111859140A CN 111859140 A CN111859140 A CN 111859140A CN 202010735484 A CN202010735484 A CN 202010735484A CN 111859140 A CN111859140 A CN 111859140A
- Authority
- CN
- China
- Prior art keywords
- knowledge
- user
- learning
- point
- chain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application is applicable to the technical field of information processing, and provides a knowledge recommendation method, a knowledge recommendation device, recommendation equipment and a readable storage medium, wherein the method comprises the following steps: acquiring a current learning knowledge point and a target learning knowledge point of a user; inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user according to the sequence; and correcting the learning knowledge chain in real time according to the mastering condition of the user on the current learning knowledge point. Therefore, the embodiment of the application fully considers the degree of the user to absorb the knowledge, and the effect of reasonably recommending the knowledge to the user can be achieved.
Description
Technical Field
The present application belongs to the technical field of information processing, and in particular, to a method and an apparatus for knowledge recommendation, a recommendation device, and a readable storage medium.
Background
With the development of the internet, the information technology is also developed rapidly, and a large amount of data is added globally, such as news data, scientific data, entertainment data, and the like. Wherein, the user can learn according to some useful data, namely knowledge, so as to improve the self ability. However, the existing knowledge recommendation method is to simply screen out knowledge from a large amount of data and recommend the knowledge to a user, and the degree of the knowledge absorption of the user is not considered, so that the knowledge recommendation effect is poor.
Disclosure of Invention
The embodiment of the application provides a knowledge recommendation method, a knowledge recommendation device, a recommendation device and a readable storage medium, and can solve the problems that knowledge recommendation in the prior art does not consider the degree of absorption of a user on knowledge, and the recommendation effect is poor.
In a first aspect, an embodiment of the present application provides a knowledge recommendation method, including:
acquiring a current learning knowledge point and a target learning knowledge point of a user;
inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user according to the sequence;
and correcting the learning knowledge chain in real time according to the mastering condition of the user on the current learning knowledge point.
In a possible implementation manner of the first aspect, the inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user according to a sequence, where before, the method includes:
determining a knowledge domain of the user;
and constructing the preset knowledge graph according to the knowledge points in the knowledge field.
In a possible implementation manner of the first aspect, the constructing the preset knowledge graph according to the knowledge points of the knowledge domain includes:
extracting knowledge points of the knowledge field;
determining the incidence relation between the knowledge points;
and taking the knowledge points as nodes, and generating the preset knowledge graph according to the knowledge points and the incidence relation between the knowledge points.
In a possible implementation manner of the first aspect, the inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the front of the user according to a sequence, further includes:
and setting weight values corresponding to the association relationship between the knowledge points.
In a possible implementation manner of the first aspect, setting a weight value corresponding to an association relationship between the knowledge points includes:
acquiring a first historical learning condition corresponding to the knowledge point;
determining a weight value corresponding to the association relation according to the first historical learning condition;
alternatively, the first and second electrodes may be,
searching for a neighboring user similar to the user;
and determining a weight value corresponding to the association relationship according to a second historical learning condition corresponding to the knowledge point of the neighbor user.
In a possible implementation manner of the first aspect, inputting the current learning knowledge point and the target learning knowledge point to a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user according to a sequence, includes:
searching a first position corresponding to the current learning knowledge point and a second position corresponding to the target learning knowledge point in the preset knowledge map;
according to the weight values corresponding to the incidence relations between the knowledge points, a learning knowledge chain from the current learning knowledge point at the first position to the target learning knowledge point at the second position is planned;
recommending the knowledge points in the learning knowledge chain to the user according to the sequence.
In a possible implementation manner of the first aspect, correcting the learning knowledge chain in real time according to the user's grasp of the current learning knowledge point includes:
generating test questions according to the current learning knowledge points, and sending the test questions to the user;
analyzing according to the answer condition returned by the user to obtain the mastering condition of the user on the current learning knowledge point;
when the mastering condition of a user to the current learning knowledge point does not accord with a preset expectation, searching a neighboring knowledge point of the current learning knowledge point;
and correcting the learning knowledge chain according to the neighbor knowledge points.
In a second aspect, an embodiment of the present application provides a knowledge recommendation apparatus, including:
the acquisition module is used for acquiring a current learning knowledge point and a target learning knowledge point of a user;
the recommendation module is used for inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user and recommending the knowledge points in the learning knowledge chain to the user according to the sequence;
and the correction module is used for correcting the learning knowledge chain in real time according to the mastering condition of the user on the current learning knowledge point.
In one possible implementation manner, the knowledge recommendation apparatus further includes:
the determining module is used for determining the knowledge field of the user;
and the construction module is used for constructing a preset knowledge map according to the knowledge points in the knowledge field.
In one possible implementation, the building module includes:
the extraction unit is used for extracting knowledge points in the knowledge field;
the determining unit is used for determining the incidence relation between the knowledge points;
and the generating unit is used for generating a preset knowledge graph by taking the knowledge points as nodes according to the association relationship between the knowledge points and the knowledge points.
In one possible implementation manner, the knowledge recommendation apparatus further includes:
and the setting module is used for setting weight values corresponding to the association relationship between the knowledge points.
In one possible implementation, the setting module includes:
the acquisition unit is used for acquiring a first historical learning condition corresponding to the knowledge point;
the first setting unit is used for determining a weight value corresponding to the association relation according to the first historical learning condition;
alternatively, the first and second electrodes may be,
a searching unit, configured to search for a neighboring user similar to the user;
and the second setting unit is used for determining the weight value corresponding to the association relationship according to a second historical learning condition of the neighbor user corresponding to the knowledge point.
In one possible implementation, the recommendation module includes:
the searching unit is used for searching a first position corresponding to the current learning knowledge point and a second position corresponding to the target learning knowledge point in the preset knowledge map;
the planning unit is used for planning a learning knowledge chain from the current learning knowledge point at the first position to the target learning knowledge point at the second position according to the weight value corresponding to the incidence relation between the knowledge points;
and the recommending unit is used for recommending the knowledge points in the learning knowledge chain to the user according to the sequence.
In one possible implementation, the orthotic module comprises:
the sending module is used for generating test questions according to the current learning knowledge points and sending the test questions to the user;
the analysis module is used for analyzing according to the answer condition returned by the user to obtain the mastering condition of the user on the current learning knowledge point;
searching for neighbor knowledge points of the current learning knowledge points when the mastering conditions of the current learning knowledge points by the user do not accord with preset expectations;
and correcting the learning knowledge chain according to the neighbor knowledge points.
In a third aspect, an embodiment of the present application provides a recommendation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, and the computer program realizes the method according to the first aspect when executed by a processor.
Compared with the prior art, the embodiment of the application has the advantages that:
according to the knowledge recommending method and device, the learning knowledge chain corresponding to the user is generated by constructing the knowledge map, knowledge points on the knowledge chain are recommended to the user for learning according to the sequence, the learning knowledge chain is corrected in real time according to the knowledge mastering condition of the user, the situation that in the prior art, the knowledge is recommended to the user simply and the knowledge recommending effect is poor due to the fact that the absorbing degree of the user on the knowledge is not considered is avoided, and the effect of reasonably recommending the knowledge to the user is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a knowledge recommendation method provided by an embodiment of the present application;
fig. 2 is another schematic flow chart of a knowledge recommendation method provided in an embodiment of the present application before step S101 in fig. 1;
FIG. 3 is a detailed flowchart of step 202 in FIG. 2 of a knowledge recommendation method provided by an embodiment of the present application;
fig. 4 is a detailed flowchart of step S103 in fig. 1 of a knowledge recommendation method according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of a knowledge recommendation device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a recommendation device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
This solution is explained below with reference to specific examples.
Referring to fig. 1, a schematic flow chart of a knowledge recommendation method provided in an embodiment of the present application is shown, where the method may be applied to a recommendation device, and the recommendation device includes a terminal device and a server, where the terminal device may be a desktop computer, a notebook, a palmtop computer, and other computing devices, and the server may be a cloud server and other computing devices, and the method includes:
and step S101, acquiring a current learning knowledge point and a target learning knowledge point of a user.
For example, the current learning knowledge point of the user is a statistic and a probability, and the target learning knowledge point is a function.
By way of example and not limitation, referring to fig. 2, another schematic flow chart of a knowledge recommendation method provided in an embodiment of the present application before step S101 in fig. 1 is shown, where the method includes:
and step S201, determining the knowledge field of the user.
The knowledge domain may refer to a knowledge domain that the user desires to learn, for example, if the user is a junior middle school student, then the knowledge domain of the user includes all courses of junior middle school mathematics and chapters under each course.
And S202, constructing a preset knowledge graph according to knowledge points in the knowledge field.
It can be understood that, in the embodiment of the present application, a preset knowledge graph is constructed in advance according to the knowledge domain of the user.
Specifically, as shown in fig. 3, for a specific flowchart of step 202 in fig. 2 of a knowledge recommendation method provided in an embodiment of the present application, constructing a preset knowledge graph according to knowledge points in a knowledge domain includes:
and S301, extracting knowledge points in the knowledge field.
For example, a knowledge point may be a course and a chapter in junior middle school mathematics.
And step S302, determining the association relation between the knowledge points.
The association relationship may refer to the overlapping degree of the content between two knowledge points, for example, the character matrixes between two knowledge points are respectively constructed, the similarity between the two character matrixes is calculated, if the similarity is greater than a similarity threshold, it is determined that the association relationship exists between the two knowledge points, and if the similarity is less than the similarity threshold, it is determined that the association relationship does not exist between the two knowledge points.
And step S303, generating a preset knowledge graph according to the knowledge points and the incidence relation among the knowledge points.
It can be understood that the embodiment of the application takes knowledge points as nodes, takes the association relationship between the knowledge points as the relationship between the nodes, and constructs the preset knowledge graph.
The mode of constructing the knowledge graph can be a top-down mode or a bottom-up mode, and the semantic representation frame of the knowledge graph can be an RDF frame, for example, the association relationship between the knowledge point 1 and the knowledge point 2, the knowledge point 1 >; the semantic representation framework of the knowledge graph in the embodiment of the application can also be an OWL framework.
It can be understood that the knowledge graph is constructed by the knowledge points and the incidence relation of the knowledge points.
Step S102, inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user according to the sequence.
It can be understood that, in the embodiment of the present application, an appropriate learning path, i.e., a learning knowledge chain, can be planned for the user according to the current learning knowledge point and the target learning knowledge point of the user.
In a possible implementation manner, inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the front of the user according to a sequence, further comprising:
and setting weight values corresponding to the association relationship among the knowledge points.
It is understood that the association relationship between the knowledge points also has corresponding attribute values, i.e., weight values, which represent the learning difficulty of the user between two knowledge points.
In a possible implementation manner, setting the weight value corresponding to the association relationship between the knowledge points may be:
the method comprises the following steps of firstly, obtaining corresponding first historical learning conditions among knowledge points.
Wherein, the first historical learning situation may refer to the learning situation of other users in the process from one knowledge point to another knowledge point.
Illustratively, the embodiment of the application can acquire the learning condition of other users between two knowledge points through big data technology.
And secondly, determining a weight value corresponding to the association relation according to the first historical learning condition.
Wherein the historical learning condition comprises the learning time length between two knowledge points, the passing rate of the test and the proportion degree in the ascending examination.
In another possible implementation manner, setting the weight value corresponding to the association relationship between the knowledge points may be:
the first step is to find neighboring users similar to the user.
The neighboring user refers to a user having attributes substantially consistent with various aspects of the user, wherein the attributes include age, gender, academic calendar, background, and the like.
And secondly, determining a weight value corresponding to the association relation according to a second historical learning condition corresponding to the knowledge point of the neighbor user.
Wherein the second historical learning condition comprises the learning duration between two knowledge points of the neighboring user, the passing rate of the test and the proportion degree in the ascending examination.
It can be understood that, in the embodiment of the present application, the weight value of the association relationship between two knowledge points of the user may be set through a second history learning situation before the neighboring user.
By way of example and not limitation, referring to fig. 4, a detailed flowchart of step S103 in fig. 1 of a knowledge recommendation method provided in an embodiment of the present application is shown, where the method includes:
step S401, a first position corresponding to the current learning knowledge point and a second position corresponding to the target learning knowledge point are searched in a preset knowledge map.
The first position is a position corresponding to the current learning knowledge point in the preset knowledge graph, and the second position is a position corresponding to the target learning knowledge point in the preset knowledge graph.
Step S402, according to the weight values corresponding to the incidence relations between the knowledge points, a learning knowledge chain from the current learning knowledge point at the first position to the target learning knowledge point at the second position is planned.
For example, there are a plurality of learning knowledge chains from the current learning knowledge point at the first location to the target learning knowledge point at the second location, and then the weight values between all the knowledge points from the current learning knowledge point at the first location to the target learning knowledge point at the second location are added, and the knowledge chain with the largest weight value is taken as the best knowledge chain.
And S403, recommending the knowledge points in the learning knowledge chain to the user according to the sequence.
In specific application, when the method steps of the embodiment of the application are applied to the terminal equipment, the recommendation mode can be that the knowledge points in the learning knowledge chain are displayed to the user according to the sequence; when the method steps of the embodiment of the application are applied to the server, the recommendation method can be to push the knowledge points in the learning knowledge chain to the terminal corresponding to the user according to the sequence.
It can be understood that, assuming that the current learning knowledge point of the user is statistics and probability, and the target learning knowledge point is a function, the knowledge chain obtained by the user in the preset knowledge map may be statistics and probability-junior high mathematics-space and graph-graph and coordinate-function. Due to the fact that specific gravity is inconsistent among different modes for achieving target learning knowledge point selection, the target learning knowledge point selection method and the target learning knowledge point selection device aim to achieve the purpose that more learned things exist in the target learning knowledge points, the more occupied ratios among the knowledge points are better, and the smaller the sum of the weights between the current knowledge points and the target learning knowledge points is, the better the sum is.
And S103, correcting the learning knowledge chain in real time according to the grasping condition of the current learning knowledge point by the user.
It can be understood that, the embodiment of the application fully considers the absorption condition of the user on the knowledge point, and corrects the learning knowledge chain in real time according to the mastering condition of the user on the current learning knowledge point.
By way of example and not limitation, referring to fig. 4, a detailed flowchart of step S103 in fig. 1 of a knowledge recommendation method provided in an embodiment of the present application is shown, where the method includes:
and S401, generating test questions according to the current learning knowledge points, and sending the test questions to the user.
Specifically, the test question is generated according to the following steps:
the method comprises the following steps of constructing a test question matrix, wherein the test questions comprise M exercises, N knowledge points and an exercise set D = { D =1，d2，……，dnK set of knowledge points = { K }1，k2，…… knTest question matrix R = [ R ]mn]M×NWherein R represents a test question matrix, Rmn= 1 meaning that problem M relates to knowledge point N, rmn= 0 indicates that the problem M does not involve the knowledge point N.
Second, construct the user association matrix, H = [ a × B =]Wherein A =
Where A represents the degree of knowledge required by the user, e.g.
For the requirement of memory,
In understanding the needs of the application,
in order to analyze the overall need,
to explore the needs, B =
And B represents the user's capability, for example,
in order to be the learning calendar of the user,
is the intelligence quotient of the user,
to the extent that the user knows about the knowledge points,
the interest degree of the user in the knowledge point.
Thirdly, generating test questions according to the matching result of the user table recommendation matrix and the test question matrix.
It can be understood that the process of generating test questions in the embodiment of the application comprehensively considers the background information of the user, such as the degree of the knowledge required by the user, and achieves the effect of reasonably recommending the test questions to the user.
And S402, analyzing according to the answer condition returned by the user to obtain the mastering condition of the current learning knowledge point by the user.
For example, the answer returned by the user may be scored according to a scoring criterion, and if the score of the score exceeds a first score threshold, the user may indicate that the user is basically mastering the current learning knowledge point, and if the score of the score exceeds a second score threshold, the user may indicate that the user is proficient in mastering the current learning knowledge point.
And S403, when the user' S grasp condition of the current learning knowledge point does not accord with the preset expectation, searching the neighboring knowledge point of the current learning knowledge point.
The adjacent knowledge points of the current learning knowledge point are knowledge points which are positioned at the same level as the current learning knowledge point in a preset knowledge graph, and are not knowledge points which are close to the current learning knowledge point in distance or have a relationship with the current learning knowledge point in the knowledge graph.
It can be understood that the preset expectation refers to the degree of mastery of the current knowledge point of the user, and when the degree of mastery of the current knowledge point of the user is found to be not expected, the learning knowledge chain is adjusted in time, the neighboring knowledge points of the current learning knowledge point are searched in the knowledge graph, and the learning knowledge chain is corrected.
And S404, correcting the learning knowledge chain according to the adjacent knowledge points.
It can be understood that, in the learning knowledge chain, the current knowledge point is replaced by the adjacent knowledge point, and then the knowledge points in the learning knowledge chain are recommended to the user for learning according to the sequence.
According to the knowledge recommending method and device, the learning knowledge chain corresponding to the user is generated by constructing the knowledge map, knowledge points on the knowledge chain are recommended to the user in sequence for learning, the learning knowledge chain is corrected in real time according to the knowledge mastering condition of the user, the situation that the knowledge is only recommended to the user simply in the prior art is avoided, the absorbing degree of the user on the knowledge is not considered, the knowledge recommending effect is poor, and the effect of reasonably recommending the knowledge to the user is achieved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 5 shows a block diagram of a knowledge recommendation device provided in an embodiment of the present application, which corresponds to a knowledge recommendation method described in the above embodiment, and only shows portions related to the embodiment of the present application for convenience of description.
Referring to fig. 5, the apparatus includes:
an obtaining module 51, configured to obtain a current learning knowledge point and a target learning knowledge point of a user;
the recommending module 52 is configured to input the current learning knowledge point and the target learning knowledge point to a preset knowledge map, obtain a learning knowledge chain of the user, and recommend the knowledge points in the learning knowledge chain to the user according to a sequence;
and the correcting module 53 is configured to correct the learning knowledge chain in real time according to the user's grasp of the current learning knowledge point.
In one possible implementation manner, the knowledge recommendation apparatus further includes:
the determining module is used for determining the knowledge field of the user;
and the construction module is used for constructing a preset knowledge map according to the knowledge points in the knowledge field.
In one possible implementation, the building module includes:
the extraction unit is used for extracting knowledge points in the knowledge field;
the determining unit is used for determining the incidence relation between the knowledge points;
and the generating unit is used for generating a preset knowledge graph by taking the knowledge points as nodes according to the association relationship between the knowledge points and the knowledge points.
In one possible implementation manner, the knowledge recommendation apparatus further includes:
and the setting module is used for setting weight values corresponding to the association relationship between the knowledge points.
In one possible implementation, the setting module includes:
the acquisition unit is used for acquiring a first historical learning condition corresponding to the knowledge point;
the first setting unit is used for determining a weight value corresponding to the association relation according to the first historical learning condition;
alternatively, the first and second electrodes may be,
a searching unit, configured to search for a neighboring user similar to the user;
and the second setting unit is used for determining the weight value corresponding to the association relationship according to a second historical learning condition of the neighbor user corresponding to the knowledge point.
In one possible implementation, the recommendation module includes:
the searching unit is used for searching a first position corresponding to the current learning knowledge point and a second position corresponding to the target learning knowledge point in the preset knowledge map;
the planning unit is used for planning a learning knowledge chain from the current learning knowledge point at the first position to the target learning knowledge point at the second position according to the weight value corresponding to the incidence relation between the knowledge points;
and the recommending unit is used for recommending the knowledge points in the learning knowledge chain to the user according to the sequence.
In one possible implementation, the orthotic module comprises:
the sending module is used for generating test questions according to the current learning knowledge points and sending the test questions to the user;
the analysis module is used for analyzing according to the answer condition returned by the user to obtain the mastering condition of the user on the current learning knowledge point;
searching for neighbor knowledge points of the current learning knowledge points when the mastering conditions of the current learning knowledge points by the user do not accord with preset expectations;
and correcting the learning knowledge chain according to the neighbor knowledge points.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 6 is a schematic structural diagram of a recommendation device according to an embodiment of the present application. As shown in fig. 6, the recommendation apparatus 6 of this embodiment includes: at least one processor 60, a memory 61 and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, the processor 60 implementing the steps of the above-described method embodiments when executing the computer program 62.
The recommendation device 6 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The recommendation device may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is merely an example of the recommendation device 6, and does not constitute a limitation on the recommendation device 6, and may include more or less components than those shown, or combine some of the components, or different components, such as an input-output device, a network access device, etc.
The Processor 60 may be a Central Processing Unit (CPU), and the Processor 60 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may in some embodiments be an internal storage unit of the recommendation device 6, such as a hard disk or a memory of the recommendation device 6. The memory 61 may also be an external storage device of the recommendation device 6 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the recommendation device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the recommendation device 6. The memory 61 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiment of the present application further provides a readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps that can be implemented in the above method embodiments.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method of knowledge recommendation, comprising:
acquiring a current learning knowledge point and a target learning knowledge point of a user;
inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user according to the sequence;
and correcting the learning knowledge chain in real time according to the mastering condition of the user on the current learning knowledge point.
2. The method of knowledge recommendation according to claim 1, wherein the current learning knowledge point and the target learning knowledge point are input to a preset knowledge map to obtain a learning knowledge chain of the user, and the knowledge points in the learning knowledge chain are recommended to the user in sequence, which previously includes:
determining a knowledge domain of the user;
and constructing the preset knowledge graph according to the knowledge points in the knowledge field.
3. The method of knowledge recommendation according to claim 2, wherein constructing the preset knowledge graph according to knowledge points of the knowledge domain comprises:
extracting knowledge points of the knowledge field;
determining the incidence relation between the knowledge points;
and taking the knowledge points as nodes, and generating the preset knowledge graph according to the knowledge points and the incidence relation between the knowledge points.
4. The method of claim 3, wherein the current learning knowledge point and the target learning knowledge point are input to a preset knowledge map to obtain a learning knowledge chain of the user, and the knowledge points in the learning knowledge chain are recommended to the front of the user according to a sequence, further comprising:
and setting weight values corresponding to the association relationship between the knowledge points.
5. The method of claim 4, wherein setting a weight value corresponding to the association relationship between the knowledge points comprises:
acquiring a first historical learning condition corresponding to the knowledge point;
determining a weight value corresponding to the association relation according to the first historical learning condition;
alternatively, the first and second electrodes may be,
searching for a neighboring user similar to the user;
and determining a weight value corresponding to the association relationship according to a second historical learning condition corresponding to the knowledge point of the neighbor user.
6. The knowledge recommendation method according to claim 4, wherein the step of inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user, and recommending the knowledge points in the learning knowledge chain to the user in a sequential order comprises:
searching a first position corresponding to the current learning knowledge point and a second position corresponding to the target learning knowledge point in the preset knowledge map;
according to the weight values corresponding to the incidence relations between the knowledge points, a learning knowledge chain from the current learning knowledge point at the first position to the target learning knowledge point at the second position is planned;
recommending the knowledge points in the learning knowledge chain to the user according to the sequence.
7. The method of any one of claims 1 to 6, wherein correcting the learning knowledge chain in real time according to the user's grasp of the current learning knowledge point comprises:
generating test questions according to the current learning knowledge points, and sending the test questions to the user;
analyzing according to the answer condition returned by the user to obtain the mastering condition of the user on the current learning knowledge point;
when the mastering condition of a user to the current learning knowledge point does not accord with a preset expectation, searching a neighboring knowledge point of the current learning knowledge point;
and correcting the learning knowledge chain according to the neighbor knowledge points.
8. A knowledge recommendation apparatus, comprising:
the acquisition module is used for acquiring a current learning knowledge point and a target learning knowledge point of a user;
the recommendation module is used for inputting the current learning knowledge point and the target learning knowledge point into a preset knowledge map to obtain a learning knowledge chain of the user and recommending the knowledge points in the learning knowledge chain to the user according to the sequence;
and the correction module is used for correcting the learning knowledge chain in real time according to the mastering condition of the user on the current learning knowledge point.
9. Recommendation device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the method according to any of claims 1 to 7 when executing said computer program.
10. A readable storage medium, storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010735484.XA CN111859140B (en) | 2020-07-28 | 2020-07-28 | Knowledge recommendation method and device, recommendation equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010735484.XA CN111859140B (en) | 2020-07-28 | 2020-07-28 | Knowledge recommendation method and device, recommendation equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111859140A true CN111859140A (en) | 2020-10-30 |
| CN111859140B CN111859140B (en) | 2021-08-06 |
Family
ID=72947784
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010735484.XA Active CN111859140B (en) | 2020-07-28 | 2020-07-28 | Knowledge recommendation method and device, recommendation equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111859140B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112632393A (en) * | 2020-12-30 | 2021-04-09 | 北京博海迪信息科技有限公司 | Course recommendation method and device and electronic equipment |
| CN113239180A (en) * | 2021-07-13 | 2021-08-10 | 北京神州泰岳智能数据技术有限公司 | Learning path generation method and device, electronic equipment and storage medium |
| CN113297419A (en) * | 2021-06-23 | 2021-08-24 | 南京谦萃智能科技服务有限公司 | Video knowledge point determining method and device, electronic equipment and storage medium |
| CN113486056A (en) * | 2021-07-09 | 2021-10-08 | 平安科技（深圳）有限公司 | Learning condition acquisition method and device based on knowledge graph and related equipment |
| CN113722506A (en) * | 2021-08-31 | 2021-11-30 | 广东艾檬电子科技有限公司 | Intelligent knowledge point identification method, system, intelligent equipment and storage medium |
| CN117437099A (en) * | 2023-12-20 | 2024-01-23 | 青岛理工大学 | Intelligent teaching system and teaching method based on large language model |
| CN117648449A (en) * | 2024-01-30 | 2024-03-05 | 青岛培诺教育科技股份有限公司 | Self-adaptive pushing method, system, equipment and medium based on knowledge graph |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8799096B1 (en) * | 2005-06-03 | 2014-08-05 | Versata Development Group, Inc. | Scoring recommendations and explanations with a probabilistic user model |
| CN107038508A (en) * | 2017-06-06 | 2017-08-11 | 海南大学 | The study point tissue and execution route of the learning ability modeling of knowledge based collection of illustrative plates and the target drives of dynamic self-adapting recommend method |
| CN107085618A (en) * | 2017-06-01 | 2017-08-22 | 海南大学 | Study point and learning path towards the 5W target drives based on data collection of illustrative plates, Information Atlas and knowledge mapping are recommended |
| CN109993673A (en) * | 2019-04-10 | 2019-07-09 | 上海乂学教育科技有限公司 | Mathematics learns system and method when surveying in adaptive learning |
| CN110544414A (en) * | 2019-07-31 | 2019-12-06 | 安徽淘云科技有限公司 | knowledge graph processing method and device |
| CN110704732A (en) * | 2019-09-19 | 2020-01-17 | 广州大学 | Cognitive diagnosis-based time-sequence problem recommendation method |
-
2020
- 2020-07-28 CN CN202010735484.XA patent/CN111859140B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8799096B1 (en) * | 2005-06-03 | 2014-08-05 | Versata Development Group, Inc. | Scoring recommendations and explanations with a probabilistic user model |
| CN107085618A (en) * | 2017-06-01 | 2017-08-22 | 海南大学 | Study point and learning path towards the 5W target drives based on data collection of illustrative plates, Information Atlas and knowledge mapping are recommended |
| CN107038508A (en) * | 2017-06-06 | 2017-08-11 | 海南大学 | The study point tissue and execution route of the learning ability modeling of knowledge based collection of illustrative plates and the target drives of dynamic self-adapting recommend method |
| CN109993673A (en) * | 2019-04-10 | 2019-07-09 | 上海乂学教育科技有限公司 | Mathematics learns system and method when surveying in adaptive learning |
| CN110544414A (en) * | 2019-07-31 | 2019-12-06 | 安徽淘云科技有限公司 | knowledge graph processing method and device |
| CN110704732A (en) * | 2019-09-19 | 2020-01-17 | 广州大学 | Cognitive diagnosis-based time-sequence problem recommendation method |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112632393A (en) * | 2020-12-30 | 2021-04-09 | 北京博海迪信息科技有限公司 | Course recommendation method and device and electronic equipment |
| CN113297419A (en) * | 2021-06-23 | 2021-08-24 | 南京谦萃智能科技服务有限公司 | Video knowledge point determining method and device, electronic equipment and storage medium |
| CN113297419B (en) * | 2021-06-23 | 2024-04-09 | 南京谦萃智能科技服务有限公司 | Video knowledge point determining method, device, electronic equipment and storage medium |
| CN113486056A (en) * | 2021-07-09 | 2021-10-08 | 平安科技（深圳）有限公司 | Learning condition acquisition method and device based on knowledge graph and related equipment |
| CN113486056B (en) * | 2021-07-09 | 2023-06-09 | 平安科技（深圳）有限公司 | Knowledge graph-based learning condition acquisition method and device and related equipment |
| CN113239180A (en) * | 2021-07-13 | 2021-08-10 | 北京神州泰岳智能数据技术有限公司 | Learning path generation method and device, electronic equipment and storage medium |
| CN113722506A (en) * | 2021-08-31 | 2021-11-30 | 广东艾檬电子科技有限公司 | Intelligent knowledge point identification method, system, intelligent equipment and storage medium |
| CN117437099A (en) * | 2023-12-20 | 2024-01-23 | 青岛理工大学 | Intelligent teaching system and teaching method based on large language model |
| CN117437099B (en) * | 2023-12-20 | 2024-05-10 | 青岛理工大学 | Intelligent teaching system and teaching method based on large language model |
| CN117648449A (en) * | 2024-01-30 | 2024-03-05 | 青岛培诺教育科技股份有限公司 | Self-adaptive pushing method, system, equipment and medium based on knowledge graph |
| CN117648449B (en) * | 2024-01-30 | 2024-05-14 | 青岛培诺教育科技股份有限公司 | Self-adaptive pushing method, system, equipment and medium based on knowledge graph |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111859140B (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111859140B (en) | Knowledge recommendation method and device, recommendation equipment and readable storage medium | |
| US10902321B2 (en) | Neural networking system and methods | |
| CN106202453B (en) | Multimedia resource recommendation method and device | |
| JP7108720B2 (en) | Knowledge sharing method between dialogue systems, dialogue method, knowledge sharing device, dialogue device, electronic device and storage medium | |
| US20180137589A1 (en) | Contextual personalized list of recommended courses | |
| CN111563192B (en) | Entity alignment method, device, electronic equipment and storage medium | |
| CN109816265B (en) | Knowledge characteristic mastery degree evaluation method, question recommendation method and electronic equipment | |
| RU2664481C1 (en) | Method and system of selecting potentially erroneously ranked documents with use of machine training algorithm | |
| CN111475627B (en) | Method and device for checking solution deduction questions, electronic equipment and storage medium | |
| CN112000881A (en) | Learning method, system, computer device and storage medium for recommending knowledge | |
| CN106294535A (en) | The recognition methods of website and device | |
| CN113407814B (en) | Text searching method and device, readable medium and electronic equipment | |
| CN111582405B (en) | Data labeling method and device | |
| CN110717019A (en) | Question-answering processing method, question-answering system, electronic device and medium | |
| CN111522886A (en) | Information recommendation method, terminal and storage medium | |
| Awaliyah et al. | Digital Literation in Increasing Reading Interest and Overcoming Hoax News from Social Media in Basic School Students | |
| CN112528109B (en) | Data classification method, device, equipment and storage medium | |
| CN114021029A (en) | Test question recommendation method and device | |
| US20180089170A1 (en) | Skills detector system | |
| CN113590811A (en) | Text abstract generation method and device, electronic equipment and storage medium | |
| US20180090021A1 (en) | Ranking courses for a presentation | |
| CN114817552A (en) | Exercise association relation processing method, exercise association relation processing device, exercise association relation processing equipment and storage medium | |
| CN112380429A (en) | Exercise recommendation method and device | |
| Hui et al. | Implementation of intelligent recommendation system for learning resources | |
| CN111062449A (en) | Prediction model training method, interestingness prediction device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP02 | Change in the address of a patent holder |
Address after: 519031 office 1316, No. 1, lianao Road, Hengqin new area, Zhuhai, Guangdong Patentee after: LONGMA ZHIXIN (ZHUHAI HENGQIN) TECHNOLOGY Co.,Ltd. Address before: Room 417.418.419, building 20, creative Valley, 1889 Huandao East Road, Hengqin New District, Zhuhai City, Guangdong Province Patentee before: LONGMA ZHIXIN (ZHUHAI HENGQIN) TECHNOLOGY Co.,Ltd. |
|
| CP02 | Change in the address of a patent holder |