CN108600337A - A kind of best learning Content automatic push method - Google Patents
A kind of best learning Content automatic push method Download PDFInfo
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- CN108600337A CN108600337A CN201810290377.3A CN201810290377A CN108600337A CN 108600337 A CN108600337 A CN 108600337A CN 201810290377 A CN201810290377 A CN 201810290377A CN 108600337 A CN108600337 A CN 108600337A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/205—Education administration or guidance
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Abstract
The present invention relates to a kind of best learning Content automatic push methods, include the following steps：Step 1 clicks theme；Step 2, typing label；The learning Content that step 3, system are suitble to according to tag match.Compared with prior art, the present invention has many advantages, such as accurately to recommend to be suitble to the learning Content of oneself to student.
Description
Technical field
The present invention relates to learning Content automatic push fields, more particularly, to a kind of best learning Content automatic push side
Method.
Background technology
Neither one product is can to recommend different learning Contents for each student on the market at present, mostly
The mode manually recommended both for certain type or teacher recommends learning Content etc. to student.
Currently, the automatic recommended technology research of Student oriented is not goed deep into still both at home and abroad, especially exploring based on subjectivity
The comment of feature and its combination query intention feature is worth automatic assessment, and automatic identification, matching, the group of best learning Content
Research in terms of knitting with recommendation still belongs to blank.
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of best learning Contents
Automatic push method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of best learning Content automatic push method, includes the following steps：
Step 1 clicks theme；
Step 2, typing label；
The learning Content that step 3, system are suitble to according to tag match.
Preferably, the typing label includes two kinds of forms：Input word and select permeability, the input word are
Several labels of student resource typing, if the select permeability is student when not selecting suitable label, system provides
Problem allows learner answering questions, to facilitate label.
Preferably, the label, which is divided into, has used and unused two class, and the label of use is as certain composition
Label occurred, it is on the contrary then be unused label.
Preferably, the step 3 includes：When the label of student's input is unused label, with the level-one mark of the label
Label are occurrence, which is changed to use label, and when the label of student's input is to have used label, system is according to the label
With suitable learning Content.
Preferably, in the step 3, when the label of student's input does not match with backstage label, system is according to mark
Sign content matching upper level label, if upper level label does not also match with backstage label, find more upper level label until
The superlative degree, if highest label does not also match with backstage label, system will ignore this label in matching content.
Preferably, in the step 3, matching principle is：The matched more persons of label are preferential；Matched number of labels is identical
When, it is preferential that non-matching label lacks person；It is random to occur when matching and non-matching label are all identical.Such matching principle energy
Quickly from the information of magnanimity fast accurate the information navigated to needed for student, reduce error.
Preferably, this method further includes evaluating matched learning Content, according to evaluation result come improve label and
The matching algorithm of learning Content.
Compared with prior art, the present invention has the following advantages：
1, student can select the matching of the content oneself liked progress learning materials；
2, the crucial label of hundreds of thousands item can match the required content of nearly all student in system；
3, by the algorithm of artificial intelligence, according to your personality, learning path, a variety of dimensions such as learning method, accurately
Recommend to be suitble to the learning Content of oneself to student.
Description of the drawings
Fig. 1 is the synchronous control flow chart of the present invention.
Specific implementation mode
The technical scheme in the embodiments of the invention will be clearly and completely described below, it is clear that described implementation
Example is a part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiments of the present invention, ordinary skill
The every other embodiment that personnel are obtained without making creative work should all belong to the model that the present invention protects
It encloses.
As shown in Figure 1, a kind of best learning Content automatic push method, includes the following steps：
Step 1 clicks theme；
Step 2, typing label；
The learning Content that step 3, system are suitble to according to tag match.
The typing label includes two kinds of forms：Input word and select permeability, the input word be student from
Several labels of main typing, if the select permeability is student when not selecting suitable label, system provides some problems and allows
Learner answering questions, to facilitate label.Example：(1) whom the hero of story is(2) whom the object that article is described is(3) you therefrom obtain
To harvest what is……
The label is divided into having used to be occurred with unused two class, the label for having used label to have write a composition as certain
Cross, it is on the contrary then be unused label.
The step 3 includes：It is matching with the level-one label of the label when the label of student's input is unused label
, which is changed to use label, and when the label of student's input is to have used label, system is suitble to according to the tag match
Learning Content.
In the step 3, when the label of student's input does not match with backstage label, system is according to label substance
With upper level label, if upper level label does not also match with backstage label, more upper level label is found up to highest, if
Highest label does not also match with backstage label, and system will ignore this label in matching content.
In the step 3, matching principle is：The matched more persons of label are preferential；When matched number of labels is identical, non-
It is preferential that the label matched lacks person；It is random to occur when matching and non-matching label are all identical.
If user inputs the earth, as there is no the article content of the related earth, system that can belong to the sun according to the earth in system
Be this one kind, push the solar system this kind of article content to user, also do not have such as, system can match more upper level such as the milky way
The content of system, until the highest level that can be found in system.
This method further includes evaluating matched learning Content, and label and learning Content are improved according to evaluation result
Matching algorithm.
Described evaluates specially matched learning Content
Return to one group and belong to certain text " keyword value " word number pair, these keywords have been best represented by this text
This core content, and the criticality relative to this article of these keywords is quantified by its value.
Good, there are also extraction keyword and the methods of quantized key degree for we, then we now can
Since compare two texts similarity degree.Formula is as follows：
Similarity (A, B)=Σ i ∈ mTFIDFA*TFIDFBSimilarity (A, B)=Σ i ∈ mTFIDFA*
TFIDFB
M is the set that two articles overlap keyword.This will two texts common key words product all be added in one
It rises, obtains the value for the similarity for finally representing two texts.
Citing：
Keyword is extracted respectively and TFIDF values are as follows：
A：" notional word study "：100, " study "：80, " translation "：40
B：" function word study "：100, " study "：90, " programming "：30
Only there are one common key words " study " for two articles, therefore similarity is：80*90=7200.
In learning process, the detailed process that knowledge point is obtained according to topic is as follows：
After getting the item content for needing to search for, corresponding first knowledge point is determined according to item content, then basis
The first knowledge point and the second weak knowledge are searched in conjunction with the second weak knowledge point that user oneself sets in the first knowledge point
The corresponding search answer of item content and reinforcing topic are shown to user, so that user by the corresponding reinforcing topic of point
Outside the answer that can be searched for, additionally it is possible to according to reinforcing topic associated with topic, be clicked through in conjunction with the second weak knowledge
Row is strengthened, and is conducive to more comprehensively grasp knowledge point, improve learning efficiency.
In another embodiment, in learning process, it can also carry out in the following ways：
User knowledge context analyzer：The more hobby of user, and social account, dynamic analysis go out the knowledge back of the body of user
Scape.
Build oriented label relational graph：Oriented label relational graph stores existing a kind of inherent connection between all knowledge points
System, i.e., the relationships such as on knowledge content includes, mutual exclusion.The structure of knowledge of entire label relational graph is a netted structure.Mark
The content that label relational graph includes can be divided into several big kens, and the structure of knowledge of each ken is tree-like knot
Structure.Each big ken eventually forms a tree structure by the root node subdivision down from level to level of a knowledge.
Structure extension knowledge base：Extension knowledge base is the further demand in order to meet user to knowledge, when user is to being
When the micro- knowledge recommended of uniting is interested, system will recommend the extension knowledge for micro- knowledge to user, this may be one with
Micro- relevant website of knowledge or an article.
It was found that the desired missing knowledge of user：Oriented label relational graph provides a complete knowledge structure system,
It is equivalent to and has formulated a comprehensive study plan for user.The knowledge point that user is currently paid close attention to is marked in label relational graph
In, therefrom find currently to pay close attention to user knowledge point it is closely related be still knowledge that user had not contacted, recommend use
Family learns, to help user to achieve the purpose that knowledge leakage detection is filled a vacancy.
Realize that the education resource based on construction is recommended:Using the desired missing knowledge of user as knowledge to be recommended, recommend
User learns.The process that user learns missing knowledge is exactly the process of a structure knowledge.User often learns a missing knowledge,
The structure of knowledge of user will be more perfect.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection domain subject to.
Claims (7)
1. a kind of best learning Content automatic push method, which is characterized in that include the following steps：
Step 1 clicks theme；
Step 2, typing label；
The learning Content that step 3, system are suitble to according to tag match.
2. a kind of best learning Content automatic push method according to claim 1, which is characterized in that the typing mark
Label include two kinds of forms：Word and select permeability are inputted, the input word is several labels of student resource typing, described
Select permeability when being if that student does not select suitable label, system provides some problems and allows learner answering questions, to facilitate label.
3. a kind of best learning Content automatic push method according to claim 1, which is characterized in that the label point
To have used and unused two class, the label of having used is to be used as the label of certain composition to occur, on the contrary then be unused label.
4. a kind of best learning Content automatic push method according to claim 3, which is characterized in that the step 3
Including：When the label of student's input is unused label, using the level-one label of the label as occurrence, which is changed to use
Label, when the label of student's input is to have used label, system is according to the suitable learning Content of the tag match.
5. a kind of best learning Content automatic push method according to claim 4, which is characterized in that the step 3
In, when the label of student's input does not match with backstage label, system matches upper level label according to label substance, if upper one
Grade label does not also match with backstage label, then finds more upper level label until highest, if highest label also not with it is rear
Station symbol label match, and system will ignore this label in matching content.
6. a kind of best learning Content automatic push method according to claim 1, which is characterized in that the step 3
In, matching principle is：The matched more persons of label are preferential；When matched number of labels is identical, it is preferential that non-matching label lacks person；
Match and when non-matching label is all identical, it is random to occur.
7. a kind of best learning Content automatic push method according to claim 1, which is characterized in that this method further includes
Matched learning Content is evaluated, the matching algorithm of label and learning Content is improved according to evaluation result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113726876A (en) * | 2021-08-27 | 2021-11-30 | 维沃移动通信有限公司 | Learning content pushing method and device, electronic equipment and readable storage medium |
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Application publication date: 20180928 |