CN103440243A - Teaching resource recommendation method and device thereof - Google Patents
Teaching resource recommendation method and device thereof Download PDFInfo
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- CN103440243A CN103440243A CN2013102864579A CN201310286457A CN103440243A CN 103440243 A CN103440243 A CN 103440243A CN 2013102864579 A CN2013102864579 A CN 2013102864579A CN 201310286457 A CN201310286457 A CN 201310286457A CN 103440243 A CN103440243 A CN 103440243A
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Abstract
The invention discloses a teaching resource recommendation method and a device thereof. The method comprises the following steps of: acquiring a teaching behavior of a user terminal; acquiring a resource which is matched with the teaching behavior from the teaching resource and then generating recommendation information; transmitting the recommendation information to the user terminal. Thus, the resource search time of the user terminal can be reduced, and the utilization rate of the teaching resource is improved.
Description
Technical field
The embodiment of the present invention relates to Internet technical field, refers to especially a kind of teaching resource recommend method and device thereof.
Background technology
In the teaching resource net, the teacher can pass through keyword, catalogue (class mark, teaching material, grade, medium type, theme) or both various ways retrieve resources such as combination.But, when " resource ocean " in the face of so immense, the user is difficult to find at short notice the teaching resource of real needs.Therefore, tend to cause " information is got lost " and " information overload ".On the one hand cause the utilization rate of teaching resource not high, carried out the difficulty that Informalized teaching has brought also on the other hand user's applied teaching resource.
Summary of the invention
The embodiment of the present invention has proposed a kind of teaching resource recommend method and device thereof, can make user terminal reduce the resource searching time, improves the utilization factor of teaching resource.
A kind of teaching resource recommend method that the embodiment of the present invention provides comprises the following steps:
Obtain the education activities of user terminal;
Obtain the resource be complementary with described education activities from teaching resource, then generating recommendations information;
Send described recommendation information to user terminal.
Alternatively, before described teaching resource recommend method is carried out, receive the teaching data of at least one user terminal uploads, and the teaching data of reception is incorporated in described teaching resource.
Further, described teaching resource is the teaching resource with multidimensional attribute.
Further, the integration of the teaching data of reception being carried out to multidimensional attribute in described teaching resource recommend method is processed, and the step that obtains having the teaching resource of multidimensional attribute comprises:
Receive teaching data, and described teaching data is extracted;
Select modeling algorithm, and set up multidimensional teaching data model according to the teaching data after extracting;
Described multidimensional teaching data model is as the teaching resource with multidimensional attribute.
Further, described multidimensional teaching data model comprises the data of three kinds of dimensions: the type dimension of teaching data, the content category dimension of teaching data and the user terminal judge dimension to teaching data.
Further, in described teaching resource recommend method, obtain the resource be complementary with described education activities from teaching resource, then the step of generating recommendations information comprises:
Described education activities are carried out to the extraction of activity data;
Activity data to described extraction carries out pre-service;
Select modeling algorithm, and set up the education activities data model according to pretreated activity data;
Obtain the data that are complementary with described education activities data model in described teaching resource, then generating recommendations information.
Further, described teaching resource recommend method, can also be by described education activities after the step of generating recommendations information, and the recommendation information of described generation stores.
Further, in the described education activities of storage, and when the recommendation information of described generation, all education activities of storage are carried out to the Classified into groups of similarity;
Then by the recommendation information in each group, send to respectively the corresponding user terminal of each education activities in this group.
Based on above-mentioned purpose, the present invention also provides a kind of teaching resource recommendation apparatus, comprising:
The education activities unit, for receiving and store the education activities of user terminal;
The teaching resource unit, for storing the teaching resource data;
The intelligent recommendation engine, for from the teaching resource unit, obtain with described education activities unit the resource that is complementary of education activities, generating recommendations information also sends to user terminal.
Alternatively, described teaching resource unit receives the teaching data of at least one user terminal uploads, and the teaching data of reception is integrated to processing.
Further, described teaching resource unit is the teaching resource with multidimensional attribute.
Further, described teaching resource unit comprises receiver module, teaching data abstraction module and MBM, wherein:
Described receiver module, for receiving the teaching data of at least one user terminal uploads;
Described teaching data abstraction module, for extracting teaching data from described receiver module;
Described MBM, for selecting modeling algorithm, and set up multidimensional teaching data model according to the teaching data after extracting.
Further, described multidimensional teaching data model comprises the data of three kinds of dimensions: the type dimension of teaching data, the content category dimension of teaching data and the user terminal judge dimension to teaching data.
Alternatively, described intelligent recommendation engine comprises education activities data extraction module, MBM, matching module and output module; Wherein:
Described education activities data extraction module, for from described education activities unit, extracting activity data, and carry out pre-service;
Described MBM, for selecting modeling algorithm, and set up the education activities data model according to pretreated activity data;
Described matching module, for obtaining the data that are complementary with described education activities data model, generating recommendations information in described teaching resource unit;
Described output module, send to user terminal for the recommendation information by generation.
Further, described intelligent recommendation engine also comprises matching module in groups, with described matching module, is connected;
Described matching module in groups receives and stores described education activities data model, and the recommendation information obtained after the Data Matching in this education activities data model and described teaching resource unit.
Further, described matching module in groups will carry out the Classified into groups of similarity to all education activities of storage, and, by the recommendation information in each group, send to respectively the corresponding user terminal of each education activities in this group.
As can be seen from above, a kind of teaching resource recommend method and device thereof that the embodiment of the present invention provides, by obtain the resource be complementary with described education activities from teaching resource, generating recommendations information sends to user terminal.Thereby, can provide the recommendation information that precision is very high for user terminal, greatly simplified the search work for the treatment of of user terminal to teaching resource.
The accompanying drawing explanation
The schematic flow sheet that Fig. 1 is a kind of teaching resource recommend method of the embodiment of the present invention;
The schematic flow sheet that Fig. 2 is a kind of recommendation information of embodiment of the present invention generation method;
The structural representation that Fig. 3 is a kind of teaching resource recommendation apparatus of the embodiment of the present invention;
The structural representation of the teaching resource unit that Fig. 4 is embodiment of the present invention multidimensional attribute;
The structural representation that Fig. 5 is a kind of intelligent recommendation engine of the embodiment of the present invention.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and, with reference to accompanying drawing, the present invention is described in more detail.
Consult shown in Fig. 1, the schematic flow sheet for a kind of teaching resource recommend method of the embodiment of the present invention comprises:
In one embodiment of the invention, before carrying out the teaching resource recommend method, the teaching data of at least one user terminal uploads is received, and the teaching data of reception is incorporated in described teaching resource.Preferably, described teaching data source is the teaching resource with multidimensional attribute.Wherein, the teaching resource of multidimensional attribute can be to obtain by the following method:
The first step: receive teaching data, and to described teaching data extracted, pre-service.
Wherein, can the teaching data received be extracted by the data pick-up rule of regulation engine setting, regulation engine is a kind of assembly be embedded in application program, its task is tested the current teaching data of submitting to regulation engine and compared with all business rule that are carried in regulation engine, find the business rule met under the current data state and activate, then according to the actuating logic of the business rule activated, trigger corresponding operation.
In addition, can carry out pre-service to the teaching data through extracting, the outlier processing that for example data that surpass certain limit can be deleted; The pre-service work of data-switching of the cannonical format that is modeling by the format conversion of the data of extraction etc.
Second step: select modeling algorithm, and set up multidimensional teaching data model according to the teaching data after extracting.
Wherein, the modeling algorithm of selection can adopt decision Tree algorithms, cluster algorithm etc.As an embodiment, the multidimensional teaching data model of setting up can comprise the data of three kinds of dimensions: a kind of dimension is the type of teaching data, comprise text, image, animation, courseware, video, audio frequency etc., user terminal can be applied to different types at different application scenarioss.A kind of dimension is the content category of teaching data, comprises affiliated subject, grade, knowledge point, difficulty, keyword etc.The information that this dimension and user terminal need has very strong relevance, and for example the teacher makes courseware for concrete certain subject, certain grade and a concrete knowledge point.A kind of dimension is the judge of user terminal to teaching data, comprises scoring, evaluation, download rate, employing rate etc., and this dimension can guarantee that the last information pushed of the embodiment of the present invention is the comprehensive higher teaching resource of passing judgment on.
The 3rd step: described multidimensional teaching data model is as the teaching resource with multidimensional attribute.
In another embodiment of the present invention, as shown in Figure 2, the teaching resource recommend method, when generating recommendations information, has adopted following method:
Step 204 is obtained the data that are complementary with described education activities data model in described multidimensional teaching data model.Wherein, described multidimensional teaching data model can obtain by the method in step 102.
Also it should be noted that, in another embodiment of the present invention, the teaching resource recommend method is after carry out step 204, the recommendation information obtained after education activities data model and this education activities data model and multidimensional teaching data Model Matching can be stored, and all education activities data models of storage be carried out to the Classified into groups of similarity.Then, by all recommendation informations of each education activities data model in each group, send to the corresponding user terminal of each education activities data model in this group.
Consulting shown in Fig. 3, is the structural representation of a kind of teaching resource recommendation apparatus of the embodiment of the present invention.The teaching resource recommendation apparatus comprises: education activities unit 301, teaching resource unit 302 and intelligent recommendation engine 3 03; Wherein,
In one embodiment of the invention, as shown in Figure 4, teaching resource unit 302 comprises receiver module 401, teaching data abstraction module 402 and MBM 403.Wherein, receiver module 401 receives the teaching data of at least one user terminal uploads.Teaching data abstraction module 402 can be extracted the teaching data in receiver module 401 by the data pick-up rule of regulation engine setting, regulation engine is a kind of assembly be embedded in application program, its task is tested the current teaching data of submitting to regulation engine and compared with all business rule that are carried in regulation engine, find the business rule met under the current data state and activate, then according to the actuating logic of the business rule activated, trigger corresponding operation.For example; The teacher of take makes the extraction of carrying out data that courseware is purpose.In addition, teaching data abstraction module 402 can also carry out pre-service to the teaching data extracted.MBM 403 is selected modeling algorithms, can adopt decision Tree algorithms, cluster algorithm etc., and the data that then extract according to teaching data abstraction module 402 are set up multidimensional teaching data model.
Preferably, multidimensional teaching data model can comprise the data of three kinds of dimensions, i.e. the content category dimension of the type dimension of teaching data, teaching data and the user terminal judge dimension to teaching data.Wherein, the type dimension of teaching data comprises text, image, animation, courseware, video, audio frequency etc., and user terminal can be applied to different types at different application scenarioss.The content category dimension of teaching data comprises affiliated subject, grade, knowledge point, difficulty, keyword etc.The information that this dimension and user terminal need has very strong relevance, and for example the teacher makes courseware for concrete certain subject, certain grade and a concrete knowledge point.User terminal comprises scoring, evaluation, download rate, employing rate etc. to the judge dimension of teaching data, and this dimension can guarantee that the last information pushed of the embodiment of the present invention is the comprehensive higher teaching resource of passing judgment on.
Intelligent recommendation engine 3 03, can from the teaching resource unit 302 of multidimensional attribute, obtain with education activities unit 301 in the resource that is complementary of education activities, then generating recommendations information, finally send to user terminal.
As one embodiment of the present of invention, as shown in Figure 5, intelligent recommendation engine 3 03 comprises education activities data extraction module 501, MBM 502, matching module 503 and output module 504.Wherein, the data pick-up rule extraction activity data that data extraction module 501 arranges by regulation engine from education activities unit 301.In addition, data extraction module 501 can also be to carrying out pre-service through extracting the teaching data obtained.MBM 502 is selected modeling algorithm, then according to pretreated activity data, sets up the education activities data model.Matching module 503 obtains the data that are complementary with the education activities data model from multidimensional teaching data model, generating recommendations information.The recommendation information that output module 504 generates matching module 503 sends to user terminal.
For example, the behavior of teacher in making the courseware process shows as: 1, the teacher likes the courseware style of using, and 2, subject and the grade of teachers ' teaching, 3, the knowledge point paid close attention to of teacher, 4, the data type often downloaded of teacher.The education activities data model is set up in these interest activities according to the teacher in making the courseware process, then from the multidimensional teaching data model established, obtains the teaching resource be complementary with this education activities data model, generating recommendations information.
Also it should be noted that, in another embodiment of the present invention, intelligent recommendation engine 3 03 also comprises matching module 505 in groups, with matching module 503, be connected, can receive and store the education activities data model, and the recommendation information obtained after this education activities data model and multidimensional teaching data Model Matching.All education activities data models of 505 pairs of storages of matching module carry out the Classified into groups of similarity in groups, then all recommendation informations of each education activities data model in each group are sent to the corresponding user terminal of each education activities data model in this group by output module 504.Thereby, can find potential new interested teaching resource for each user terminal, enlarged the information of intelligent recommendation engines recommendations.
This shows, teaching resource recommend method and device thereof that the present invention realizes, creationaryly proposed the teaching resource data are integrated to process to set up a multidimensional teaching data model, and the education activities of user terminal are processed and set up the education activities model, finally obtain the teaching resource data that are complementary with the education activities data model from multidimensional teaching data model, generating recommendations information also is pushed to user terminal; User terminal is all obtaining lifting significantly to the information pushed aspect degree of accuracy and satisfaction; And the characteristics that had in actual applications for teaching resource, be divided into multiple dimension by the teaching resource data, can to data, be screened from each dimension; Meanwhile, the present invention can also form the user terminal with education activities model of similarity the group of same interest, provide potential teaching resource information thereby the education activities model by other user terminals in group is retrieved as this user terminal, improved the value-added service of the present invention to each user terminal; Finally, whole described teaching resource recommend method and install easyly, compact, be easy to realize, and provide a kind of the most convenient, practical experience for user terminal.
Should be noted that, in all parts of controller of the present invention, the function that will realize according to it and parts have wherein been carried out to logical partitioning, but, the present invention is not limited to this, can be repartitioned or combine all parts as required, for example, can be single parts by some component combinations, or some parts further can be decomposed into to more subassembly.
All parts embodiment of the present invention can realize with hardware, or realizes with the software module of moving on one or more processor, or realizes with their combination.It will be understood by those of skill in the art that and can use in practice microprocessor or digital signal processor (DSP) to realize according to some or all some or repertoire of parts in the controller of the embodiment of the present invention.The present invention for example can also be embodied as, for carrying out part or all equipment or device program (, computer program and computer program) of method as described herein.The program of the present invention that realizes like this can be stored on computer-readable medium, or can have the form of one or more signal.Such signal can be downloaded and obtain from internet website, or provides on carrier signal, or provides with any other form.
It should be noted above-described embodiment the present invention will be described rather than limit the invention, and those skilled in the art can design alternative embodiment in the situation that do not break away from the scope of claims.In claims, any reference symbol between bracket should be configured to limitations on claims.Word " comprises " not to be got rid of existence and is not listed in element or the step in claim.Being positioned at word " " before element or " one " does not get rid of and has a plurality of such elements.The present invention can realize by means of the hardware that includes some different elements and by means of the computing machine of suitably programming.In having enumerated the unit claim of some devices, several in these devices can be to carry out imbody by same hardware branch.The use of word first, second and C grade does not mean any order.Can be title by these word explanations.
Claims (16)
1. a teaching resource recommend method, is characterized in that, comprising:
Obtain the education activities of user terminal;
Obtain the resource be complementary with described education activities from teaching resource, then generating recommendations information;
Send described recommendation information to user terminal.
2. recommend method according to claim 1, is characterized in that, before described teaching resource recommend method is carried out, receives the teaching data of at least one user terminal uploads, and the teaching data of reception is incorporated in described teaching resource.
3. recommend method according to claim 1 and 2, is characterized in that, described teaching resource is the teaching resource with multidimensional attribute.
4. recommend method according to claim 3, is characterized in that, the integration of the teaching data of reception being carried out to multidimensional attribute in described teaching resource recommend method is processed, and the step that obtains having the teaching resource of multidimensional attribute comprises:
Receive teaching data, and described teaching data is extracted;
Select modeling algorithm, and set up multidimensional teaching data model according to the teaching data after extracting;
Described multidimensional teaching data model is as the teaching resource with multidimensional attribute.
5. recommend method according to claim 4, is characterized in that, described multidimensional teaching data model comprises the data of three kinds of dimensions: the type dimension of teaching data, the content category dimension of teaching data and the user terminal judge dimension to teaching data.
6. recommend method according to claim 2, is characterized in that, in described teaching resource recommend method, obtains the resource be complementary with described education activities from teaching resource, and then the step of generating recommendations information comprises:
Described education activities are carried out to the extraction of activity data;
Activity data to described extraction carries out pre-service;
Select modeling algorithm, and set up the education activities data model according to pretreated activity data;
Obtain the data that are complementary with described education activities data model in described teaching resource, then generating recommendations information.
7. according to the described recommend method of claim 1 to 6 any one, it is characterized in that, described teaching resource recommend method, can also be by described education activities after the step of generating recommendations information, and the recommendation information of described generation stores.
8. recommend method according to claim 7, is characterized in that, in the described education activities of storage, and when the recommendation information of described generation, all education activities of storage carried out to the Classified into groups of similarity;
Then by the recommendation information in each group, send to respectively the corresponding user terminal of each education activities in this group.
9. a teaching resource recommendation apparatus, is characterized in that, comprising:
The education activities unit, for receiving and store the education activities of user terminal;
The teaching resource unit, for storing the teaching resource data;
The intelligent recommendation engine, for from the teaching resource unit, obtain with described education activities unit the resource that is complementary of education activities, generating recommendations information also sends to user terminal.
10. recommendation apparatus according to claim 9, is characterized in that, described teaching resource unit receives the teaching data of at least one user terminal uploads, and the teaching data of reception is integrated to processing.
11. according to the described recommendation apparatus of claim 9 or 10, it is characterized in that, described teaching resource unit is the teaching resource with multidimensional attribute.
12. recommendation apparatus according to claim 11, is characterized in that, described teaching resource unit comprises receiver module, teaching data abstraction module and MBM, wherein:
Described receiver module, for receiving the teaching data of at least one user terminal uploads;
Described teaching data abstraction module, for extracting teaching data from described receiver module;
Described MBM, for selecting modeling algorithm, and set up multidimensional teaching data model according to the teaching data after extracting.
13. recommendation apparatus according to claim 12, is characterized in that, described multidimensional teaching data model comprises the data of three kinds of dimensions: the type dimension of teaching data, the content category dimension of teaching data and the user terminal judge dimension to teaching data.
14. recommendation apparatus according to claim 9, is characterized in that, described intelligent recommendation engine comprises education activities data extraction module, MBM, matching module and output module; Wherein:
Described education activities data extraction module, for from described education activities unit, extracting activity data, and carry out pre-service;
Described MBM, for selecting modeling algorithm, and set up the education activities data model according to pretreated activity data;
Described matching module, for obtaining the data that are complementary with described education activities data model, generating recommendations information in described teaching resource unit;
Described output module, send to user terminal for the recommendation information by generation.
15. recommendation apparatus according to claim 14, is characterized in that, described intelligent recommendation engine also comprises matching module in groups, with described matching module, is connected;
Described matching module in groups receives and stores described education activities data model, and the recommendation information obtained after the Data Matching in this education activities data model and described teaching resource unit.
16. recommendation apparatus according to claim 15, it is characterized in that, described matching module in groups will carry out the Classified into groups of similarity to all education activities of storage, and, by the recommendation information in each group, send to respectively the corresponding user terminal of each education activities in this group.
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| CN105225178A (en) * | 2014-06-25 | 2016-01-06 | 鸿合科技有限公司 | A kind of multi-user teaching resource read method and device |
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| CN108628967A (en) * | 2018-04-23 | 2018-10-09 | 西安交通大学 | A kind of e-learning group partition method generating network similarity based on study |
| CN108628967B (en) * | 2018-04-23 | 2020-07-28 | 西安交通大学 | Network learning group division method based on learning generated network similarity |
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| CN111241165B (en) * | 2020-02-17 | 2023-07-07 | 河北仓澜教育科技集团有限公司 | Artificial intelligence education system based on big data and data processing method |
| CN112182413A (en) * | 2020-12-01 | 2021-01-05 | 三盟科技股份有限公司 | Intelligent recommendation method and server based on big teaching data |
| CN112182413B (en) * | 2020-12-01 | 2021-02-19 | 三盟科技股份有限公司 | Intelligent recommendation method and server based on big teaching data |
| CN114549249A (en) * | 2022-02-24 | 2022-05-27 | 江苏兴教科技有限公司 | Online teaching resource library management system and method for colleges |
| CN114549249B (en) * | 2022-02-24 | 2023-02-24 | 江苏兴教科技有限公司 | Online teaching resource library management system and method for colleges |
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