CN111523028A - Data recommendation method, device, equipment and storage medium - Google Patents
Data recommendation method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN111523028A CN111523028A CN202010301573.3A CN202010301573A CN111523028A CN 111523028 A CN111523028 A CN 111523028A CN 202010301573 A CN202010301573 A CN 202010301573A CN 111523028 A CN111523028 A CN 111523028A
- Authority
- CN
- China
- Prior art keywords
- data
- recommendation
- teaching
- student
- teaching content
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000006399 behavior Effects 0.000 claims description 69
- 239000000463 material Substances 0.000 claims description 57
- 230000006870 function Effects 0.000 description 11
- 238000000605 extraction Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 230000003044 adaptive effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the application discloses a data recommendation method, a device, equipment and a storage medium, which relate to the technical field of network teaching and comprise the following steps: when a data acquisition instruction is received, acquiring a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content; selecting target teaching contents in the teaching content data set according to the recommended reference data; recommending the selected target teaching content to the student. By adopting the scheme, the technical problem that the required teaching contents cannot be pertinently recommended to students in the prior art can be solved.
Description
Technical Field
The embodiment of the application relates to the technical field of network teaching, in particular to a data recommendation method, device, equipment and storage medium.
Background
The network teaching is a teaching mode which realizes teaching targets by applying multimedia and network technology under the guidance of certain teaching theory and thought and through multi-edge and multi-direction interaction of teachers, students, media and the like and collection, transmission, processing and sharing of teaching information of various media.
With the development of internet technology, network teaching has become a common teaching mode. When the students start the network teaching, the students can recommend related teaching contents to the students according to the contents browsed by the students in history so as to be selected by the students. However, when a student browses teaching contents which are not related to or less helpful for self-learning, it may result in that the recommended teaching contents are not teaching contents helpful to the student.
In conclusion, how to recommend needed teaching contents to students in a targeted manner becomes a problem which needs to be solved urgently.
Disclosure of Invention
The application provides a data recommendation method, device, equipment and storage medium, which are used for solving the technical problem that the required teaching contents cannot be pertinently recommended to students in the prior art.
In a first aspect, an embodiment of the present application provides a data recommendation method, including:
when a data acquisition instruction is received, acquiring a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content;
selecting target teaching contents in the teaching content data set according to the recommended reference data;
recommending the selected target teaching content to the student.
Further, the recommendation reference data includes a recommendation category,
the method further comprises the following steps:
acquiring historical browsing records of students browsing historical teaching contents;
sending the historical browsing record to the control equipment so that the control equipment sets a recommendation category according to the historical browsing record;
and storing the recommendation category sent by the control equipment.
Further, the sending the historical browsing record to the control device, so that the control device sets a recommendation category according to the historical browsing record includes:
determining the historical browsing category of the student according to the historical browsing record;
and sending the historical browsing category to the control equipment so that the control equipment sets a recommendation category according to the historical browsing category.
Further, the recommendation reference data includes: recommending categories, student location data and behavior data, wherein selecting targeted teaching content in the teaching content dataset according to the recommended reference data comprises:
selecting teaching contents matched with the student position data in the teaching content data set, and forming a first data subset;
selecting teaching contents matched with the behavior data in the teaching content data set, and forming a second data subset;
selecting teaching contents matched with the recommended categories in the teaching content data set, and forming a third data subset;
and respectively extracting teaching contents in the first data subset, the second data subset and the third data subset, and forming target teaching contents.
Further, the extracting the teaching contents in the first data subset, the second data subset and the third data subset respectively, and the composing the target teaching contents includes:
acquiring recommendation weights corresponding to the recommendation categories, the student position data and the behavior data;
and extracting teaching contents in proportion from the first data subset, the second data subset and the third data subset according to the recommendation weight, and forming target teaching contents.
Further, the method further comprises:
and acquiring the recommended weight set by the control equipment.
Further, the selecting the teaching content matching with the student position data in the teaching content data set and composing the first data subset includes:
determining teaching material information used by students according to the student position data;
acquiring teaching material labels of all teaching contents in the teaching content data set;
searching teaching material labels matched with the teaching material information in each teaching material label;
and acquiring teaching contents corresponding to the matched teaching material labels, and forming a first data subset.
In a second aspect, an embodiment of the present application further provides a data recommendation device, including:
the data acquisition module is used for acquiring a teaching content data set and recommendation reference data when receiving a data acquisition instruction, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content;
the target determining module is used for selecting target teaching contents in the teaching content data set according to the recommended reference data;
and the data recommendation module is used for recommending the selected target teaching content to the student.
In a third aspect, an embodiment of the present application further provides a data recommendation device, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the data recommendation method of the first aspect.
In a fourth aspect, embodiments of the present application further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the data recommendation method according to the first aspect.
According to the data recommendation method, the device, the equipment and the storage medium, when the data acquisition instruction is received, the teaching content data set and the recommendation reference data are acquired, wherein the recommendation reference data comprise at least one of recommendation types, student position data and behavior data, then the target teaching content is selected in the teaching content data set according to the recommendation reference data, and the target teaching content is recommended to the student, so that the technical problem that the required teaching content cannot be pertinently recommended to the student in the prior art can be solved. Through the recommendation categories set by the household terminal, the teaching contents corresponding to the knowledge points set by the parents can be recommended to the student terminal, and the condition that inapplicable teaching contents are recommended to the student due to low recognition degree of the student on the knowledge points is avoided. Through the student position data, the teaching content adaptive to the teaching version of the area can be recommended to the student end, and the condition that the teaching content of different teaching versions is recommended to the student so that the student cannot learn in a contrast mode is avoided. Knowledge points of student key learning can be determined through behavior data, and then relevant teaching contents are recommended. The recommendation method is more accurate than recommending teaching contents based on browsing records. Because the recommended teaching content is more suitable for students, the students can learn the needed content more pertinently and purposefully.
Drawings
Fig. 1 is a flowchart of a data recommendation method according to an embodiment of the present application;
FIG. 2 is a flowchart of another data recommendation method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a data recommendation system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a data recommendation device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a data recommendation device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration and not limitation. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
The data recommendation method provided in the embodiments may be executed by a data recommendation device, which may be implemented in software and/or hardware and integrated in a data recommendation device. It can be understood that the data recommendation device may be an intelligent device used by a student in a network teaching scene, and may be a background server providing services for the intelligent device used by the student. In the embodiment, the data recommendation device is taken as a background server for description, and the intelligent device used by the student is called a student end, and at this time, the student end includes but is not limited to intelligent devices such as a tablet computer, a mobile phone, and a learning machine.
Typically, the background server is in data communication with the student side to provide the student side with the network teaching service. Furthermore, in order to facilitate the parents to master the learning condition of the student, a home agent end may be further provided, where the home agent end includes but is not limited to an intelligent device used by the parents, such as a mobile phone and a tablet computer, that is, an application program with a control function is installed in the intelligent device used by the parents, and the application program communicates with the backend server, so that the parents can obtain the learning condition of the student end through the backend server.
Fig. 1 is a flowchart of a data recommendation method according to an embodiment of the present application. Referring to fig. 1, the data recommendation method specifically includes:
and 110, when a data acquisition instruction is received, acquiring a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content.
Specifically, when a student starts network teaching through a student end to learn online, the student end acquires teaching resources from the data recommendation device, and at the moment, a resource acquisition instruction sent by the student end can be understood as a data acquisition instruction.
Further, the teaching content data set refers to a data set composed of teaching contents, wherein the teaching contents can also be understood as teaching resources. The teaching content can be live video for teacher teaching, recorded video for teacher teaching, exercise collection, teaching notes and other contents. In the embodiment, a live video in which teaching contents are used as teachers for teaching is taken as an example. At the moment, when the student side confirms that the live video playing function is entered, a data acquisition instruction is sent to the data recommendation equipment, and when the data recommendation equipment receives the data acquisition instruction, a teaching content data set is acquired. Alternatively, when the educational content data set is obtained, the current latest live video may be obtained and the educational content data set is composed. In practical applications, live videos meeting other conditions may also be obtained and composed into an educational content data set, which is not limited in the embodiment. Typically, each live video in the teaching content data set may have a corresponding tag, where the tag content may be set according to an actual situation, for example, the tag records live subject, applicable grade, knowledge point of explanation, and applicable teaching material version. The tag content can be added by a teacher explaining live videos or automatically added by data recommendation equipment.
Since the live video in the acquired teaching content data set may not be completely suitable for the student side, the live video suitable for the student side needs to be selected and recommended to the student side. In an embodiment, live video suitable for a student side is selected by recommending reference data. Each student end has corresponding recommended reference data, and the specific content of the recommended reference data can be set according to actual conditions. In an embodiment, the set recommendation reference data includes at least one of a recommendation category, student location data, and behavior data.
The recommendation category contains knowledge points applicable to the student side, each knowledge point can be considered as a category, and the recommendation category is set by the associated control device, wherein the associated control device can also be understood as the parent side, that is, the knowledge points applicable to the student side are set by the parent side. It can be understood that the embodiment of the method for setting the recommendation category by the parent end is not limited, for example, the parent end sets the recommendation category according to the current learning content and the mastery degree of the student, and for example, the parent end obtains a live video historically browsed by the student end and sets the recommendation category according to the live video historically browsed by the student end.
The student position data refers to the geographical position data of the student, and the embodiment of the determination method is not limited. For example, the student end adds the student position data and reports the student position data to the data recommendation device, and for example, when the student end communicates, the student position data is obtained through the positioning function of the student end. For another example, when communicating with the student terminal, if the student terminal is detected to be WiFi connected, the student location data is acquired through the WiFi network.
The behavior data is behavior data generated when the student uses the student side to browse the historical teaching contents (live video). The historical teaching content can be live videos played by the student end in the previous network teaching process or live videos played by the student end within a set time length. When the student end watches the live video, operation behaviors such as searching, praise, comment and attention can be sent out, and at the moment, behavior data can be obtained according to the operation behaviors and the browsing records. Optionally, the behavior data may be generated by the data recommendation device by acquiring the operation behavior of the student side, or may be generated by the student side based on the operation behavior and sent to the data recommendation device.
And 120, selecting target teaching contents in the teaching content data set according to the recommended reference data.
Specifically, according to the recommended reference data, the teaching content (live video) suitable for the student side can be selected from the teaching content data set and recorded as the target teaching content. Wherein the target teaching content may be composed of a plurality of live videos. Optionally, the maximum number of live videos in the target teaching content is preset, and when the number of live videos applicable to the student end exceeds the maximum number, the live videos can be deleted. Alternatively, live video suitable for the student side is selected in the tutorial content dataset based on the maximum number.
It can be understood that the way of determining the target teaching content is different when the recommendation reference data contains different contents.
In a first example, when the recommendation reference data includes a recommendation category, a live video suitable for the recommendation category may be selected from the teaching content data set, that is, a live video having the same knowledge points as those included in the recommendation category is selected, and all or part of the live videos obtained by the selection may be used as the target teaching content.
In the second example, when the recommended reference data includes student position data, a teaching material version used in a region where the recommended reference data is located may be determined based on the student position data, then, a live video using the teaching material version is selected in the teaching content data set, that is, a live video of a teaching material version suitable for the region where the student is located is selected according to the teaching material version recorded in a tag of each live video, and then, all or part of the selected live videos are used as target teaching content.
And in the third example, when the recommended data comprises behavior data, determining live videos (such as commented and concerned live videos) of the student end in the historical network teaching process based on the behavior data to further determine knowledge points corresponding to the live videos of the student end, selecting the live videos corresponding to the knowledge points in the teaching content data set through tags, and then taking all or part of the selected live videos as target teaching content.
And in the fourth example, when the recommended reference data includes any two of recommended categories, student position data and behavior data, the teaching content data set can be screened for one time based on one recommended reference data to select a proper live video, and then the screened live video is screened for another time based on another recommended reference data to obtain the target teaching content. The method can also be used for screening the teaching content data sets once respectively based on two kinds of recommended reference data, each kind of recommended reference data corresponds to one data set at the moment, and then a certain number of live videos are selected from each data set to form the target teaching content. It can be understood that, when a certain number of live videos are selected from the two data sets, the number ratio corresponding to each data set may be set according to an actual situation, and the embodiment is not limited. For example, when the recommendation reference data includes a recommendation category and student position data, a live video suitable for the recommendation category may be selected from the teaching content data set based on the recommendation category (in the same manner as described in example one), and a live video suitable for the student position data may be selected again from the selected live videos (in the same manner as described in example two), so as to constitute the target teaching content. And selecting live videos suitable for student position data in the teaching content data set, and then selecting live videos suitable for recommended categories again in the selected live videos to form target teaching content. Or selecting live videos suitable for student position data and live videos suitable for recommendation categories in the teaching content data sets respectively, wherein the student position data corresponds to one data set, the recommendation categories correspond to one data set, and then selecting the live videos in the two data sets to form target teaching content. For another example, when the recommendation reference data includes a recommendation category and behavior data, a live video suitable for the recommendation category may be selected from the instructional content data set based on the recommendation category (in the same manner as described in example one), and a live video suitable for the behavior data may be selected again from the selected live videos (in the same manner as described in example three), so as to form the target instructional content. Or selecting live videos suitable for behavior data in the teaching content data set, and then selecting live videos suitable for recommended categories again in the selected live videos to form the target teaching content. Or selecting live videos suitable for behavior data and live videos suitable for recommendation categories in the teaching content data sets respectively, wherein the behavior data corresponds to one data set, the recommendation categories correspond to one data set, and then selecting the live videos in the two data sets to form the target teaching content.
Example five, the recommendation reference data includes a recommendation category, student location data, and behavior data. At this time, the priority relationship of the three types of recommended reference data can be set, firstly, the teaching content data set is screened once based on the recommended reference data with the highest priority, a proper live broadcast video is selected to form one data set, then, the data set is screened again based on the recommended reference data with the next highest priority, a proper live broadcast video is selected to form another data set, and finally, the another data set is screened again based on the recommended reference data with the lowest priority to obtain the target teaching content. The method can also be used for firstly screening the teaching content data sets once based on the three recommended reference data to obtain three data sets, and then selecting a certain number of live videos in each data set to form the target teaching content. It can be understood that, when a certain number of live videos are selected from the three data sets, the number proportion corresponding to each data set may be set according to actual conditions, and the embodiment is not limited. The manner of selecting the live video based on the recommended reference data may refer to the selection manner of the live video described in the corresponding example one to example three.
And step 130, recommending the selected target teaching content to the student.
Typically, the obtained target teaching content is returned to the student. Optionally, the live videos in the target teaching content are sorted, the sorted target teaching content is returned to the student side, and at this time, the sorted target teaching content is displayed in a home page of a live function of the student side. The sorting rule can be set according to actual conditions, for example, the live video is sorted according to data such as the number of viewers, the number of attention people or the number of subscribers.
After receiving the target teaching content, the student end can select the required live broadcast video to carry out online learning.
The technical problem that the required teaching content cannot be pertinently recommended to the students in the prior art can be solved by the technical means that when a data acquisition instruction is received, the teaching content data set and the recommendation reference data are acquired, wherein the recommendation reference data comprise at least one of recommendation categories, student position data and behavior data, then the target teaching content is selected in the teaching content data set according to the recommendation reference data, and the target teaching content is recommended to the students. Through the recommendation categories set by the household terminal, the teaching contents corresponding to the knowledge points set by the parents can be recommended to the student terminal, and the condition that inapplicable teaching contents are recommended to the student due to low recognition degree of the student on the knowledge points is avoided. Through the student position data, the teaching content adaptive to the teaching version of the area can be recommended to the student end, and the condition that the teaching content of different teaching versions is recommended to the student so that the student cannot learn in a contrast mode is avoided. Knowledge points of student key learning can be determined through behavior data, and then relevant teaching contents are recommended. The recommendation method is more accurate than recommending teaching contents based on browsing records. Because the recommended teaching content is more suitable for students, the students can learn the needed content more pertinently and purposefully.
On the basis of the above embodiment, the recommendation reference data includes a recommendation category. At this time, the recommendation category needs to be set by the associated control apparatus (i.e., the home agent). Correspondingly, in order to enable the parent end to set a reasonable recommendation category, the historical teaching content of the student end can be sent to the parent end, so that the parent end sets the recommendation category according to the historical teaching content. Accordingly, the setting data recommendation method includes steps 210 to 230:
and step 210, acquiring historical browsing records of the students browsing historical teaching contents.
The history browsing record refers to the record of the history teaching content browsed by the student terminal. The history browsing record may contain links, tags and the like of the live video.
Optionally, the data recommendation device may record the historical teaching content browsed by the student side and generate a historical browsing record, or the student side may generate the historical browsing record and send the historical browsing record to the data recommendation device.
Step 220, sending the historical browsing record to the control device, so that the control device sets a recommendation category according to the historical browsing record.
Specifically, the data recommendation device records the corresponding relationship between the student terminal and the parent terminal, and after the history browsing record is acquired, the history browsing record can be sent to the parent terminal. Optionally, the data recommendation device may periodically obtain the historical browsing records and send the historical browsing records to the parent end, so that the parent can regularly view the teaching contents browsed by the student end through the parent end. The acquisition period of the historical browsing records can be set according to actual conditions. Or when the fact that the household terminal is on line is detected, the history browsing record is obtained and sent to the household terminal.
After the parent end receives the historical browsing record, the teaching content recently browsed by the student end can be determined according to the historical browsing record. Then, the parent end can determine which teaching contents are helpful for the learning of the student and which are not helpful for the learning of the student in the teaching contents recently browsed by the student end, and set knowledge points suitable for the student to obtain the recommendation categories. The specific operation mode embodiment when the parent end sets the recommendation category is not limited. And after the household terminal finishes setting the recommendation category, returning the recommendation category to the data recommendation equipment.
It can be understood that when the student end and the student end can communicate directly, the student end can also send the history browsing records to the student end directly. Correspondingly, after the parent terminal sets the recommendation categories, the parent terminal can also send the recommendation categories to the student terminal, and when the student terminal sends the data acquisition instruction, the recommendation categories are sent at the same time.
Optionally, in order to facilitate the parent end to quickly and accurately get familiar with knowledge points of historical browsing of students, the setting step 220 may include steps 221 to 222:
and step 221, determining the historical browsing category of the student according to the historical browsing record.
Specifically, the knowledge points corresponding to the historical teaching contents can be determined according to the knowledge points recorded by the tags of the historical teaching contents in the historical browsing records. Optionally, if different historical teaching contents correspond to the same historical browsing category, only one historical browsing category may be reserved.
Step 222, sending the historical browsing category to the control device, so that the control device sets a recommendation category according to the historical browsing category.
After the history browsing category is obtained, the history browsing category is sent to the parent terminal, and then the parent terminal can determine the recommendation category according to the history browsing category.
And step 230, storing the recommendation category sent by the control device.
And after receiving the recommendation category fed back by the home agent, saving the recommendation category so as to obtain the recommendation category when receiving the data acquisition instruction. It can be understood that after the parental side updates the recommendation categories, the data recommendation device updates the recommendation categories at the same time.
Above-mentioned, set up recommendation classification through the head of a family end, can make recommendation classification more laminate student's actual conditions, simultaneously, avoided because the student makes the condition of recommending inapplicable teaching content to the student to knowledge point distinguishability.
Fig. 2 is a flowchart of another data recommendation method according to an embodiment of the present application. At this time, the recommendation reference data includes a recommendation category, student location data, and behavior data. Referring to fig. 2, the data recommendation method specifically includes:
and 310, when a data acquisition instruction is received, acquiring a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises recommendation categories, student position data and behavior data.
And 320, selecting teaching contents matched with the student position data in the teaching content data set, and forming a first data subset.
Specifically, each teaching content in the teaching content data set is traversed to find the teaching content matched with the student position data, and the matched teaching content is combined into a first data subset. When the teaching contents are matched, the position data added when the teaching contents are created can be obtained, and matching is carried out according to the position data. And determining the teaching material version according to the student position data, and further performing matching according to the teaching material version.
In the embodiment, a description is given by taking a manner of determining a teaching material version according to student position data and matching teaching contents according to the teaching material version as an example, in this case, step 320 includes steps 321 to 324:
step 321, determining teaching material information used by the student according to the student position data.
Specifically, the data recommendation device is preset with corresponding relations between different teaching material versions and use areas thereof, and when the teaching material version in a certain area is updated, the data recommendation device synchronously updates the corresponding relations. Illustratively, after the student position data is acquired, the area to which the student position data belongs is determined, then the teaching material version corresponding to the area is determined according to the pre-recorded corresponding relation, and the teaching material information is generated based on the teaching material version. It is to be understood that the division rule embodiment of the region is not limited, and for example, division is performed in units of provinces.
And 322, acquiring teaching material labels of the teaching contents in the teaching content data set.
Illustratively, the tags corresponding to the teaching contents in the teaching content data set include teaching material tags, and the teaching material tags record teaching material versions of the teaching materials used by the teaching contents. And when the teaching content data set is obtained, synchronously obtaining each teaching material label.
Step 323, searching teaching material labels matched with the teaching material information in each teaching material label.
Specifically, each teaching material label is matched with teaching material information to search for the matched teaching material label. And the teaching material version recorded by the matched teaching material label is consistent with the teaching material version in the teaching material information.
And 324, acquiring teaching contents corresponding to the matched teaching material labels, and forming a first data subset.
Specifically, according to the matched teaching material label, corresponding teaching contents are acquired in a teaching content data set, and the acquired teaching contents are combined into a data set.
Specifically, the knowledge points of the student for key learning are determined according to the behavior data. And then, acquiring knowledge points recorded in the labels corresponding to the teaching contents in the teaching content data set. Then, in the knowledge points corresponding to the labels, the knowledge points which are the same as the key learning knowledge points are searched, the teaching contents corresponding to the searched knowledge points are obtained, and then the obtained teaching contents are combined into a data set.
And 340, selecting teaching contents matched with the recommendation categories in the teaching content data set, and forming a third data subset.
Specifically, the knowledge points indicated by the recommendation categories are determined. And then, acquiring knowledge points recorded in the labels corresponding to the teaching contents in the teaching content data set. And searching knowledge points with the same recommendation category in the knowledge points corresponding to the labels, acquiring teaching contents corresponding to the searched knowledge points, and then forming a data set by the acquired teaching contents, wherein in the embodiment, the data set is recorded as a third data subset.
It should be noted that the first data subset, the second data subset, and the third data subset may be generated simultaneously or sequentially, and the embodiment is not limited thereto.
And 350, extracting teaching contents in the first data subset, the second data subset and the third data subset respectively, and forming target teaching contents.
Specifically, after the first data subset, the second data subset and the third data subset are obtained, the teaching contents are respectively extracted from the three data subsets, and the extracted teaching contents are used as target teaching contents. When the teaching contents are extracted from the three data subsets, the same amount of teaching contents can be extracted from the three data subsets, or the teaching contents can be extracted from the three data subsets according to the recommendation weight. In the embodiment, the teaching contents are extracted from the three data subsets according to the recommendation weight. The recommendation weight refers to the weight corresponding to the recommendation reference data, the recommendation category, the student position data and the behavior data respectively correspond to one recommendation weight, and the higher the recommendation weight is, the more important the corresponding recommendation reference data is for selecting the target teaching content. The recommendation weight may be set by the data recommendation device, for example, by using a big data technology, it is determined which recommended reference data corresponds to the teaching content with a high selection probability, and then a higher weight is set for the recommended reference data. The recommendation weight may also be set by the parent end, in an embodiment, the recommendation weight is set by the parent end as an example. At this time, the setting of the present embodiment further includes: and acquiring the recommended weight set by the control equipment. Typically, the parent end has a recommendation weight setting option, so that the parents set the recommendation weight of each recommendation reference data directly through the parent end, or the parents receive the priority of each recommendation reference data set by the parents, and then the parents determine the corresponding recommendation weight according to each priority. And after the parental terminal sets the recommendation weight, sending the recommendation weight to the data recommendation equipment. It can be understood that the parent end can set or modify the recommendation weight at any time according to actual needs, and send the set or modified recommendation weight to the data recommendation device. After the recommendation weight is set, the data recommendation device may select the teaching content according to the recommendation weight, at this time, the setting step 350 specifically includes steps 351 to 352:
and 351, acquiring recommendation weights corresponding to the recommendation categories, the student position data and the behavior data.
Step 352, extracting teaching contents in proportion from the first data subset, the second data subset and the third data subset according to the recommendation weight, and forming a target teaching content.
Optionally, the extraction ratios corresponding to different recommendation weights are preset, and then the teaching contents are respectively extracted from the three data subsets according to the extraction ratios. Or converting the recommendation weight into the corresponding percentage, calculating the corresponding extraction proportion according to the percentage, and then extracting the teaching contents in the three data subsets according to the extraction proportion.
It can be understood that the parent end may also set the extraction ratio directly according to the recommendation weight and send the extraction ratio to the data recommendation device, and at this time, the data recommendation device may extract directly based on the extraction ratio.
And step 360, recommending the selected target teaching content to the student.
The technical means that when a data acquisition instruction is received, a teaching content data set and a recommendation category, student position data and behavior data are acquired, then teaching contents are selected from the teaching content data set according to the student position data to form a first data subset, teaching contents are selected from the teaching content data set according to the behavior data to form a second data subset, teaching contents are selected from the teaching content data set according to the recommendation category to form a third data subset, then the teaching contents are selected from the three data subsets to form target teaching contents, and then the target teaching contents are recommended to students can be solved. The teaching contents recommended to the home keeper end, the teaching contents adaptive to the teaching version of the area where the student is located and the teaching contents of key learning of the student can be selected through the recommendation category, the student position data and the behavior data, so that the recommended teaching contents are more accurate, more targeted and more targeted. Meanwhile, by setting the recommendation weight, parents can guide the teaching content of students, and then the students can learn the required content.
Fig. 3 is a schematic structural diagram of a data recommendation system according to an embodiment of the present application, and referring to fig. 3, the data recommendation system includes a data recommendation device 41, a student end 42, and a household end 43. The parent end 43 may also be understood as an associated control device, and the student end 42 may be understood as an intelligent device used by the student in the network teaching process.
The data recommendation device 41 receives a data acquisition instruction sent by the student end 42, and acquires a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by the keeper end 43, and the behavior data is behavior data generated when the student browses historical teaching content; the data recommendation device 41 selects a target teaching content in the teaching content data set based on the recommendation reference data, and recommends the selected target teaching content to the student terminal 42. After receiving the target teaching content, the student end 42 displays the target teaching content for the student to select.
Further, the recommendation reference data includes a recommendation category, and the data recommendation device 41 may further obtain a history browsing record of the student end 42 and send the history browsing record to the household end 43. The household terminal 43 sets a recommendation category according to the history browsing record and returns to the data recommendation device 41. The data recommendation device 41 saves the recommendation category.
Further, when the data recommendation device 41 obtains the historical browsing record of the student end 42 and sends the historical browsing record to the household end 43, specifically: the data recommendation device 41 determines the historical browsing category of the student according to the historical browsing record, and sends the historical browsing category to the household 43. Correspondingly, when the parental level 43 sets the recommendation category according to the history browsing record, specifically: and setting a recommendation category according to the historical browsing category.
Further, recommending reference data includes: recommending categories, student position data and behavior data, and when the data recommendation device 41 selects target teaching content in the teaching content data set according to the recommendation reference data, specifically: selecting teaching contents matched with the student position data in the teaching content data set, and forming a first data subset; selecting teaching contents matched with the behavior data in the teaching content data set, and forming a second data subset; selecting teaching contents matched with the recommended categories in the teaching content data set, and forming a third data subset; and respectively extracting teaching contents in the first data subset, the second data subset and the third data subset, and forming target teaching contents.
Further, when the data recommendation device 41 extracts the teaching contents in the first data subset, the second data subset, and the third data subset, respectively, and composes the target teaching content, specifically: acquiring recommendation weights corresponding to the recommendation categories, the student position data and the behavior data; and extracting teaching contents in proportion from the first data subset, the second data subset and the third data subset according to the recommendation weight, and forming target teaching contents.
Further, the parent end 43 may set a recommendation weight and send the recommendation weight to the data recommendation device 41.
Further, when the data recommendation device 41 selects teaching contents matching the student position data in the teaching content data set and forms a first data subset, specifically: determining teaching material information used by students according to the student position data; acquiring teaching material labels of all teaching contents in the teaching content data set; searching teaching material labels matched with the teaching material information in each teaching material label; and acquiring teaching contents corresponding to the matched teaching material labels, and forming a first data subset.
It is understood that the functions not shown in the data recommendation system can refer to the data recommendation method and have the same beneficial effects.
Fig. 4 is a schematic structural diagram of a data recommendation device according to an embodiment of the present application, and referring to fig. 4, the data recommendation device includes: a data acquisition module 501, a targeting module 502, and a data recommendation module 503.
The data acquisition module 501 is configured to acquire a teaching content data set and recommended reference data when receiving a data acquisition instruction, where the recommended reference data includes at least one of recommended categories, student position data, and behavior data, the recommended categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content; a target determination module 502, configured to select a target teaching content in the teaching content data set according to the recommended reference data; and a data recommending module 503, configured to recommend the selected target teaching content to the student.
The technical problem that the required teaching content cannot be pertinently recommended to the students in the prior art can be solved by the technical means that when a data acquisition instruction is received, the teaching content data set and the recommendation reference data are acquired, wherein the recommendation reference data comprise at least one of recommendation categories, student position data and behavior data, then the target teaching content is selected in the teaching content data set according to the recommendation reference data, and the target teaching content is recommended to the students. Through the recommendation categories set by the household terminal, the teaching contents corresponding to the knowledge points set by the parents can be recommended to the student terminal, and the condition that inapplicable teaching contents are recommended to the student due to low recognition degree of the student on the knowledge points is avoided. Through the student position data, the teaching content adaptive to the teaching version of the area can be recommended to the student end, and the condition that the teaching content of different teaching versions is recommended to the student so that the student cannot learn in a contrast mode is avoided. Knowledge points of student key learning can be determined through behavior data, and then relevant teaching contents are recommended. The recommendation method is more accurate than recommending teaching contents based on browsing records. Because the recommended teaching content is more suitable for students, the students can learn the needed content more pertinently and purposefully.
On the basis of the above embodiment, the recommendation reference data includes a recommendation category, and the apparatus further includes: the record acquisition module is used for acquiring a history browsing record when a student browses history teaching contents; the browsing record sending module is used for sending the historical browsing record to the control equipment so that the control equipment sets a recommendation category according to the historical browsing record; and the category storage module is used for storing the recommendation categories sent by the control equipment.
On the basis of the above embodiment, the browsing record sending module specifically includes: the browsing category determining unit is used for determining the historical browsing category of the student according to the historical browsing record; and the browsing category sending unit is used for sending the historical browsing categories to the control equipment so as to enable the control equipment to set recommendation categories according to the historical browsing categories.
On the basis of the above embodiment, the recommended reference data includes: recommending categories, student location data, and behavior data, the goal determining module 502 specifically includes: a first subset determining unit, configured to select teaching contents matching the student location data from the teaching content data set, and form a first data subset; a second subset determining unit that selects teaching contents matching the behavior data in the teaching content data set and composes a second data subset; a third subset determining unit that selects teaching contents matching the recommended category in the teaching content data set and composes a third data subset; and the teaching content selection unit is used for extracting teaching contents in the first data subset, the second data subset and the third data subset respectively and forming target teaching contents.
On the basis of the above embodiment, the teaching content selecting unit includes: the weight obtaining subunit is used for obtaining recommendation weights corresponding to the recommendation categories, the student position data and the behavior data; and the content extraction subunit is used for extracting teaching contents in the first data subset, the second data subset and the third data subset in proportion according to the recommendation weight and forming target teaching contents.
On the basis of the above embodiment, the data recommendation apparatus further includes: and the weight receiving module is used for acquiring the recommended weight set by the control equipment.
On the basis of the above embodiment, the first subset determining unit includes: the information determining subunit is used for determining teaching material information used by the students according to the student position data; the label acquiring subunit is used for acquiring teaching material labels of each teaching content in the teaching content data set; the label matching subunit is used for searching teaching material labels matched with the teaching material information in each teaching material label; and the content acquisition subunit is used for acquiring the teaching content corresponding to the matched teaching material label and forming a first data subset.
The data recommendation device provided by the embodiment is included in the data recommendation device, can be used for executing the data recommendation method provided by any of the above embodiments, and has corresponding functions and beneficial effects.
It should be noted that, in the embodiment of the data recommendation apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the application.
Fig. 5 is a schematic structural diagram of a data recommendation device according to an embodiment of the present application. Specifically, as shown in fig. 5, the data recommendation apparatus includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of the processors 60 in the data recommendation device may be one or more, and one processor 60 is taken as an example in fig. 5; the processor 60, the memory 61, the input device 62 and the output device 63 in the data recommendation device may be connected by a bus or other means, and fig. 5 illustrates the connection by the bus as an example.
The memory 61 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules in the data recommendation method in the embodiment of the present application (for example, the data acquisition module 501, the target determination module 502, and the data recommendation module 503 in the data recommendation device). The processor 60 executes various functional applications and data processing of the data recommendation device by running software programs, instructions and modules stored in the memory 61, namely, implements the data recommendation method provided by any of the above embodiments.
The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the data recommendation device, and the like. Further, the memory 61 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 61 may further include memory located remotely from the processor 60, which may be connected to the data recommendation device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 62 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the data recommendation apparatus, and may also function as an image capture device (e.g., a camera), an audio capture device (e.g., a microphone), and the like. The output device 63 may include a display screen, speakers, etc. In addition, the data recommendation device may further include a communication device (not shown) that can perform data communication with other devices (such as the student side and the household side).
The data recommendation device can be used for executing the data recommendation method provided by any embodiment, and has corresponding functions and beneficial effects.
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a data recommendation method, the method including:
when a data acquisition instruction is received, acquiring a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content;
selecting target teaching contents in the teaching content data set according to the recommended reference data;
recommending the selected target teaching content to the student.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the data recommendation method provided in any embodiment of the present application.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute the data recommendation method according to the embodiments of the present application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (10)
1. A method for recommending data, comprising:
when a data acquisition instruction is received, acquiring a teaching content data set and recommendation reference data, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content;
selecting target teaching contents in the teaching content data set according to the recommended reference data;
recommending the selected target teaching content to the student.
2. The data recommendation method according to claim 1, wherein the recommendation reference data includes a recommendation category,
the method further comprises the following steps:
acquiring historical browsing records of students browsing historical teaching contents;
sending the historical browsing record to the control equipment so that the control equipment sets a recommendation category according to the historical browsing record;
and storing the recommendation category sent by the control equipment.
3. The data recommendation method according to claim 2, wherein the sending the historical browsing history to the control device to cause the control device to set recommendation categories according to the historical browsing history comprises:
determining the historical browsing category of the student according to the historical browsing record;
and sending the historical browsing category to the control equipment so that the control equipment sets a recommendation category according to the historical browsing category.
4. The data recommendation method according to claim 1, wherein the recommending reference data comprises: recommendation categories, student location data and behavior data,
the selecting the target teaching content in the teaching content data set according to the recommended reference data comprises:
selecting teaching contents matched with the student position data in the teaching content data set, and forming a first data subset;
selecting teaching contents matched with the behavior data in the teaching content data set, and forming a second data subset;
selecting teaching contents matched with the recommended categories in the teaching content data set, and forming a third data subset;
and respectively extracting teaching contents in the first data subset, the second data subset and the third data subset, and forming target teaching contents.
5. The data recommendation method of claim 4, wherein the extracting teaching content in the first data subset, the second data subset and the third data subset, respectively, and composing the target teaching content comprises:
acquiring recommendation weights corresponding to the recommendation categories, the student position data and the behavior data;
and extracting teaching contents in proportion from the first data subset, the second data subset and the third data subset according to the recommendation weight, and forming target teaching contents.
6. The data recommendation method of claim 5, further comprising:
and acquiring the recommended weight set by the control equipment.
7. The data recommendation method of claim 4, wherein the selecting teaching content in the teaching content data set that matches the student location data and composing a first data subset comprises:
determining teaching material information used by students according to the student position data;
acquiring teaching material labels of all teaching contents in the teaching content data set;
searching teaching material labels matched with the teaching material information in each teaching material label;
and acquiring teaching contents corresponding to the matched teaching material labels, and forming a first data subset.
8. A data recommendation device, comprising:
the data acquisition module is used for acquiring a teaching content data set and recommendation reference data when receiving a data acquisition instruction, wherein the recommendation reference data comprises at least one of recommendation categories, student position data and behavior data, the recommendation categories are set by associated control equipment, and the behavior data is behavior data generated when a student browses historical teaching content;
the target determining module is used for selecting target teaching contents in the teaching content data set according to the recommended reference data;
and the data recommendation module is used for recommending the selected target teaching content to the student.
9. A data recommendation device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a data recommendation method as recited in any one of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the data recommendation method of any one of claims 1-7 when executed by a computer processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010301573.3A CN111523028A (en) | 2020-04-16 | 2020-04-16 | Data recommendation method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010301573.3A CN111523028A (en) | 2020-04-16 | 2020-04-16 | Data recommendation method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111523028A true CN111523028A (en) | 2020-08-11 |
Family
ID=71902044
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010301573.3A Pending CN111523028A (en) | 2020-04-16 | 2020-04-16 | Data recommendation method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111523028A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112380335A (en) * | 2020-11-24 | 2021-02-19 | 中教云智数字科技有限公司 | Digital education resource recommendation system |
| CN113435827A (en) * | 2021-05-31 | 2021-09-24 | 广州番禺职业技术学院 | Classroom knowledge application propagation system under intelligent field |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050114340A1 (en) * | 2003-11-21 | 2005-05-26 | Huslak Nicholas S. | Method, system, and storage medium for providing adaptive programming listings over a network |
| CN109670110A (en) * | 2018-12-20 | 2019-04-23 | 蒋文军 | A kind of educational resource recommended method, device, equipment and storage medium |
| CN110196948A (en) * | 2019-06-10 | 2019-09-03 | 北京金山安全软件有限公司 | Content recommendation method and device, computer equipment and storage medium |
| CN110399541A (en) * | 2019-05-31 | 2019-11-01 | 平安国际智慧城市科技股份有限公司 | Topic recommended method, device and storage medium based on deep learning |
| CN110781391A (en) * | 2019-10-22 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Information recommendation method, device, equipment and storage medium |
-
2020
- 2020-04-16 CN CN202010301573.3A patent/CN111523028A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050114340A1 (en) * | 2003-11-21 | 2005-05-26 | Huslak Nicholas S. | Method, system, and storage medium for providing adaptive programming listings over a network |
| CN109670110A (en) * | 2018-12-20 | 2019-04-23 | 蒋文军 | A kind of educational resource recommended method, device, equipment and storage medium |
| CN110399541A (en) * | 2019-05-31 | 2019-11-01 | 平安国际智慧城市科技股份有限公司 | Topic recommended method, device and storage medium based on deep learning |
| CN110196948A (en) * | 2019-06-10 | 2019-09-03 | 北京金山安全软件有限公司 | Content recommendation method and device, computer equipment and storage medium |
| CN110781391A (en) * | 2019-10-22 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Information recommendation method, device, equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112380335A (en) * | 2020-11-24 | 2021-02-19 | 中教云智数字科技有限公司 | Digital education resource recommendation system |
| CN113435827A (en) * | 2021-05-31 | 2021-09-24 | 广州番禺职业技术学院 | Classroom knowledge application propagation system under intelligent field |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109670110B (en) | Educational resource recommendation method, device, equipment and storage medium | |
| CN110322738B (en) | Course optimization method, device and system | |
| US20190294668A1 (en) | Methods and systems for generating contextual data elements for effective consumption of multimedia | |
| Norris | Comparative political communications: Common frameworks or Babelian confusion? | |
| CN111611434B (en) | Online course interaction method and interaction platform | |
| CN111177304B (en) | Method and terminal for automatically generating user exercises | |
| CN110796098B (en) | Method, device, equipment and storage medium for training and auditing content auditing model | |
| CN106126524B (en) | Information pushing method and device | |
| CN105635824A (en) | Personalized channel recommendation method and system | |
| CN111522970A (en) | Exercise recommendation method, exercise recommendation device, exercise recommendation equipment and storage medium | |
| CN113382301A (en) | Video processing method, storage medium and processor | |
| CN107424100B (en) | Information providing method and system | |
| CN111400586A (en) | Group display method, terminal, server, system and storage medium | |
| CN114095749B (en) | Recommendation and live interface display method, computer storage medium and program product | |
| CN112131361B (en) | Answer content pushing method and device | |
| CN113254708A (en) | Video searching method and device, computer equipment and storage medium | |
| CN111523035A (en) | Recommendation method, device, server and medium for APP browsing content | |
| CN114339285A (en) | Knowledge point processing method, video processing method and device and electronic equipment | |
| CN111581435A (en) | Video cover image generation method and device, electronic equipment and storage medium | |
| KR20230036627A (en) | Learning video providing system that enables systematic learning | |
| CN111523028A (en) | Data recommendation method, device, equipment and storage medium | |
| CN105610973A (en) | Method and system for recording webpage reading process based on segmented interaction mode | |
| CN111522992A (en) | Method, device and equipment for putting questions into storage and storage medium | |
| CN114090882B (en) | Learning material pushing method and system based on mobile terminal | |
| CN114880458A (en) | Book recommendation information generation method, device, equipment and 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 |