CN117076605A

CN117076605A - Content recommendation method and device, electronic equipment and storage medium

Info

Publication number: CN117076605A
Application number: CN202311049213.9A
Authority: CN
Inventors: 王刚
Original assignee: Baidu com Times Technology Beijing Co Ltd
Current assignee: Baidu com Times Technology Beijing Co Ltd
Priority date: 2023-08-18
Filing date: 2023-08-18
Publication date: 2023-11-17

Abstract

The disclosure provides a content recommendation method, a content recommendation device, electronic equipment and a storage medium, relates to the technical field of computers, and particularly relates to the technical fields of natural language processing, knowledge graph and the like. The specific implementation scheme is as follows: acquiring questions and student answers; inquiring a knowledge graph of the subject to which the topic belongs based on the knowledge point corresponding to the topic to acquire a first knowledge point path corresponding to the topic; determining a second knowledge point path corresponding to the student answer; determining target knowledge points which are not mastered by students according to the first knowledge point path and the second knowledge point path; and determining the content to be recommended based on the target knowledge points. Therefore, the first knowledge point path of the reference answer can be determined by combining the knowledge graph, and the target knowledge point which is not mastered by the student can be accurately determined according to the difference between the second knowledge point path of the student answer and the first knowledge point path, so that relevant content is provided for the student to learn, and personalized coaching can be provided for the target knowledge point which is not mastered by the student.

Description

Content recommendation method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of computers, in particular to the technical fields of natural language processing, knowledge graph and the like, and specifically relates to a content recommendation method, a content recommendation device, electronic equipment and a storage medium.

Background

Along with the continuous improvement of the living standard of people, the importance of people to education is higher and higher. In the learning process, students may encounter problems of difficult understanding of knowledge points, lost thinking of solving problems, and the like. Therefore, how to conduct personalized coaching for the student's problem becomes an important research direction.

Disclosure of Invention

The disclosure provides a content recommendation method, a content recommendation device, electronic equipment and a storage medium.

According to a first aspect of the present disclosure, there is provided a content recommendation method including:

acquiring questions and student answers;

inquiring a knowledge graph of the subject to which the subject belongs based on the knowledge points corresponding to the subject so as to acquire a first knowledge point path corresponding to the subject;

determining a second knowledge point path corresponding to the student answer;

determining target knowledge points which are not mastered by students according to the first knowledge point path and the second knowledge point path;

and determining the content to be recommended based on the target knowledge point.

According to a second aspect of the present disclosure, there is provided a content recommendation apparatus including:

the first acquisition module is used for acquiring the questions and the answers of the students;

the second acquisition module is used for inquiring the knowledge graph of the subject to which the subject belongs based on the knowledge points corresponding to the subject so as to acquire a first knowledge point path corresponding to the subject;

The first determining module is used for determining a second knowledge point path corresponding to the student answer;

the second determining module is used for determining target knowledge points which are not mastered by the students according to the first knowledge point path and the second knowledge point path;

and the third determining module is used for determining the content to be recommended based on the target knowledge point.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the content recommendation method according to the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the content recommendation method according to the first aspect.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the content recommendation method as described in the first aspect.

The content recommendation method, device, electronic equipment and storage medium provided by the present disclosure include the following steps

The beneficial effects are that:

in the embodiment of the disclosure, firstly, a question and a student answer are acquired, then, based on knowledge points corresponding to the question, a knowledge map of a subject to which the question belongs is queried, so as to acquire a first knowledge point path corresponding to the question, determine a second knowledge point path corresponding to the student answer, further, according to the first knowledge point path and the second knowledge point path, determine a target knowledge point which is not mastered by the student, and finally, slowly determine contents to be recommended based on the target knowledge point. Therefore, the first knowledge point path of the reference answer can be determined by combining the knowledge graph, and the target knowledge point which is not mastered by the student can be accurately determined according to the difference between the second knowledge point path of the student answer and the first knowledge point path, so that relevant content is provided for the student to learn, and personalized coaching can be provided for the target knowledge point which is not mastered by the student.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of a content recommendation method according to an embodiment of the present disclosure;

fig. 1A is a schematic diagram of a knowledge graph according to an embodiment of the disclosure;

FIG. 2 is a flow chart of a content recommendation method provided in accordance with yet another embodiment of the present disclosure;

FIG. 3 is a flow chart of a content recommendation method provided in accordance with yet another embodiment of the present disclosure;

fig. 4 is a schematic structural view of a content recommendation device according to an embodiment of the present disclosure;

fig. 5 is a block diagram of an electronic device for implementing a content recommendation method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The embodiment of the disclosure relates to the technical fields of natural language processing, knowledge graph and the like.

Natural language processing (Natural Language Processing), abbreviated as NLP, is a process that allows a computer to understand natural language like humans, thereby enabling the computer to process text, speech, and image data, to implement intelligent interactions, and to solve problems associated with natural language processing, such as text analysis, information retrieval, emotion analysis, and the like.

The Knowledge map (knowledgegraph), called Knowledge domain visualization or Knowledge domain mapping map in book condition report, is a series of various graphs showing Knowledge development process and structural relationship, and uses visualization technology to describe Knowledge resources and their carriers, and excavate, analyze, construct, draw and display Knowledge and their interrelationships. Specifically, the knowledge graph is a modern theory which combines the theory and method of subjects such as application mathematics, graphics, information visualization technology, information science and the like with the method of metering introduction analysis, co-occurrence analysis and the like, and utilizes the visualized graph to vividly display the core structure, development history, leading edge field and overall knowledge architecture of the subjects to achieve the aim of multi-subject fusion.

The following describes a content recommendation method, apparatus, electronic device, and storage medium of the embodiments of the present disclosure with reference to the accompanying drawings.

It should be noted that, the execution body of the content recommendation method in this embodiment is a content recommendation device, and the device may be implemented in a software and/or hardware manner, and the device may be configured in an electronic device, where the electronic device may include, but is not limited to, a terminal, a server, and the like.

Fig. 1 is a flowchart of a content recommendation method according to an embodiment of the present disclosure.

As shown in fig. 1, the content recommendation method includes:

s101: and obtaining the questions and the answers of the students.

The student answers are answers which the students answer according to the questions.

Alternatively, the student answer can be directly obtained by obtaining the text input by the student. Alternatively, the voice input by the student may be obtained, and then the voice of the student is subjected to text conversion to obtain the answer of the student.

S102: and inquiring the knowledge graph of the subject to which the subject belongs based on the knowledge points corresponding to the subject so as to acquire a first knowledge point path corresponding to the subject.

Optionally, text matching can be performed on the content in the topic and the content associated with the knowledge points in the knowledge graph, so as to obtain the knowledge points corresponding to the topic.

The subject matter may include Chinese, politics, mathematics, physics, and the like. The present disclosure is not limited in this regard.

In some embodiments, one knowledge graph may be created for each subject, and after determining the subject to which the subject belongs, a first knowledge point path may be determined based on the knowledge graph of the subject to which the subject belongs.

Alternatively, the knowledge data set corresponding to each subject may be obtained first, then the knowledge points contained in each knowledge data set and the association relationship between the knowledge points are determined, and finally the knowledge map corresponding to each subject is generated based on the knowledge points contained in each knowledge data set and the association relationship between the knowledge points. Therefore, the knowledge graph corresponding to each subject can be provided, and further after the subject to which the subject belongs is determined, the knowledge graph corresponding to the subject to which the subject belongs can be directly obtained, and further the first knowledge point path corresponding to the subject can be accurately determined based on the knowledge graph.

Wherein each subject's corresponding knowledge data set may contain a textbook of the subject, a teaching courseware of a teacher, a video resource, a teaching activity, and so on.

Alternatively, through text analysis technology, information such as keywords, entities, phrases and the like can be extracted from the text. These words and phrases can be used as clues to determine knowledge points. For example, frequently occurring terms or terms in text are extracted using techniques such as word frequency statistics, named entity recognition, and the like.

Optionally, there are relationships between knowledge points in the text, such as causal relationships, conceptual associations, etc. Through a relationship extraction technique, the relationships can be identified and extracted, thereby determining the association relationship between knowledge points.

Specifically, knowledge points are used as nodes in the knowledge graph, and the association relationship between the knowledge points is used as edges of two nodes in the knowledge graph, so that the knowledge graph is generated.

Optionally, determining a known condition text and a question text contained in the title, then determining a second knowledge point corresponding to the known condition text and a third knowledge point corresponding to the question text, and finally determining all paths of the second knowledge point reaching the third knowledge point in the knowledge graph as paths of the first knowledge point. Therefore, after the starting point and the end point of the first knowledge point path are determined, the first knowledge point path can be determined more accurately based on the knowledge graph.

Specifically, text matching is performed on the known condition text and knowledge content associated with each knowledge point in the knowledge graph, so as to determine a second knowledge point corresponding to the known condition text. And carrying out text matching on the question text and knowledge content associated with each knowledge point in the knowledge graph so as to determine a third knowledge point corresponding to the question text.

Fig. 1A is a schematic diagram of a knowledge graph according to an embodiment of the disclosure; as shown in fig. 1A, if the second knowledge point is the knowledge point C and the third knowledge point is the knowledge point O; all paths for knowledge point C to knowledge point O include path 1: C-G-M-N-O; path 2: C-G-K-R-O; path 3: C-G-K-O. The path 1, the path 2 and the path 3 are all paths of the first knowledge point.

S103: and determining a second knowledge point path corresponding to the student answer.

Optionally, determining each answering step in the student answer, performing text matching on the knowledge content associated with each answering step and each knowledge point in the knowledge graph to determine a knowledge point corresponding to each answering step, and finally generating a second knowledge point path based on the sequence of the answering steps and the knowledge point corresponding to each answering step. Thus, the second knowledge point path of the student answer can be accurately determined.

For example, step 1, step 2, step 3 are common among student answers; text matching is carried out on the knowledge content associated with each knowledge point in the knowledge graph in the steps 1, 2 and 3 respectively, so that knowledge points 1, 2 and 3 corresponding to the step 1 and 2 are determined; the generated second knowledge point path is knowledge point 1-knowledge point 2-knowledge point 3.

S104: and determining target knowledge points which are not mastered by the students according to the first knowledge point path and the second knowledge point path.

In some embodiments, where the second knowledge point path is a path segment in the first knowledge point path and the first knowledge point in the first knowledge point path is the same as the first knowledge point in the second knowledge point path, determining the last knowledge point in the second knowledge point path as the target knowledge point.

For example, the first knowledge point path is C-G-M-N-O, the second knowledge point path is C-G-M, and then knowledge point M is the target knowledge point.

Or under the condition that the first knowledge point path and the second knowledge point path have a path intersection, and the first knowledge point in the first knowledge point path is the same as the first knowledge point in the second knowledge point path, determining the last knowledge point in the path intersection as the target knowledge point.

For example, the first knowledge point path is C-G-M-N-O, the second knowledge point path is C-G-D, the path intersection is C-G, and then knowledge point G is the target knowledge point.

Or determining the first knowledge point in the first knowledge point path as the target knowledge point when the first knowledge point in the first knowledge point path is different from the first knowledge point in the second knowledge point path.

In the embodiment of the disclosure, since the student cannot deduce the next knowledge point according to the correct last knowledge point in the second knowledge point path, the student does not accurately grasp the correct last knowledge point in the second knowledge point path, and therefore the correct last knowledge point in the second knowledge point path is determined as the target knowledge point.

If the first knowledge point in the first knowledge point path is different from the first knowledge point in the second knowledge point path, the second knowledge point corresponding to the known condition text in the question is not mastered by the student, so that the first knowledge point in the first knowledge point path is determined as the target knowledge point.

In the embodiment of the disclosure, the target knowledge point can be accurately determined according to the difference between the first knowledge point path and the second knowledge point path.

S105: and determining the content to be recommended based on the target knowledge points.

Specifically, knowledge content related to the target knowledge point can be determined as content to be recommended. Therefore, students can learn the target knowledge points to guide the students to deduce the correct problem solving ideas in the next step based on the related knowledge of the target knowledge points.

For example, content in the knowledge dataset associated with the target knowledge point may be determined as content to be recommended.

as shown in fig. 2, the content recommendation method includes:

s201: and obtaining the questions and the answers of the students.

S202: and inquiring the knowledge graph of the subject to which the subject belongs based on the knowledge points corresponding to the subject so as to acquire a first knowledge point path corresponding to the subject.

S203: and determining a second knowledge point path corresponding to the student answer.

S204: and determining target knowledge points which are not mastered by the students according to the first knowledge point path and the second knowledge point path.

The specific implementation forms of step S201 to step S204 may refer to the detailed descriptions in other embodiments of the disclosure, and are not described in detail herein.

S205: and under the condition that the target knowledge point is not the first knowledge point in the first knowledge point path, acquiring a candidate knowledge point set connected with the target knowledge point in the knowledge graph.

For example, as shown in fig. 1A, if the target knowledge point is the knowledge point G, the candidate knowledge points included in the candidate knowledge point set are the knowledge point C, the knowledge point D, the knowledge point M, the knowledge point H, and the knowledge point K.

S206: a first knowledge point in the second knowledge point path adjacent to and before the target knowledge point is determined.

For example, if the second knowledge point path is C-G, where G is the target knowledge point, the first knowledge point is knowledge point C.

S207: and determining knowledge contents associated with the rest candidate knowledge points except the first knowledge point in the candidate knowledge point set and the target knowledge point as contents to be recommended.

For example, if the target knowledge point is the knowledge point G, the candidate knowledge points included in the candidate knowledge point set are the knowledge point C, the knowledge point D, the knowledge point M, the knowledge point H, and the knowledge point K. And determining knowledge contents associated with the knowledge points D, M, H, K and G as recommended contents if the first knowledge point is the knowledge point C. For example, the content in the knowledge data set associated with the knowledge point C, the knowledge point D, the knowledge point M, the knowledge point H, the knowledge point K, and the knowledge point G is determined as the recommended content.

It should be noted that, the target knowledge point is not the first knowledge point in the first knowledge point path, which indicates that the student can understand the problem, but an error occurs in a certain problem solving step, so that the candidate knowledge point associated with the target knowledge point can be obtained, and the candidate knowledge point and the knowledge content associated with the target knowledge point are recommended to the user, so that the student can master the target knowledge point and the association relationship between the target knowledge point and other knowledge points, and further deduce the next problem solving step.

S208: and under the condition that the target knowledge point is the first knowledge point in the first knowledge point path, acquiring a reference answer corresponding to the question and knowledge content associated with each knowledge point in the first knowledge point path.

The reference answers corresponding to the questions can be obtained from the question bank. Alternatively, a reference answer corresponding to the question may be generated according to the first knowledge point path. The present disclosure is not limited in this regard.

The knowledge content associated with each knowledge point in the first knowledge point path may be content associated with each knowledge point in the knowledge data set.

S209: and determining the reference answer and the knowledge content associated with each knowledge point in the first knowledge point path as the content to be recommended.

It should be noted that, the target knowledge point is the first knowledge point in the first knowledge point path, which indicates that the student's question understands incorrectly, and the first knowledge point (i.e. the second knowledge point corresponding to the known condition) is not mastered, at this time, the reference answer and the knowledge content related to the knowledge point related to each question solving step in the reference answer (i.e. each knowledge point in the first knowledge point path) may be directly recommended to the student, so that the student may learn to master the knowledge point related to the question.

In the embodiment of the disclosure, after determining a target knowledge point which is not mastered by a student, if the target knowledge point is not the first knowledge point in the first knowledge point path, acquiring a candidate knowledge point set connected with the target knowledge point in the knowledge graph, determining a first knowledge point adjacent to the target knowledge point and located in front of the target knowledge point in the second knowledge point path, and finally determining knowledge contents related to other candidate knowledge points except the first knowledge point and the target knowledge point in the candidate knowledge point set as contents to be recommended, or if the target knowledge point is the first knowledge point in the first knowledge point path, acquiring a reference answer corresponding to a question and knowledge contents related to each knowledge point in the first knowledge point path, and finally determining the reference answer and the knowledge contents related to each knowledge point in the first knowledge point path as contents to be recommended. Therefore, the content to be recommended can be determined according to whether the target knowledge point is the first knowledge point in the first knowledge point path, so that the students can recommend more proper content for the students according to whether the students understand the questions or not for the students to learn.

as shown in fig. 3, the content recommendation method includes:

s301: and obtaining the questions and the answers of the students.

S302: and inquiring the knowledge graph of the subject to which the subject belongs based on the knowledge points corresponding to the subject so as to acquire a first knowledge point path corresponding to the subject.

S303: and determining a second knowledge point path corresponding to the student answer.

S304: and determining target knowledge points which are not mastered by the students according to the first knowledge point path and the second knowledge point path.

S305: and determining the content to be recommended based on the target knowledge points.

The specific implementation forms of step S301 to step S305 may refer to the detailed descriptions in other embodiments of the disclosure, and are not described in detail herein.

S306: and displaying the content to be recommended.

In the embodiment of the disclosure, after the content to be recommended is determined, the content to be recommended can be displayed in a display interface of the terminal device, so that students can learn the content to be recommended.

S307: and under the condition that the updated answer of the questions submitted by the students is obtained, returning to execute the step of determining the second knowledge point path based on the updated answer until the second knowledge point path is identical with the first knowledge point path.

In some embodiments, after learning the content to be recommended, if the student knows the correct question solving step, the initial answer may be updated, so after obtaining the updated answer of the question submitted by the student, the student may further determine a second knowledge point path corresponding to the updated answer, and if the second knowledge point path is the same as the first knowledge point path, the updated answer submitted by the student is indicated to be correct.

If the second knowledge point path corresponding to the updated answer is different from the first knowledge point path, it can be further determined that the target knowledge point not mastered by the student is determined according to the updated answer, and the content related to the target knowledge point not mastered by the student is continuously recommended to the student for the student to learn again until the second knowledge point path is the same as the first knowledge point path.

In some embodiments, if the student submits the updated answer multiple times, but the second knowledge point path is still different from the first knowledge point path, the reference answer may be directly displayed. For example, when the number of times that the student submits the answer reaches the threshold value, and the second knowledge point path is still different from the first knowledge point path, determining the content to be recommended as the reference answer.

For example, the threshold may be 3 times, 5 times, etc. The present disclosure is not limited in this regard.

In the embodiment of the disclosure, after determining the content to be recommended, the content to be recommended may be displayed, and in the case that an updated answer of the question submitted by the student is obtained, the step of determining the second knowledge point path is performed based on the updated answer in a return manner until the second knowledge point path is the same as the first knowledge point path. Therefore, based on the content to be recommended, the student can be guided to perform the next correct problem solving step, and after the updated answer of the student is obtained, the knowledge points which the student does not grasp are further determined, so that the student can be ensured to grasp the problems and the related knowledge points.

Fig. 4 is a schematic structural view of a content recommendation device according to an embodiment of the present disclosure; as shown in fig. 4, the content recommendation device 400 includes:

a first obtaining module 401, configured to obtain a question and a student answer;

a second obtaining module 402, configured to query a knowledge graph of a subject to which the subject belongs based on a knowledge point corresponding to the subject, so as to obtain a first knowledge point path corresponding to the subject;

a first determining module 403, configured to determine a second knowledge point path corresponding to an answer of the student;

a second determining module 404, configured to determine a target knowledge point that is not mastered by the student according to the first knowledge point path and the second knowledge point path;

And a third determining module 405, configured to determine the content to be recommended based on the target knowledge point.

In some embodiments of the present disclosure, the second determining module 404 is configured to:

under the condition that the second knowledge point path is a path segment in the first knowledge point path and the first knowledge point in the first knowledge point path is the same as the first knowledge point in the second knowledge point path, determining the last knowledge point in the second knowledge point path as a target knowledge point; or,

under the condition that the first knowledge point path and the second knowledge point path have a path intersection, and the first knowledge point in the first knowledge point path is the same as the first knowledge point in the second knowledge point path, determining the last knowledge point in the path intersection as a target knowledge point; or,

and determining the first knowledge point in the first knowledge point path as a target knowledge point under the condition that the first knowledge point in the first knowledge point path is different from the first knowledge point in the second knowledge point path.

In some embodiments of the present disclosure, the third determining module 405 is configured to:

under the condition that the target knowledge point is not the first knowledge point in the first knowledge point path, acquiring a candidate knowledge point set which is connected with the target knowledge point in the knowledge graph;

Determining a first knowledge point adjacent to and in front of the target knowledge point in the second knowledge point path;

and determining knowledge contents associated with the rest candidate knowledge points except the first knowledge point in the candidate knowledge point set and the target knowledge point as contents to be recommended.

under the condition that the target knowledge point is the first knowledge point in the first knowledge point path, acquiring a reference answer corresponding to the question and knowledge content associated with each knowledge point in the first knowledge point path;

and determining the reference answer and the knowledge content associated with each knowledge point in the first knowledge point path as the content to be recommended.

In some embodiments of the present disclosure, the second obtaining module 402 is configured to:

determining a known condition text and a question text contained in the title;

determining a second knowledge point corresponding to the known condition text and a third knowledge point corresponding to the problem text;

and determining all paths from the second knowledge point to the third knowledge point in the knowledge graph as paths of the first knowledge point.

In some embodiments of the present disclosure, the first determining module 403 is configured to:

A step of determining each answer in the student answers;

text matching is carried out on each answering step and the knowledge content associated with each knowledge point in the knowledge graph so as to determine the knowledge point corresponding to each answering step;

and generating a second knowledge point path based on the sequence of the answering steps and the knowledge points corresponding to each answering step.

In some embodiments of the present disclosure, the apparatus further includes a generating module configured to:

acquiring a knowledge data set corresponding to each subject;

determining knowledge points contained in each knowledge data set and association relations among the knowledge points;

and generating a knowledge graph corresponding to each discipline based on the knowledge points contained in each knowledge data set and the association relation between the knowledge points.

In some embodiments of the present disclosure, wherein the processing module is configured to:

displaying the content to be recommended;

and under the condition that the updated answer of the questions submitted by the students is obtained, returning to execute the step of determining the second knowledge point path based on the updated answer until the second knowledge point path is identical with the first knowledge point path.

It should be noted that the foregoing explanation of the content recommendation method is also applicable to the content recommendation device of the present embodiment, and is not repeated here.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 5 illustrates a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the respective methods and processes described above, such as a content recommendation method. For example, in some embodiments, the content recommendation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the content recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the content recommendation method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise. In the description of the present disclosure, the words "if" and "if" are used to be interpreted as "at … …" or "at … …" or "in response to a determination" or "in the … … case".

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A content recommendation method, comprising:

acquiring questions and student answers;

determining a second knowledge point path corresponding to the student answer;

2. The method of claim 1, wherein the determining a target knowledge point not mastered by the student from the first knowledge point path and the second knowledge point path comprises:

determining the last knowledge point in the second knowledge point path as the target knowledge point under the condition that the second knowledge point path is a path segment in the first knowledge point path and the first knowledge point in the first knowledge point path is the same as the first knowledge point in the second knowledge point path; or,

determining the last knowledge point in the path intersection as the target knowledge point under the condition that the first knowledge point path and the second knowledge point path have a path intersection, and the first knowledge point in the first knowledge point path and the first knowledge point in the second knowledge point path are the same; or,

And determining the first knowledge point in the first knowledge point path as the target knowledge point under the condition that the first knowledge point in the first knowledge point path is different from the first knowledge point in the second knowledge point path.

3. The method according to claim 1 or 2, wherein the determining content to be recommended based on the target knowledge point comprises:

determining a first knowledge point adjacent to the target knowledge point and located before the target knowledge point in the second knowledge point path;

and determining knowledge contents associated with the rest candidate knowledge points except the first knowledge point in the candidate knowledge point set and the target knowledge point as the contents to be recommended.

4. The method according to claim 1 or 2, wherein the determining content to be recommended based on the target knowledge point comprises:

acquiring a reference answer corresponding to the question and knowledge content associated with each knowledge point in the first knowledge point path under the condition that the target knowledge point is the first knowledge point in the first knowledge point path;

5. The method of claim 1, wherein the querying the knowledge graph of the subject to which the subject belongs based on the knowledge points corresponding to the subject to obtain the first knowledge point path corresponding to the subject comprises:

determining a known conditional text and a question text contained in the title;

and determining all paths of the second knowledge points reaching the third knowledge points in the knowledge graph as paths of the first knowledge points.

6. The method of claim 1, wherein the determining a second knowledge point path corresponding to the student answer comprises:

determining each answer step in the student answers;

7. The method of claim 1, wherein the method further comprises:

acquiring a knowledge data set corresponding to each subject;

and generating a knowledge graph corresponding to each discipline based on the knowledge points contained in each knowledge data set and the association relation among the knowledge points.

8. The method of claim 1, wherein after the determining content to be recommended based on the target knowledge point, further comprising:

displaying the content to be recommended;

and under the condition that the updated answer of the question submitted by the student is obtained, returning to execute the step of determining a second knowledge point path based on the updated answer until the second knowledge point path is identical with the first knowledge point path.

9. A content recommendation device, comprising:

10. The apparatus of claim 9, wherein the second determining module is configured to:

11. The apparatus of claim 9 or 10, wherein the third determining module is configured to:

12. The apparatus of claim 9 or 10, wherein the third determining module is configured to:

13. The apparatus of claim 9, wherein the second acquisition module is configured to:

14. The apparatus of claim 9, wherein the first determining module is configured to:

determining each answer step in the student answers;

15. The apparatus of claim 9, wherein the apparatus further comprises a generation module to:

acquiring a knowledge data set corresponding to each subject;

16. The apparatus of claim 9, wherein the processing module is to:

displaying the content to be recommended;

17. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.

19. A computer program product comprising computer instructions which, when executed by a processor, implement the steps of the method of any of claims 1-8.