Knowledge recommendation method and device for online education platform, computer equipment and medium

Abstract

The invention discloses a knowledge recommendation method and device for an online education platform, computer equipment and a medium, and the method comprises the steps: constructing a heterogeneous information network of the online education platform, dynamically generating semantic embedding of knowledge concepts, and obtaining basic embedded information; performing knowledge concept enhancement optimization on the basic embedded information to obtain enhanced embedded information; based on the enhanced embedding information, constructing a multi-type user interaction graph integrated with time context information; performing message transmission in the multi-type user interaction graph, updating node embedding of users and knowledge concepts, and obtaining target embedded information; performing weighted fusion on the target embedded information of different sources through a semantic attention module to obtain fused embedded information; by means of the extended matrix decomposition model and the fusion embedded information, the preference of the user for knowledge concepts is predicted, recommendation information is generated, and the knowledge recommendation accuracy of the online education platform is improved.

Description

Technical Field

[0001] The present invention relates to the field of information recommendation technology, and in particular to a knowledge recommendation method, device, computer equipment and medium for an online education platform. Background Art

[0002] With the development of artificial intelligence technology, online education platforms are becoming more and more widely used. With advantages such as rich resources and no time and space restrictions, they have attracted more and more students and teachers to join. However, as the number of resources continues to increase, it becomes difficult for learners to find content that they are really interested in or need. Studies have shown that many learners lack the perseverance and motivation to complete the course, resulting in most learners ultimately failing to complete their studies, causing a high dropout rate problem.

[0003] The current method is to make recommendations to users by profiling them and then determining their preferences. In the process of implementing the present invention, the inventors found that although this existing method can make targeted recommendations to a certain extent, it will lead to inaccurate profiling and recommendations when data is scarce. At the same time, the interests of the same user will change over time. Therefore, the accuracy of knowledge recommendation in this existing method is not high. Summary of the invention

[0004] The embodiments of the present invention provide a knowledge recommendation method, apparatus, computer equipment and storage medium for an online education platform, so as to improve the accuracy of knowledge recommendation of the online education platform.

[0005] In order to solve the above technical problems, the present application embodiment provides a knowledge recommendation method for an online education platform, comprising:

[0006] Constructing a heterogeneous information network of an online education platform, and using a meta-path-based random walk algorithm in the heterogeneous information network to dynamically generate semantic embeddings of knowledge concepts to obtain basic embedding information;

[0007] Using a neural network to enhance and optimize the knowledge concept of the basic embedded information to obtain enhanced embedded information;

[0008] Based on the enhanced embedding information, construct a multi-type user interaction graph incorporating time context information, wherein the multi-type user interaction graph records a variety of interaction behaviors between users and knowledge concepts at different time points;

[0009] By using a graph neural network to pass messages in the multi-type user interaction graph, the node embeddings of users and knowledge concepts are updated to obtain updated information of each enhanced embedding information as the target embedding information;

[0010] The target embedding information from different sources is weightedly fused through the semantic attention module to obtain fused embedding information;

[0011] The extended matrix factorization model and the fused embedding information are used to predict user preferences for knowledge concepts and generate recommendation information.

[0012] Optionally, in the heterogeneous information network, a random walk algorithm based on a meta-path is used to dynamically generate semantic embeddings of knowledge concepts, and the basic embedding information obtained includes:

[0013] A meta-path-based random walk is used to sample heterogeneous information networks and filter out nodes of the same type, and each group of nodes of the same type is formed into a node sequence;

[0014] The node sequence is projected into a low-dimensional space using a skip-word model to obtain a distributed representation of the node as the basic embedding information.

[0015] Optionally, the step of using a neural network to enhance and optimize the knowledge concept of the basic embedded information to obtain the enhanced embedded information includes:

[0016] Aggregating neighbor node information of the basic embedding information using a graph convolutional network to generate an auxiliary embedding representation;

[0017] A graph attention network is used to aggregate neighbor node information of the basic embedding information to generate an enhanced embedding representation;

[0018] The auxiliary embedding representation, the enhanced embedding representation and the basic embedding information are spliced ​​and fused respectively to obtain first enhanced embedding information and second enhanced embedding information, and the first enhanced embedding information and the second enhanced embedding information are used as the enhanced embedding information.

[0019] Optionally, constructing a multi-type user interaction graph incorporating temporal context information based on the enhanced embedding information includes:

[0020] Construct the initial interaction graph G m =(V m ,E m ), if user u and item v interact under the kth interaction type, then u and v k There is an edge between them, so V m =U∪V',v k ∈V', U represents all users, V' is the item with interaction type considered, where the initial interaction graph G m There are (I+J×K) vertices in , where I and J represent the number of users and items respectively, and K represents the number of interaction types;

[0021] If user u has an item vk The interaction time is It is mapped to a single time slot The sine function is used to generate the relative time embedding of the interaction, and a multi-type user interaction graph with temporal context information is obtained.

[0022] Optionally, the weighted fusion of target embedding information from different sources by the semantic attention module to obtain fused embedding information includes:

[0023] Calculate the attention weights of the target embedding information corresponding to user u and item v, and perform normalization;

[0024] The normalized attention weight is applied to the target embedding information to obtain the fused embedding information.

[0025] Optionally, the knowledge recommendation method of the online education platform further includes:

[0026] Define the latent factors of user embedding and item embedding, and adjust the parameters of the initial matrix decomposition model through the optimization function;

[0027] The model is trained using a gradient descent method until convergence, thereby obtaining the extended matrix decomposition model.

[0028] In order to solve the above technical problems, the embodiment of the present application further provides a knowledge recommendation device for an online education platform, comprising:

[0029] An information extraction module is used to construct a heterogeneous information network of an online education platform, and in the heterogeneous information network, a random walk algorithm based on a meta-path is used to dynamically generate semantic embeddings of knowledge concepts to obtain basic embedding information;

[0030] An information enhancement module, used to enhance and optimize the knowledge concept of the basic embedded information using a neural network to obtain enhanced embedded information;

[0031] An interaction graph construction module, used to construct a multi-type user interaction graph incorporating time context information based on the enhanced embedding information, wherein the multi-type user interaction graph records a variety of interaction behaviors between users and knowledge concepts at different time points;

[0032] An information updating module is used to perform message transmission in the multi-type user interaction graph through a graph neural network, update the node embedding of users and knowledge concepts, and obtain update information of each enhanced embedding information as target embedding information;

[0033] The information fusion module is used to perform weighted fusion of target embedding information from different sources through the semantic attention module to obtain fused embedding information;

[0034] The knowledge recommendation module is used to predict the user's preference for knowledge concepts and generate recommendation information by using the extended matrix decomposition model and the fused embedding information.

[0035] Optionally, the information extraction module includes:

[0036] A node screening unit is used to sample the heterogeneous information network using a random walk based on a meta-path and screen out nodes of the same type, and form each group of nodes of the same type into a node sequence;

[0037] The node projection unit is used to project the node sequence into a low-dimensional space using a skip-word model to obtain a distributed representation of the node as the basic embedding information.

[0038] Optionally, the information enhancement module includes:

[0039] A first aggregation unit, configured to aggregate neighbor node information of the basic embedding information using a graph convolutional network to generate an auxiliary embedding representation;

[0040] A second aggregation unit is used to aggregate neighbor node information of the basic embedding information using a graph attention network to generate an enhanced embedding representation;

[0041] A fusion enhancement unit is used to splice and fuse the auxiliary embedding representation, the enhanced embedding representation and the basic embedding information respectively to obtain first enhanced embedding information and second enhanced embedding information, and use the first enhanced embedding information and the second enhanced embedding information as the enhanced embedding information.

[0042] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the knowledge recommendation method of the above online education platform when executing the computer program.

[0043] In order to solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the steps of the knowledge recommendation method of the above online education platform.

[0044] The knowledge recommendation method, device, computer equipment and storage medium for an online education platform provided by the embodiment of the present invention construct a heterogeneous information network of the online education platform, and use a random walk algorithm based on a meta-path in the heterogeneous information network to dynamically generate semantic embeddings of knowledge concepts to obtain basic embedding information; use a neural network to enhance and optimize the basic embedding information for knowledge concepts to obtain enhanced embedding information; based on the enhanced embedding information, construct a multi-type user interaction graph incorporating time context information, and the multi-type user interaction graph records a variety of interaction behaviors between users and knowledge concepts at different time points; use a graph neural network to transmit messages in the multi-type user interaction graph, update the node embeddings of users and knowledge concepts, and obtain update information of each enhanced embedding information as target embedding information; use a semantic attention module to weightedly fuse target embedding information from different sources to obtain fused embedding information; use an extended matrix decomposition model and fused embedding information to predict user preferences for knowledge concepts and generate recommendation information, and realize semantic extraction based on heterogeneous information networks and multi-type interaction data of users, so as to obtain accurate recommendations based on user preferences, thereby improving the accuracy of knowledge recommendation on the online education platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0046] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0047] Figure 2 It is a flow chart of an embodiment of the knowledge recommendation method of the online education platform of the present application;

[0048] Figure 3 This is a structural example diagram of a meta-path encoder of the present application;

[0049] Figure 4 is a structural diagram of an embodiment of a knowledge recommendation device for an online education platform according to the present application;

[0050] Figure 5 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 ,like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0055] Users can use terminal devices 101 , 102 , 103 to interact with server 105 via network 104 to receive or send messages, etc.

[0056] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0057] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0058] It should be noted that the knowledge recommendation method of the online education platform provided in the embodiment of the present application is executed by a server, and accordingly, the knowledge recommendation device of the online education platform is arranged in the server.

[0059] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. The terminal devices 101, 102, 103 in the embodiment of the present application may specifically correspond to application systems in actual production.

[0060] See also Figure 2 , Figure 2 A knowledge recommendation method for an online education platform provided by an embodiment of the present invention is shown. Figure 1 The server in is used as an example to illustrate the details as follows:

[0061] S201: Construct a heterogeneous information network of the online education platform, and use a meta-path-based random walk algorithm in the heterogeneous information network to dynamically generate semantic embeddings of knowledge concepts and obtain basic embedding information.

[0062] In a specific implementation, in step S201, in a heterogeneous information network, a random walk algorithm based on a meta-path is used to dynamically generate semantic embeddings of knowledge concepts, and the basic embedding information obtained includes:

[0063] A meta-path-based random walk is used to sample heterogeneous information networks and filter out nodes of the same type, and each group of nodes of the same type is formed into a node sequence;

[0064] The skip-gram model is used to project the node sequence into a low-dimensional space to obtain the distributed representation of the nodes as the basic embedding information.

[0065] Specifically, Figure 3 As shown, Figure 3 This is a schematic diagram of a specific meta-path encoder structure of this embodiment. This embodiment considers a heterogeneous information network G = {V, E}, uses a meta-path-based random walk to sample the heterogeneous information network and filter out node sequences of the same type. The walk strategy is shown in formula (1):

[0066]

[0067] In the above formula, represents the node visited in the i-th step of the random walk, t represents the type of the node, and x i+1 Represents the node to be visited next. represent The probability calculation function P is used to calculate the neighbor nodes of type t+1 from node Walk to node x i+1 probability.

[0068] Furthermore, after obtaining the random walk path, the nodes of the same type are combined into a node sequence, and then the node sequence is projected into a low-dimensional space using the skip-gram model to learn the distributed representation of the nodes. Specifically, the optimization goal of the skip-gram model is Σ c∈C(x) logP(c t |θ(x)), where x represents the current node, X represents all nodes in the graph, and t represents the node

[0069] type, T represents all node types, C(x) represents the node sequence obtained by random walk, and θ represents the mapping function. By applying this function to each entity in the sequence, we can get the embedding h of the entity under the meta-path p p .

[0070] S202: Using a neural network to enhance and optimize the knowledge concepts of the basic embedded information to obtain enhanced embedded information.

[0071] In a specific implementation, in step S202, a neural network is used to enhance and optimize the knowledge concept of the basic embedded information, and the enhanced embedded information obtained includes:

[0072] Use graph convolutional networks to aggregate neighbor node information of basic embedding information to generate auxiliary embedding representations;

[0073] A graph attention network is used to aggregate neighbor node information of the basic embedding information to generate an enhanced embedding representation;

[0074] The auxiliary embedding representation, the enhanced embedding representation and the basic embedding information are concatenated and fused respectively to obtain first enhanced embedding information and second enhanced embedding information, and the first enhanced embedding information and the second enhanced embedding information are used as enhanced embedding information.

[0075] Specifically, this embodiment uses a two-stage meta-path embedding enhancement module to optimize the embedding representation.

[0076] In the first stage, a single-layer GCN is used to aggregate the neighbors of the nodes to generate another set of auxiliary embeddings to enrich the number of meta-path embeddings. The adjacency matrix M is defined to describe the connection relationship between nodes in the context node set previously obtained by random walks. In order to consider self-connection, M is added with an identity matrix: M = M + I, and M is normalized using the degree matrix Δ: Finally, the generation function of auxiliary embedding is shown in formula (2):

[0077]

[0078] in and are two trainable weights, h p Represents the original meta-path embedding.

[0079] In the second stage, the graph attention network is used to further aggregate the neighbor information of the node to more comprehensively enhance and capture the node features. Taking item v as an example, the embedding of item v under meta-path l is The attention scores of its neighbor nodes are: η (l)T represents the attention parameter under meta-path l, W (l) is a trainable weight matrix. After calculating the attention scores of all neighbor nodes, it is normalized as shown in formula (3):

[0080]

[0081] After obtaining the normalized attention scores, a multi-head attention mechanism is used to calculate the new features of item v, as shown in formula (4):

[0082]

[0083] Among them, σ is a nonlinear function, [W p ] k is a trainable weight matrix under attention head k. After the calculation is completed, the results under all heads are concatenated. In addition, in order to preserve the information of the original embedding, the original embedding and the embedding processed by GAT are concatenated to obtain the final embedding: The auxiliary embedding obtained in the first stage is also processed in a similar way to obtain the enhanced

[0084] S203: Based on the enhanced embedded information, a multi-type user interaction graph incorporating time context information is constructed, where the multi-type user interaction graph records various interaction behaviors between users and knowledge concepts at different time points.

[0085] In this embodiment, a multi-type interaction graph incorporating time context information is constructed, and a graph neural network is used to perform message passing to update node embeddings to obtain a deep embedding representation that carries time series and interaction type information.

[0086] In a specific implementation, in step S203, constructing a multi-type user interaction graph incorporating temporal context information based on enhanced embedding information includes:

[0087] Construct the initial interaction graph G m =(V m ,E m ), if user u and item v interact under the kth interaction type, then u and v k There is an edge between them, so V m =U∪V',v k ∈V', U represents all users, V' is the item with interaction type considered, where the initial interaction graph G m There are (I+J×K) vertices in , where I and J represent the number of users and items respectively, and K represents the number of interaction types;

[0088] If user u has an item v k The interaction time is It is mapped to a single time slot The sine function is used to generate the relative time embedding of the interaction, and a multi-type user interaction graph with temporal context information is obtained.

[0089] Specifically, construct a multi-type interaction graph G m =(V m ,E m ). If user u and item v interact under the kth interaction type, then u and v k There is an edge between them, so V m =U∪V',v k ∈V', U represents all users, and V' is the item with interaction type considered. Multi-type interaction graph G m There are (I+J×K) vertices in , where I and J represent the number of users and items respectively, and K represents the number of interaction types.

[0090] At the same time, a temporal context encoding scheme is introduced so that the model can dynamically integrate the interaction between users and items into the message aggregation architecture. Specifically, suppose user u has an item v k The interaction time is Map it to a single time slot Then a sine function is used to generate the relative time embedding of the interaction. As shown in formula (5):

[0091]

[0092] In formula (5), (2i) and (2i+1) represent the even and odd position indexes in the time embedding, respectively.

[0093] S204: Message passing is performed in a multi-type user interaction graph through a graph neural network to update the node embeddings of users and knowledge concepts, and update information of each enhanced embedding information is obtained as the target embedding information.

[0094] Specifically, after constructing the multi-type interaction graph with time context information, the following paradigm is stipulated: k Messages are passed between them, as shown in formula (6):

[0095]

[0096] In this formula, φ(·) represents a feature of the input and h u The function that performs the encoding. is an attenuation factor that normalizes the propagation effect according to the node degree and can be defined as: Where N u and They are nodes u and v respectively k Therefore, from the object v k The message delivery paradigm to user u can be further defined as shown in formula (7):

[0097]

[0098] Assign user u to item v k The relative time embedding of the interaction between user u and item v k In the message passing paradigm between, as shown in formula (8), we can get:

[0099]

[0100] Among them, l represents the neural network layer, Represents node u iTherefore, the message passing paradigm can be further defined as shown in formula (9):

[0101]

[0102] in is a trainable weight matrix, represents element-by-element addition. Finally, the results of each layer of the neural network are concatenated to generate the final user u and item v k The multi-type interaction embedding of is shown in formula (10):

[0103]

[0104] Among them, the summary of item v is Generated by a gating mechanism to distinguish the importance of different types of interactions.

[0105] S205: Perform weighted fusion on target embedding information from different sources through a semantic attention module to obtain fused embedding information.

[0106] In this embodiment, after obtaining the meta-path embeddings (including original embeddings and auxiliary embeddings) and multi-type interaction embeddings of users and items, a semantic attention module is designed to calculate their weights and fuse them together.

[0107] In a specific implementation, in step S201, the target embedding information from different sources is weightedly fused by the semantic attention module to obtain the fused embedding information including:

[0108] Calculate the attention weights of the target embedding information corresponding to user u and item v, and perform normalization;

[0109] The normalized attention weights are applied to the target embedding information to obtain the fused embedding information.

[0110] Specifically, consider the obtained embedding set of user u and item v: Taking user u as an example, the attention weight of each embedding can be calculated as shown in the following formula (11):

[0111]

[0112] in and are two trainable weight matrices, is an attention bias. It is the attention weight, which reflects the importance of meta-path embedding and multi-type interaction embedding to the recommendation task. After the calculation is completed, it is normalized to obtain Finally, the final embedding of user u is calculated by formula (12):

[0113]

[0114] The final embedding e of item v v It is also calculated in a similar way.

[0115] S206: Utilize the extended matrix decomposition model and the fused embedding information to predict the user's preference for knowledge concepts and generate recommendation information.

[0116] In a specific implementation, before step S206, the knowledge recommendation method of the online education platform further includes:

[0117] Define the latent factors of user embedding and item embedding, and adjust the parameters of the initial matrix decomposition model through the optimization function;

[0118] The gradient descent method is used to train the model until convergence, and an extended matrix decomposition model is obtained.

[0119] Specifically, an extended matrix decomposition module is introduced to generate prediction results and optimize the function, which can be defined as: where e u and e v denote user embedding and item embedding respectively, x u and v Represent the latent factors of users and items respectively, γ v and γ u are two trainable parameters used to ensure that the user embedding u and item embedding v In the same space, α and β are two adjustable parameters. The final optimization function is defined as shown in formula (13):

[0120]

[0121] To prevent overfitting, L2 regularization is added to the optimization function, where Θ represents all trainable parameters in the model. The gradient descent method is used during training, by calculating partial derivatives and iteratively updating parameters in the direction of the negative gradient until the model converges.

[0122] In this embodiment, a heterogeneous information network of an online education platform is constructed, and in the heterogeneous information network, a meta-path-based random walk algorithm is used to dynamically generate semantic embeddings of knowledge concepts to obtain basic embedding information; a neural network is used to enhance and optimize the basic embedding information for knowledge concepts to obtain enhanced embedding information; based on the enhanced embedding information, a multi-type user interaction graph incorporating time context information is constructed, and the multi-type user interaction graph records various interaction behaviors between users and knowledge concepts at different time points; a graph neural network is used to transmit messages in the multi-type user interaction graph, and node embeddings of users and knowledge concepts are updated to obtain update information of each enhanced embedding information as target embedding information; a semantic attention module is used to weightedly fuse target embedding information from different sources to obtain fused embedding information; an extended matrix decomposition model and fused embedding information are used to predict user preferences for knowledge concepts and generate recommendation information, thereby realizing semantic extraction based on heterogeneous information networks and multi-type interaction data of users, thereby obtaining accurate recommendations based on user preferences, and improving the accuracy of knowledge recommendations on the online education platform.

[0123] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0124] Figure 4 The principle block diagram of the knowledge recommendation device of the online education platform corresponding to the knowledge recommendation method of the online education platform in the above embodiment is shown. Figure 4 As shown, the knowledge recommendation device of the online education platform includes an information extraction module 31, an information enhancement module 32, an interaction graph construction module 33, an information update module 34, an information fusion module 35 and a knowledge recommendation module 36. The detailed description of each functional module is as follows:

[0125] The information extraction module 31 is used to construct a heterogeneous information network of the online education platform, and dynamically generate semantic embeddings of knowledge concepts in the heterogeneous information network using a random walk algorithm based on a meta-path to obtain basic embedding information;

[0126] An information enhancement module 32 is used to enhance and optimize the knowledge concept of the basic embedded information using a neural network to obtain enhanced embedded information;

[0127] An interaction graph construction module 33 is used to construct a multi-type user interaction graph incorporating time context information based on the enhanced embedding information, wherein the multi-type user interaction graph records various interaction behaviors between users and knowledge concepts at different time points;

[0128] An information updating module 34 is used to perform message transmission in a multi-type user interaction graph through a graph neural network, update the node embeddings of users and knowledge concepts, and obtain update information of each enhanced embedding information as target embedding information;

[0129] An information fusion module 35 is used to perform weighted fusion on target embedding information from different sources through a semantic attention module to obtain fused embedding information;

[0130] The knowledge recommendation module 36 is used to predict the user's preference for knowledge concepts and generate recommendation information by using the extended matrix decomposition model and fusion embedding information.

[0131] Optionally, the information extraction module 31 includes:

[0132] A node screening unit is used to sample the heterogeneous information network using a random walk based on a meta-path and screen out nodes of the same type, and form each group of nodes of the same type into a node sequence;

[0133] The node projection unit is used to project the node sequence into a low-dimensional space using a skip-gram model to obtain a distributed representation of the node as basic embedding information.

[0134] Optionally, the information enhancement module 32 includes:

[0135] A first aggregation unit, used to aggregate neighbor node information of basic embedding information using a graph convolutional network to generate an auxiliary embedding representation;

[0136] The second aggregation unit is used to aggregate neighbor node information of the basic embedding information using a graph attention network to generate an enhanced embedding representation;

[0137] The fusion enhancement unit is used to splice and fuse the auxiliary embedding representation, the enhanced embedding representation and the basic embedding information respectively to obtain the first enhanced embedding information and the second enhanced embedding information, and use the first enhanced embedding information and the second enhanced embedding information as the enhanced embedding information.

[0138] For the specific definition of the knowledge recommendation device of the online education platform, please refer to the definition of the knowledge recommendation method of the online education platform above, which will not be repeated here. Each module in the above-mentioned knowledge recommendation device of the online education platform can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0139] To solve the above technical problems, the present application also provides a computer device. Figure 5 , Figure 5This is a basic structural block diagram of the computer device in this embodiment.

[0140] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components connected to the memory 41, the processor 42, and the network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0141] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0142] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D interface display memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as the program code of the knowledge recommendation method of the online education platform, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0143] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code of the knowledge recommendation method of the online education platform.

[0144] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0145] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores an interface display program, and the interface display program can be executed by at least one processor so that the at least one processor performs the steps of the knowledge recommendation method of the online education platform as described above.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0147] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A knowledge recommendation method for an online education platform, characterized in that: include: Constructing a heterogeneous information network of an online education platform, and using a meta-path-based random walk algorithm in the heterogeneous information network to dynamically generate semantic embeddings of knowledge concepts to obtain basic embedding information; Using a neural network to enhance and optimize the knowledge concept of the basic embedded information to obtain enhanced embedded information; Based on the enhanced embedding information, construct a multi-type user interaction graph incorporating time context information, wherein the multi-type user interaction graph records a variety of interaction behaviors between users and knowledge concepts at different time points; By using a graph neural network to pass messages in the multi-type user interaction graph, the node embeddings of users and knowledge concepts are updated to obtain updated information of each enhanced embedding information as the target embedding information; The target embedding information from different sources is weightedly fused through the semantic attention module to obtain fused embedding information; The extended matrix factorization model and the fused embedding information are used to predict user preferences for knowledge concepts and generate recommendation information.

2. The knowledge recommendation method of the online education platform according to claim 1, characterized in that: In the heterogeneous information network, a random walk algorithm based on a meta-path is used to dynamically generate semantic embeddings of knowledge concepts, and basic embedding information obtained includes: A meta-path-based random walk is used to sample heterogeneous information networks and filter out nodes of the same type, and each group of nodes of the same type is formed into a node sequence; The node sequence is projected into a low-dimensional space using a skip-word model to obtain a distributed representation of the node as the basic embedding information.

3. The knowledge recommendation method of the online education platform according to claim 1, characterized in that: The method of using a neural network to enhance and optimize the knowledge concept of the basic embedded information to obtain enhanced embedded information includes: Aggregating neighbor node information of the basic embedding information using a graph convolutional network to generate an auxiliary embedding representation; A graph attention network is used to aggregate neighbor node information of the basic embedding information to generate an enhanced embedding representation; The auxiliary embedding representation, the enhanced embedding representation and the basic embedding information are spliced ​​and fused respectively to obtain first enhanced embedding information and second enhanced embedding information, and the first enhanced embedding information and the second enhanced embedding information are used as the enhanced embedding information.

4. The knowledge recommendation method of the online education platform according to claim 1, characterized in that: The step of constructing a multi-type user interaction graph incorporating temporal context information based on the enhanced embedding information includes: Construct the initial interaction graph G m =(V m ,E m ), if user u and item v interact under the kth interaction type, then u and v k There is an edge between them, so V m =U∪V',v k ∈V', U represents all users, V' is the item with interaction type considered, where the initial interaction graph G m There are (I+J×K) vertices in , where I and J represent the number of users and items respectively, and K represents the number of interaction types; If user u has an item v k The interaction time is It is mapped to a single time slot The sine function is used to generate the relative time embedding of the interaction, and a multi-type user interaction graph with temporal context information is obtained.

5. The knowledge recommendation method of the online education platform according to claim 1, characterized in that: The target embedding information from different sources is weightedly fused by the semantic attention module to obtain fused embedding information including: Calculate the attention weights of the target embedding information corresponding to user u and item v, and perform normalization; The normalized attention weight is applied to the target embedding information to obtain the fused embedding information.

6. The knowledge recommendation method of the online education platform according to claim 1, characterized in that: The knowledge recommendation method of the online education platform also includes: Define the latent factors of user embedding and item embedding, and adjust the parameters of the initial matrix decomposition model through the optimization function; The model is trained using a gradient descent method until convergence, thereby obtaining the extended matrix decomposition model.

7. A knowledge recommendation device for an online education platform, characterized in that: include: An information extraction module is used to construct a heterogeneous information network of an online education platform, and in the heterogeneous information network, a random walk algorithm based on a meta-path is used to dynamically generate semantic embeddings of knowledge concepts to obtain basic embedding information; An information enhancement module, used to enhance and optimize the knowledge concept of the basic embedded information using a neural network to obtain enhanced embedded information; An interaction graph construction module, used to construct a multi-type user interaction graph incorporating time context information based on the enhanced embedding information, wherein the multi-type user interaction graph records a variety of interaction behaviors between users and knowledge concepts at different time points; An information updating module is used to perform message transmission in the multi-type user interaction graph through a graph neural network, update the node embedding of users and knowledge concepts, and obtain update information of each enhanced embedding information as target embedding information; The information fusion module is used to perform weighted fusion of target embedding information from different sources through the semantic attention module to obtain fused embedding information; The knowledge recommendation module is used to predict the user's preference for knowledge concepts and generate recommendation information by using the extended matrix decomposition model and the fused embedding information.

8. The knowledge recommendation device for an online education platform as claimed in claim 7, characterized in that: The information extraction module comprises: A node screening unit is used to sample the heterogeneous information network using a random walk based on a meta-path and screen out nodes of the same type, and form each group of nodes of the same type into a node sequence; The node projection unit is used to project the node sequence into a low-dimensional space using a skip-word model to obtain a distributed representation of the node as the basic embedding information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the knowledge recommendation method for the online education platform as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the knowledge recommendation method for an online education platform as described in any one of claims 1 to 6 is implemented.

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