Personalized teaching resource recommendation method and system for large-scale users

Abstract

The present application provides a personalized teaching resource recommendation method and system for large-scale users. The method includes: constructing a dynamic interactive network; extracting teaching resources that users are already interested in and teaching resources that users are about to be interested in from the dynamic interactive network; establishing a multi-layer interest graph for each user based on the teaching resources that users are already interested in and the teaching resources that users are about to be interested in; determining a personalized teaching resource recommendation list for each user based on the multi-layer interest graph of each user, performing a comparative analysis of the local teaching resources of interest and the global teaching resources of interest in the personalized teaching resource recommendation list, and optimizing the multi-layer interest graph based on the comparative results; and co-training the dynamic interactive network on multiple edge devices through federated learning to optimize the network structure of the dynamic interactive network. The technical solution provided by the present application improves the accuracy of personalized teaching resource recommendations for large-scale users.

Claims

1. A personalized teaching resource recommendation method for large-scale users, characterized in that: include: Based on the historical interaction behavior data between large-scale users and teaching resources, a dynamic interaction network is constructed using a quantum annealing algorithm, where the number of large-scale users exceeds a first preset number threshold; Extracting teaching resources that users have been interested in and teaching resources that users are expected to be interested in from the dynamic interactive network, and establishing a multi-layer interest graph for each user based on the teaching resources that users have been interested in and the teaching resources that users are expected to be interested in; Determine a personalized teaching resource recommendation list for each user based on the multi-layer interest graph of each user, compare and analyze the local teaching resources of interest and the global teaching resources of interest in the personalized teaching resource recommendation list, and optimize the multi-layer interest graph based on the comparison results; The dynamic interaction network is collaboratively trained on multiple edge devices through federated learning to optimize the network structure of the dynamic interaction network; The step of establishing a multi-layer interest graph for each user based on the teaching resources that the user has been interested in and the teaching resources that the user is to be interested in includes: Extracting the features of the teaching resources that the user has been interested in and the teaching resources that the user will be interested in at each layer from the teaching resource metadata; According to the characteristics of the teaching resources that the user has been interested in and the teaching resources that the user is about to be interested in at each layer, combined with the weight of the teaching resource type and the weight of the layer, a multi-layer interest map of each user is established, wherein the multiple layers include a macro layer, a meso layer and a micro layer, the macro layer reflects the fields and subjects that the user is interested in, the meso layer reflects the knowledge points and topics that the user is interested in, and the micro layer reflects the resource characteristics and user behavior characteristics that the user is interested in, the resource characteristics include distributors, release time and content characteristics, different teaching resource types correspond to different weights, and different layers correspond to different weights; Determining a personalized teaching resource recommendation list for each user based on the multi-layer interest graph of each user includes: For each user, the fields and subjects of teaching resources that the user is not interested in are converted into a first vector group, and the fields and subjects that the user is interested in are converted into a second vector group, and the cosine similarity is used to calculate the interest similarity at the macro level; The knowledge points and topics of the teaching resources that the user is not interested in are converted into a third vector group, and the knowledge points and topics that the user is interested in are converted into a fourth vector group, and the cosine similarity is used to calculate the interest similarity of the meso-level; The resource features and user behavior features of the teaching resources that the user is not interested in are converted into a fifth vector group, the resource features and user behavior features that the user is interested in are converted into a sixth vector group, and the cosine similarity is used to calculate the interest similarity of the micro-level; According to the interest similarity at the macro level, the interest similarity at the meso level, and the interest similarity at the micro level, multiple candidate teaching resources are screened out, and the candidate teaching resources are teaching resources that the user is not interested in that meet the similarity design rules corresponding to the teaching scenario; The multiple candidate teaching resources are sorted, and a personalized teaching resource recommendation list for the user is generated according to the sorting result.

2. The method according to claim 1, characterized in that The historical interaction behavior data includes: user identifier, user personal information, user preference information, interaction mode, teaching resource identifier, teaching resource metadata, teaching resource format and user feedback information; The method uses quantum annealing algorithm to construct a dynamic interaction network based on historical interaction behavior data between large-scale users and teaching resources, including: Define nodes and edges, a node is a user node or a teaching resource node, the attribute information of the user node includes: user identifier, user personal information and user preference information, the attribute information of the teaching resource node includes: teaching resource identifier, teaching resource metadata and teaching resource format, the information of an edge includes the similarity between users, the similarity between teaching resources, or the association between users and teaching resources, the association includes the interaction mode and user feedback information, the attribute information of the node and the information of the edge are mapped to the Ising model, the stable state of the Ising model is determined by using the quantum annealing algorithm, and the stable state of the Ising model is used as the initial user and teaching resource association graph; When new interactive behavior data appears, determine the content to be updated and the teaching scene to which the content to be updated belongs, determine the update strategy corresponding to the teaching scene in combination with the scene characteristics of the teaching scene and the update content, and update the initial user and teaching resource association map according to the update strategy corresponding to the teaching scene to obtain an updated user and teaching resource association map; Structural optimization is performed on the updated user-teaching resource association graph to obtain an optimized user-teaching resource association graph; According to preset evaluation indicators, the optimized user-teaching resource association graph is cross-validated, and the user-teaching resource association graph that passes the cross-validation is determined as a dynamic interaction network. The preset evaluation indicators include recommendation accuracy and recall rate.

3. The method according to claim 2, characterized in that The teaching scenario includes at least one of the following: an exam preparation scenario, an independent learning scenario, and an online classroom scenario; The initial user-teaching resource association graph is updated according to the update strategy corresponding to the teaching scenario to obtain an updated user-teaching resource association graph, including at least one of the following: Filter out key update data for the test preparation scenario from the content to be updated, and based on the key update data for the test preparation scenario, in combination with a first preset annealing time and a first preset Hamiltonian strength, use a batch processing and incremental update mechanism to update the attribute information of the nodes and the edge information in the initial user-teaching resource association graph, use natural language processing technology to perform sentiment analysis on the comment content in the newly added interactive behavior data, and adjust the edge weight in the corresponding edge information according to the sentiment analysis result, so as to obtain an updated user-teaching resource association graph; Filtering non-critical update data of the autonomous learning scenario from the content to be updated, and periodically updating the attribute information of the nodes and the edge information in the initial user-teaching resource association graph according to the non-critical update data of the autonomous learning scenario, in combination with a second preset annealing time and a second preset Hamiltonian strength, to obtain an updated user-teaching resource association graph; the second preset annealing time is greater than the first preset annealing time, and the second preset Hamiltonian strength is less than the first preset Hamiltonian strength; Filter out key update data and non-key update data of the online classroom scene from the content to be updated; based on the key update data of the online classroom scene, in combination with the first preset annealing time and the first preset Hamiltonian strength, use batch processing and incremental update mechanism to update the attribute information of the nodes and edge information in the initial user-teaching resource association graph; use natural language processing technology to perform sentiment analysis on the comment content in the newly added interactive behavior data, and adjust the edge weight in the corresponding edge information according to the sentiment analysis result to obtain an adjusted association graph; based on the non-key update data of the online classroom scene, in combination with the second preset annealing time and the second preset Hamiltonian strength, periodically update the adjusted association graph to obtain an updated user-teaching resource association graph; Among them, the critical update data includes: update data whose number of included update items exceeds a second preset number threshold, and / or update data of nodes whose number of affected nodes exceeds a third preset number threshold; the non-critical update data is other update data in the content to be updated except the critical update data. In different teaching scenarios, the value of the second preset number threshold is different, and the value of the third preset number threshold is different.

4. The method according to claim 2, characterized in that: The structural optimization of the updated user-teaching resource association graph to obtain an optimized user-teaching resource association graph includes: Using density-based clustering algorithms, identify sparse and dense areas in the updated user-teaching resource association graph; Merge nodes in sparse areas, supplement edges in sparse areas, prune edges in dense areas, and use community detection algorithms to divide dense areas into communities to obtain an intermediate association graph. The intermediate association graph is mapped to four-dimensional space-time, and abnormal nodes and abnormal edges in the intermediate association graph are detected in the four-dimensional space-time based on the lens effect and the user life cycle, and abnormal processing is performed on the abnormal nodes and abnormal edges to obtain an optimized user and teaching resource association graph, and the abnormal processing is adjustment or deletion.

5. The method according to claim 4, characterized in that The method of using a density-based clustering algorithm to identify sparse areas and dense areas in the updated user-teaching resource association graph includes: Initializing parameters of a density-based clustering algorithm, including a neighborhood radius and a minimum number of neighbors; Traverse each node in the updated user-teaching resource association graph, calculate the number of neighbor nodes in the domain determined by the domain radius, and if the number of neighbor nodes is greater than or equal to the minimum number of neighbors, determine the node as a core node, merge the core node and its reachable neighbor nodes into a cluster, evaluate the cluster, and determine whether the cluster is a dense area based on the cluster evaluation result; Alternatively, if the number of neighbor nodes is less than a preset node number threshold, the node is determined to be a boundary point or a noise point. The noise point is not a core node and is a node that is not reachable by any core node. The sparse area is determined based on the location distribution of the noise point and the minimum number of neighbors.

6. A personalized teaching resource recommendation system for large-scale users, characterized in that: include: A construction module is used to construct a dynamic interaction network using a quantum annealing algorithm based on historical interaction behavior data between large-scale users and teaching resources, where the number of large-scale users exceeds a first preset number threshold; An extraction and establishment module, used to extract teaching resources that users have been interested in and teaching resources that users are expected to be interested in from the dynamic interactive network, and establish a multi-layer interest graph for each user based on the teaching resources that users have been interested in and the teaching resources that users are expected to be interested in; Determine a comparison module, which is used to determine a personalized teaching resource recommendation list for each user according to the multi-layer interest map of each user, compare and analyze the local teaching resources of interest and the global teaching resources of interest in the personalized teaching resource recommendation list, and optimize the multi-layer interest map based on the comparison results; A training optimization module, used for collaboratively training the dynamic interaction network on multiple edge devices through federated learning to optimize the network structure of the dynamic interaction network; The step of establishing a multi-layer interest graph for each user based on the teaching resources that the user has been interested in and the teaching resources that the user is to be interested in includes: Extracting the features of the teaching resources that the user has been interested in and the teaching resources that the user will be interested in at each layer from the teaching resource metadata; According to the characteristics of the teaching resources that the user has been interested in and the teaching resources that the user is about to be interested in at each layer, combined with the weight of the teaching resource type and the weight of the layer, a multi-layer interest map of each user is established, wherein the multiple layers include a macro layer, a meso layer and a micro layer, the macro layer reflects the fields and subjects that the user is interested in, the meso layer reflects the knowledge points and topics that the user is interested in, and the micro layer reflects the resource characteristics and user behavior characteristics that the user is interested in, the resource characteristics include distributors, release time and content characteristics, different teaching resource types correspond to different weights, and different layers correspond to different weights; Determining a personalized teaching resource recommendation list for each user based on the multi-layer interest graph of each user includes: For each user, the fields and subjects of teaching resources that the user is not interested in are converted into a first vector group, and the fields and subjects that the user is interested in are converted into a second vector group, and the cosine similarity is used to calculate the interest similarity at the macro level; The knowledge points and topics of the teaching resources that the user is not interested in are converted into a third vector group, and the knowledge points and topics that the user is interested in are converted into a fourth vector group, and the cosine similarity is used to calculate the interest similarity of the meso-level; The resource features and user behavior features of the teaching resources that the user is not interested in are converted into a fifth vector group, the resource features and user behavior features that the user is interested in are converted into a sixth vector group, and the cosine similarity is used to calculate the interest similarity of the micro-level; According to the interest similarity at the macro level, the interest similarity at the meso level, and the interest similarity at the micro level, multiple candidate teaching resources are screened out, and the candidate teaching resources are teaching resources that the user is not interested in that meet the similarity design rules corresponding to the teaching scenario; The multiple candidate teaching resources are sorted, and a personalized teaching resource recommendation list for the user is generated according to the sorting result.

7. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a personalized teaching resource recommendation method for large-scale users as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a personalized teaching resource recommendation method for large-scale users as described in any one of claims 1 to 5 is implemented.

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