CN114722269A - Article recommendation method and device based on graph neural network and storage medium - Google Patents
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Abstract
In the article recommendation method, the article recommendation device and the storage medium based on the graph neural network, user information, article information and user-article interaction information are obtained, a directed network graph between a user and an article is constructed by using the user information, the article information and the user-article interaction information, an ApeGNN model based on the graph neural network is constructed, and the ApeGNN model is trained by using the directed network graph to obtain a target ApeGNN model. The method comprises the steps of obtaining information of an article to be recommended of each user, obtaining a prediction score of the article to be recommended by using a target ApeGNN model, determining a sorting result according to the prediction score, and outputting recommended article information corresponding to each user according to the sorting result. According to the method and the device, semantic differences of the user and the article in each layer are considered, so that the article recommendation accuracy is improved.
Description
Technical Field
The present application relates to the field of data recommendation technologies, and in particular, to an article recommendation method and apparatus based on a graph neural network, and a storage medium.
Background
With the rapid development of e-commerce and social media platforms, recommendation systems have become indispensable tools for enterprises to improve profits. At present, the graph neural network has achieved symbolic success on the recommendation system, and particularly, the graph neural network aggregates neighbor information on a graph structure through a message-passing process.
In the related art, the recommended models based on the graph neural network include the following:
the method comprises the following steps: and applying information propagation operation on the user-item bipartite graph, and recommending the possibly interested items for the user through the historical interaction records of the user.
The second method comprises the following steps: on the user-item bipartite graph, based on the recommendation model of the graph neural network, the sum-of-embedding average of each layer is taken as the characterization of the last layer in the pooling stage.
However, in method one, the user-item bipartite graph is a special type of graph, and the edges on the bipartite graph only have interaction information between the user and the item, and no edges exist between the user and the user or between the item and the item. In the second method, the semantic difference between the user and the article is ignored in the aggregation stage, and the importance of embedding different layers is not distinguished, so that the article recommendation accuracy is low.
Disclosure of Invention
The application provides an article recommendation method, an article recommendation device and a storage medium based on a graph neural network, which are used for at least solving the technical problem that the article recommendation accuracy is low due to the fact that semantic differences between users and articles are ignored in the related technology.
An embodiment of a first aspect of the present application provides an article recommendation method based on a graph neural network, including:
acquiring user information, article information and user-article interaction information;
constructing a directed network graph between the user and the object by using the user information, the object information and the user-object interaction information;
an ApeGNN model based on a graph neural network is built, and the ApeGNN model is trained by utilizing the directed network graph to obtain a target ApeGNN model;
acquiring information of articles to be recommended of each user;
and obtaining the prediction score of the item to be recommended by using the target ApeGNN model, determining an ordering result according to the prediction score, and outputting the recommended item information corresponding to each user according to the ordering result.
The embodiment of the second aspect of the present application provides an article recommendation device based on a graph neural network, including:
the acquisition module is used for acquiring user information, article information and user-article interaction information;
the building module is used for building a directed network graph between the user and the article by utilizing the user information, the article information and the user-article interaction information;
the training module is further used for constructing an ApeGNN model based on a neural network of a graph, and training the ApeGNN model by using the directed network graph to obtain a target ApeGNN model;
the acquisition module is also used for acquiring the information of the articles to be recommended by each user;
and the output module is used for obtaining the prediction scores of the to-be-recommended articles by using the target ApeGNN model, determining an ordering result according to the prediction scores, and outputting the recommended article information corresponding to each user according to the ordering result.
A non-transitory computer-readable storage medium according to an embodiment of a third aspect of the present application, where the non-transitory computer-readable storage medium stores a computer program; which when executed by a processor implements the method as shown in the first aspect above.
A computer device according to an embodiment of a fourth aspect of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to the first aspect is implemented.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
in the article recommendation method, the article recommendation device and the storage medium based on the graph neural network, user information, article information and user-article interaction information are obtained, a directed network graph between a user and an article is constructed by using the user information, the article information and the user-article interaction information, an ApeGNN model based on the graph neural network is constructed, the ApeGNN model is trained by using the directed network graph to obtain a target ApeGNN model, article information to be recommended of each user is obtained, a prediction score of the article to be recommended is obtained by using the target ApeGNN model, a sorting result is determined according to the prediction score, and recommended article information corresponding to each user is output according to the sorting result. According to the method and the device, the user and the article are taken as different types in the aggregation stage, and the graph convolution in the self-adaptive pooling layer configures different weights for different layers, so that the semantic difference of the user and the article in each layer is considered, and the article recommendation accuracy is improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of an item recommendation method based on a graph neural network according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an article recommendation device based on a graph neural network according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present application and should not be construed as limiting the present application.
An article recommendation method and apparatus based on a graph neural network according to an embodiment of the present application are described below with reference to the drawings.
Example one
Fig. 1 is a schematic flowchart of an item recommendation method based on a graph neural network according to an embodiment of the present application, and as shown in fig. 1, the method may include:
In an embodiment of the present invention, user information, item information, and user-item interaction information may be obtained from a transaction data set. And, the user information may include the user's ID, gender, age occupation, etc. for subsequent selection of the user's neighbor set using the user information. The item information may include an ID, type, price, etc. of the item for subsequent selection of a neighbor set of the item using the item information.
And, in the embodiment of the present invention, the user-item interaction information may include transaction information of the user and the item, and click browsing information of the user and the item.
And 102, constructing a directed network graph between the user and the article by utilizing the user information, the article information and the user-article interaction information.
For example, in the embodiment of the present invention, a directed network graph G between a user and an item is constructed as (U, V, R), where U represents a set of | U | user nodes, V represents a set of | V | item nodes, and R represents a set of interaction records between the user and the item, i.e., | R | edges. And uiRepresents a user node, vjRepresents an item node, r (u)ivj) Representing two nodes uiAnd vjEdge between if two nodes uiAnd vjWith interactive recording, r (u)ivj) Is 1, otherwise r (u)ivj) Is 0.
And 103, constructing an ApeGNN model based on the neural network of the graph, and training the ApeGNN model by using the directed network graph to obtain a target ApeGNN model.
Wherein, in an embodiment of the present invention, the ApeGNN model treats the user and the item as different types during the aggregation phase. And, in an embodiment of the present invention, the ApeGNN model may include, in order, an adaptive aggregation layer, an adaptive high-dimensional propagation layer, an adaptive pooling layer, and a prediction layer.
In an embodiment of the present invention, the method for training the ApeGNN model by using the directed net graph to obtain the target ApeGNN model may include the following steps:
step a, performing initialization embedding on all users and all articles according to the directed network graph to obtain an initialization embedding representation of the ith userInitialized embedded representation of jth item
In the embodiment of the present invention, the embedded dimension is d, and m and n are the numbers of users and articles, respectively.
And b, respectively sequentially passing the user initialization embedded representation and the article initialization embedded representation through the self-adaptive aggregation layer, the self-adaptive high-dimensional propagation layer, the self-adaptive pooling layer and the prediction layer to obtain final prediction scores.
In an embodiment of the invention, when the user-initialized embedded representation and the article-initialized embedded representation pass through the adaptive aggregation layer, the adaptive aggregation layer embeds the representation to the userAnd article embedded representationPerforming graph convolution operation by using embedded first aggregation function to obtain user embedded representationAnd article embedded representationAnd the first aggregation function is AGG1The first aggregation function is to aggregate the neighbor item sets of the users to obtain the representations of the users, and aggregate the neighbor user sets of the items to obtain the representations of the items, where the neighbor item sets of the users may be specifically item sets that have interacted with the users in the neighbor sets of the users, and the neighbor user sets of the items may be user sets that have interacted with the items in the neighbor sets of the items.
In particular, a user-embedded representation is obtainedAnd article embedded representationComprises the following steps:
wherein, N (u)i) Is user uiOf a neighbor set of thetauIs the only weight parameter that the user has t, L is the number of layers of the graph convolution network,andweights configured for the adaptive aggregation layer at the ith layer for the user and the item, respectively, e is a logarithmic function,d is the diagonal node degree matrix and a is the adjacency matrix that does not contain self-joins.
Further, in the embodiment of the present invention, the adaptive high-dimensional propagation layer performs a graph convolution operation using the embedded second aggregation function and the result of the adaptive aggregation layer to search for the high-dimensional connection information for embedded propagation, wherein the second aggregation function is AGG2User uiAnd an article vjThe propagation embedding at the l-th layer is:
wherein the content of the first and second substances,the number of convolution layers of the self-adaptive high-dimensional propagation layer and the self-adaptive aggregation layer is the same, and the convolution layers are L layers.
Further, in embodiments of the present invention, each layer in a graph convolution operation embeds semantic structure information that contains a different layer, based on which the embedding of each layer is handled separately in the pooling stage. Wherein, in the embodiment of the present disclosure, different weights are set for each layer to capture semantic information of each layer, so as to consider the importance of users and articles in different layers in the adaptive pooling stage.
Specifically, in the embodiment of the present disclosure, the adaptive pooling layer combines the results of the adaptive high-dimensional propagation layer to obtain the user uiAnd an article vjLast characterizationAnd
where β (u, l) is a weight configured for the l-th tier of users and β (ν, l) is a weight configured for the l-th tier of items.
Further, in embodiments of the present invention, the prediction layer will pass user uiIs characterized byAnd an article vjIs characterized byInner product is carried out to obtain the final prediction fractionAnd determining a sorting result according to the prediction score, and recommending the user according to the sorting result.
Wherein, in the embodiment of the invention, the user u is passediIs characterized byAnd an article vjIs characterized byInner product is carried out to obtain the final prediction fractionCan be as follows:
and, in one embodiment of the invention, the prediction scores may be ranked from high to low, and items ranked top (e.g., top 10) may be recommended according to the ranking results. In another embodiment of the invention, the prediction scores may be ranked from low to high, and ranked (e.g., 10 th) items may be recommended based on the ranking.
And c, optimizing the parameters of the ApeGNN model by using a Bayes personalized ranking loss function until the value of the loss function is not reduced any more, and obtaining the target ApeGNN model.
In an embodiment of the present invention, the bayesian personalized ranking loss function may be:
wherein the content of the first and second substances,are the training data in pairs of the training data,is a collection of interaction records that are,is a collection of non-interactive records and,is sigmoid function, lambda controls L2Regularization, while L2Regularization may prevent overfitting during the training process.
And, in embodiments of the present invention, the user per-layer weights and the item per-layer weights in the adaptive aggregation layer in the ApeGNN model can be optimized using a bayesian personalized ranking loss function, and the user per-layer weights and the item per-layer weights in the adaptive pooling layer in the ApeGNN model can be optimized.
And step 104, acquiring information of the articles to be recommended of each user.
And 105, obtaining a prediction score of the item to be recommended by using the target ApeGNN model, determining an ordering result according to the prediction score, and outputting recommended item information corresponding to each user according to the ordering result.
The method for recommending the articles based on the graph neural network comprises the steps of obtaining user information, article information and user-article interaction information, constructing a directed network graph between a user and the articles by utilizing the user information, the article information and the user-article interaction information, constructing an ApeGNN model based on the graph neural network, training the ApeGNN model by utilizing the directed network graph to obtain a target ApeGNN model, obtaining the information of the articles to be recommended of each user, obtaining a prediction score of the articles to be recommended by utilizing the target ApeGNN model, determining a sorting result according to the prediction score, and outputting recommended article information corresponding to each user according to the sorting result. According to the method and the device, the user and the article are taken as different types in the aggregation stage, and the graph convolution in the self-adaptive pooling layer configures different weights for different layers, so that the semantic difference of the user and the article in each layer is considered, and the article recommendation accuracy is improved.
Example two
Further, fig. 2 is a schematic structural diagram of an article recommendation device based on a graph neural network according to an embodiment of the present application, and as shown in fig. 2, the article recommendation device may include:
an obtaining module 201, configured to obtain user information, item information, and user-item interaction information;
a building module 202, configured to build a directed network graph between a user and an article by using the user information, the article information, and the user-article interaction information;
the training module 203 is further configured to construct an ApeGNN model based on a graph neural network, and train the ApeGNN model by using a directed network graph to obtain a target ApeGNN model;
the obtaining module 201 is further configured to obtain information of an item to be recommended by each user;
the output module 204 is configured to obtain a prediction score of the to-be-recommended item by using the target ApeGNN model, determine an ordering result according to the prediction score, and output recommended item information corresponding to each user according to the ordering result.
To implement the above embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium.
A non-transitory computer-readable storage medium provided by an embodiment of the present disclosure stores a computer program; the computer program, when executed by a processor, is capable of implementing the method as shown in fig. 1.
In order to implement the above embodiments, the present disclosure also provides a computer device.
The computer device provided by the embodiment of the disclosure comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor; the processor, when executing the program, is capable of implementing the method as shown in fig. 1.
In the article recommendation method, the article recommendation device and the storage medium based on the graph neural network, user information, article information and user-article interaction information are obtained, a directed network graph between a user and an article is constructed by using the user information, the article information and the user-article interaction information, an ApeGNN model based on the graph neural network is constructed, the ApeGNN model is trained by using the directed network graph to obtain a target ApeGNN model, article information to be recommended of each user is obtained, a prediction score of the article to be recommended is obtained by using the target ApeGNN model, a sorting result is determined according to the prediction score, and recommended article information corresponding to each user is output according to the sorting result. According to the method and the device, the user and the article are taken as different types in the aggregation stage, and the graph convolution in the self-adaptive pooling layer configures different weights for different layers, so that the semantic difference of the user and the article in each layer is considered, and the article recommendation accuracy is improved.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. An item recommendation method based on a graph neural network is characterized by comprising the following steps:
acquiring user information, article information and user-article interaction information;
constructing a directed network graph between the user and the object by using the user information, the object information and the user-object interaction information;
an ApeGNN model based on a graph neural network is built, and the ApeGNN model is trained by utilizing the directed network graph to obtain a target ApeGNN model;
acquiring information of articles to be recommended of each user;
and obtaining the prediction score of the item to be recommended by using the target ApeGNN model, determining an ordering result according to the prediction score, and outputting the recommended item information corresponding to each user according to the ordering result.
2. The method of claim 1, wherein the ApeGNN model comprises, in order, an adaptive aggregation layer, an adaptive high-dimensional propagation layer, an adaptive pooling layer, and a prediction layer, and wherein training the ApeGNN model with the directed net graph to obtain a target ApeGNN model comprises:
according to the directed network graph, all users and all articles are initialized and embedded to obtain an initialized and embedded representation of the ith userInitialized embedded representation of jth itemThe embedded dimensionality is d, and m and n are the numbers of users and articles respectively;
respectively sequentially passing the user initialization embedded representation and the article initialization embedded representation through a self-adaptive aggregation layer, a self-adaptive high-dimensional propagation layer, a self-adaptive pooling layer and a prediction layer to obtain final prediction scores;
and optimizing the parameters of the ApeGNN model by using a Bayes personalized ranking loss function until the value of the loss function is not reduced any more, thereby obtaining the target ApeGNN model.
3. The method as recited in claim 2, wherein the operation of the adaptive aggregation layer comprises:
the adaptive aggregation layer embeds representations for usersAnd an article embedded representationPerforming graph convolution operation by using embedded first aggregation function to obtain user embedded representationAnd article embedded representationWherein the first aggregation function is AGG1,
wherein, N (u)i) Is user uiOf a neighbor set of thetauIs the only weight parameter that the user has t, L is the number of layers of the graph convolution network,andweights configured for the adaptive aggregation layer at the ith layer for the user and the item, respectively, e is a logarithmic function,is a diagonal node degree matrixAnd A is an adjacency matrix that does not contain self-connection.
4. The method as recited in claim 2, wherein said operation of said adaptive high-dimensional propagation layer comprises:
the self-adaptive high-dimensional propagation layer performs graph convolution operation by using the embedded second aggregation function and the result of the self-adaptive aggregation layer to perform embedded propagation and exploration of high-dimensional connection information, wherein the second aggregation function is AGG2User uiAnd an article vjThe propagation embedding at the l-th layer is:
5. The method of claim 2, wherein the operation of the adaptive pooling layer comprises:
the self-adaptive pooling layer combines the results of the self-adaptive high-dimensional propagation layer to obtain a user uiAnd an article vjLast characterizationAnd
wherein β (u, l) is a weight configured for the l-th layer of users, and β (ν, l) is a weight configured for the l-th layer of articles.
6. The method of claim 2, wherein the operation of predicting the layer comprises:
7. The method of claim 2, wherein the bayesian personalized ranking loss function comprises:
wherein the content of the first and second substances,are the training data in pairs of the training data,is a collection of interaction records that are,is a collection of non-interactive records and,is sigmoid function, lambda controls L2Regularization while L2Regularization may prevent overfitting during the training process.
8. An article recommendation device based on a graph neural network is characterized by comprising the following modules:
the acquisition module is used for acquiring user information, article information and user-article interaction information;
the building module is used for building a directed network graph between the user and the article by utilizing the user information, the article information and the user-article interaction information;
the training module is further used for constructing an ApeGNN model based on a neural network of a graph, and training the ApeGNN model by using the directed network graph to obtain a target ApeGNN model;
the acquisition module is also used for acquiring the information of the articles to be recommended by each user;
and the output module is used for obtaining the prediction scores of the to-be-recommended articles by using the target ApeGNN model, determining an ordering result according to the prediction scores, and outputting the recommended article information corresponding to each user according to the ordering result.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as claimed in any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method of any one of claims 1-7.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
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CN115688907A (en) * | 2022-12-30 | 2023-02-03 | 中国科学技术大学 | Recommendation model training method based on graph propagation and recommendation method based on graph propagation |
CN116541716A (en) * | 2023-07-06 | 2023-08-04 | 深圳须弥云图空间科技有限公司 | Recommendation model training method and device based on sequence diagram and hypergraph |
CN117112915A (en) * | 2023-10-24 | 2023-11-24 | 广州美术学院 | Intelligent design method and system based on user characteristics and big data training |
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
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CN115688907A (en) * | 2022-12-30 | 2023-02-03 | 中国科学技术大学 | Recommendation model training method based on graph propagation and recommendation method based on graph propagation |
CN116541716A (en) * | 2023-07-06 | 2023-08-04 | 深圳须弥云图空间科技有限公司 | Recommendation model training method and device based on sequence diagram and hypergraph |
CN116541716B (en) * | 2023-07-06 | 2024-04-16 | 深圳须弥云图空间科技有限公司 | Recommendation model training method and device based on sequence diagram and hypergraph |
CN117112915A (en) * | 2023-10-24 | 2023-11-24 | 广州美术学院 | Intelligent design method and system based on user characteristics and big data training |
CN117112915B (en) * | 2023-10-24 | 2024-02-20 | 广州美术学院 | Intelligent design method and system based on user characteristics and big data training |
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