Article recommendation method and device, storage medium and electronic equipment

Abstract

The invention discloses an article recommendation method and device, a storage medium and electronic equipment. Relates to the field of artificial intelligence, in particular to the field of recommendation systems. Based on the initial implicit collaborative knowledge graph, constructing a plurality of embedded representations, the plurality of embedded representations including an account embedded representation, an article embedded representation and an entity embedded representation; based on the multiple embedded representations, an adaptive implicit collaborative knowledge graph is constructed, and the adaptive implicit collaborative knowledge graph is used for representing potential interests of the account on the articles and implicit preferences and associations between the articles and the entities; based on the self-adaptive implicit collaborative knowledge graph, an article recommendation model is adopted to obtain an article recommendation result corresponding to a target account, and the target account is an account in the self-adaptive implicit collaborative knowledge graph. The object recommendation method and device solve the technical problem that the object recommendation effect is poor due to large sparsity of interaction data and a knowledge graph.

Description

Technical Field

[0001] The present invention relates to the field of graph neural networks and recommendation systems, and in particular to an item recommendation method, device, storage medium and electronic device. Background Art

[0002] The recommendation system is a system that provides personalized recommendation services to users based on user information, item features, and historical interaction data. The recommendation system builds the relationship between users, items, and entities through deep learning model training, so as to perform cold start, cross-domain recommendation, and multimodal recommendation to improve the recommendation effect. In recent years, graph neural networks (graph learning) have developed rapidly. Its models can learn high-order relationships between entities, integrate entity features, build association networks, and improve the effect of recommendation systems.

[0003] The recommendation effect of the recommendation system is affected by the sparsity of user-item interaction data, user variability, product diversity, uneven distribution and cold start problems. How to improve the personalization and accuracy of recommendation results is the core issue of recommendation system research. Interaction data and knowledge graphs are often very sparse, that is, there are few edges in the graph, which makes the graph neural network unable to fully play its role. Specifically: 1) There are a large number of unobserved behaviors between users and items. However, related technologies regard these unobserved behaviors as negative feedback information, ignoring that some of these behaviors may also reflect the user's positive preferences, that is, unobserved positive feedback information. The above practices may reduce the quality of user preference modeling; 2) The incomplete user preference modeling in related technologies and the insufficient entity relationship mining in the knowledge graph lead to low accuracy of item recommendation results.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] Embodiments of the present invention provide an item recommendation method, device, storage medium, and electronic device to at least solve the technical problem of poor item recommendation effect caused by the large sparsity of interaction data and knowledge graphs.

[0006] According to one aspect of an embodiment of the present invention, a method for recommending items is provided, comprising: constructing an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent explicit interactive relationships and implicit preferences and associations between accounts, items and entities, and the entity is attribute information of the item, or association information associated with the item; based on the initial implicit collaborative knowledge graph, constructing multiple embedding representations, wherein the multiple embedding representations include account embedding representations, item embedding representations and entity embedding representations; based on the multiple embedding representations, constructing an adaptive implicit collaborative knowledge graph, wherein the adaptive implicit collaborative knowledge graph is used to represent an account's potential interest in items, and implicit preferences and associations between items and entities; based on the adaptive implicit collaborative knowledge graph, using an item recommendation model to obtain an item recommendation result corresponding to a target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

[0007] According to another aspect of an embodiment of the present invention, there is also provided an item recommendation device, including: a construction module, used to construct an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent the explicit interaction relationship and implicit preferences and associations between accounts, items and entities, and the entity is the attribute information of the item, or the association information associated with the item; a representation module, used to construct multiple embedded representations based on the initial implicit collaborative knowledge graph, wherein the multiple embedded representations include account embedded representations, item embedded representations and entity embedded representations; an association module, used to construct an adaptive implicit collaborative knowledge graph based on the multiple embedded representations, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in items, as well as the implicit preferences and associations between items and entities; a target module, used to obtain item recommendation results corresponding to a target account based on the adaptive implicit collaborative knowledge graph using an item recommendation model, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

[0008] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing any one of the item recommendation methods.

[0009] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the item recommendation methods.

[0010] In an embodiment of the present invention, an initial implicit collaborative knowledge graph is constructed, wherein the initial implicit collaborative knowledge graph is used to represent explicit interactive relationships and implicit preferences and associations between accounts, items and entities, and the entity is attribute information of the item, or association information associated with the item; based on the initial implicit collaborative knowledge graph, multiple embedding representations are constructed, wherein the multiple embedding representations include account embedding representations, item embedding representations and entity embedding representations; based on the multiple embedding representations, an adaptive implicit collaborative knowledge graph is constructed, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in the item, as well as the implicit preferences and associations between the item and the entity; based The adaptive implicit collaborative knowledge graph adopts an item recommendation model to obtain item recommendation results corresponding to the target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph. This achieves the purpose of more accurately predicting the user's potential interest in items by constructing and optimizing the implicit collaborative knowledge graph and combining explicit and implicit user preferences, thereby providing each target user with personalized and high-quality item recommendation results. This achieves the technical effect of more comprehensively capturing and utilizing the complex relationships between users, items, and entities, and improving the accuracy and diversity of recommendations, thereby solving the technical problem of poor item recommendation effects caused by the large sparsity of interaction data and knowledge graphs. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0012] Figure 1 is a flow chart of an item recommendation method according to an embodiment of the present invention;

[0013] Figure 2 is an optional model framework diagram according to an embodiment of the present invention;

[0014] Figure 3 is a schematic diagram of an optional graph neural network module according to an embodiment of the present invention;

[0015] Figure 4 is a flow chart of an optional item recommendation method according to an embodiment of the present invention;

[0016] Figure 5 is a schematic diagram of an item recommendation device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention are explained below:

[0020] Graph Neural Network (GNN) is a deep learning model for processing graph structured data. It performs tasks such as node classification, graph classification, and link prediction by learning representations on the nodes and edges of the graph. Graph neural networks can capture complex relationships between nodes and can process non-Euclidean data, which makes them widely used in social network analysis, recommendation systems, bioinformatics, and other fields.

[0021] Recommender systems refer to an important branch of computer science, artificial intelligence and data mining. They aim to provide users with personalized recommendations for goods or services by analyzing their historical behaviors, preferences and other relevant information. Recommender systems are widely used in many fields such as e-commerce, social media, online video, music and news.

[0022] Implicit Collaborative Knowledge Graph (ICKG) is a knowledge representation and reasoning framework that combines implicit collaborative filtering and knowledge graph technology. It builds a graph structure with rich semantics and relationships by mining implicit collaborative relationships between users and association information between entities (such as goods, services, content, etc.). In this graph, nodes represent entities and edges represent relationships between entities, which can be user preferences for entities, similarities between entities, category relationships, etc.

[0023] Embedding Representation refers to a technique that maps high-dimensional data (such as text, images, or sounds) to a low-dimensional space (usually Euclidean space) so that the intrinsic structure and relationship of the data are preserved. In machine learning and deep learning, embedding representation is often used for feature extraction and dimensionality reduction to better perform tasks such as classification, clustering, or similarity measurement.

[0024] According to an embodiment of the present invention, an embodiment of a method for item recommendation is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flowchart of an item recommendation method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, constructing an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent explicit interactive relationships and implicit preferences and associations between accounts, items, and entities, and the entity is attribute information of the item, or association information associated with the item;

[0027] Optionally, the implicit collaborative knowledge graph does not rely on explicit user feedback (such as ratings, comments), but infers the user's interests and preferences through the user's behavioral data (such as purchase history, browsing history). Entities and their relationships can be organized and stored by utilizing the graph structure, allowing complex queries and reasoning. By analyzing user behavior and preferences, the implicit collaborative knowledge graph can be used to provide personalized recommendation services. The introduction of the knowledge graph enables the system to understand and reason about the complex relationships between entities, improving the accuracy and relevance of recommendations. In addition, the implicit collaborative knowledge graph can add new entities and relationships to adapt to changing data and needs.

[0028] Optionally, the initial implicit collaborative knowledge graph not only contains explicit interaction data between users and items, such as purchase and browsing history, but also incorporates item attribute information and implicit relationships between items and items, and between items and entities. This integration of multi-source information can more comprehensively understand user preferences and improve the level of personalization of recommendations. By constructing an implicit collaborative knowledge graph, this embodiment can explore items and entities that users have not directly expressed but may be interested in. For example, if a user often browses a certain type of product but has never purchased it, the user's implicit preference can be inferred by analyzing the association between the user's browsing behavior and the product attributes, thereby predicting items that the user may be interested in but has not yet explored. The nodes and edges in the graph make it possible to establish high-order associations, which can capture complex multi-hop relationships between users, items, and entities. For example, a user may be interested in a certain brand of product. This preference is not only based on the product itself, but also based on the brand to which the product belongs, other brand-related products or entities (such as designers, places of origin, etc.).

[0029] Optionally, the item can be a commodity, and the corresponding entity can be the category, brand, attribute of the commodity, or other information related to the commodity, such as reviewers, places, events, etc. Entities exist in the form of nodes in the knowledge graph, connected to other entities or items through edges to express the relationship between them. The relationship between entities and items is diverse. It can be direct, such as the item belongs to a certain entity category (if the entity is a category), or indirect, such as the item and entity are implicitly associated through user behavior or recommendation context. For example, if the entity is a brand, the item can be a product under a certain brand; or if the entity is a place, the item can be an activity or service related to the place. In the knowledge graph, these relationships are represented in the form of edges, forming a complex network structure, which helps to more fully understand the characteristics of the items and the possible preferences of users.

[0030] In an optional embodiment, constructing an initial implicit collaborative knowledge graph includes: constructing multiple triples, wherein the triples include items, entities, and implicit relationships between items and entities; constructing a binary graph based on interactive operations between accounts and items; using items as a bridge to connect accounts and entities, integrating multiple triples into the binary graph to obtain an initial implicit collaborative knowledge graph.

[0031] Optional, Figure 2 It is an optional model framework diagram provided according to an embodiment of the present invention, and an initial implicit collaborative knowledge graph G can be constructed according to training samples:

[0032] 1) Organize items, entities and implicit relationships in the business system in the form of triples, and construct an item-entity implicit graph from multiple pairs of triples;

[0033] 2) Collect the interactive operation behaviors between users and items in the business system and construct a user-item binary graph;

[0034] 3) The item-entity pairs are integrated into the user-item binary graph, and the item nodes are used as bridges to connect the user nodes and the entity nodes, to construct the initial implicit collaborative knowledge graph G, and store it in the graph database; assuming that there are M user nodes, N item nodes, and T entity nodes.

[0035] Optionally, by constructing a triple containing items, entities, and implicit relationships between them, the complex associations between items and entities can be captured. These associations can be derived from the attributes, brands, categories, etc. of items, or the implicit similarities between items. This fine-grained relationship expression can provide a basis for subsequent graph construction, so that the recommendation system can not only understand the user's explicit preferences, but also explore the implicit preferences that the user may not have expressed. Based on the explicit interactive operations between users and items, such as purchases, comments, and collections, a user-item binary graph is constructed. This step can intuitively indicate which items the user is interested in and the intensity of the interest, providing direct user feedback data for subsequent graph fusion. By fusing multiple triples into the binary graph, using items as a bridge to connect users and entities, implicit preferences and explicit interaction data can be combined to form a more complete and refined implicit collaborative knowledge graph. This fusion can not only enhance the model's understanding of user preferences and item attributes, but also further enrich the multi-dimensional features of items through the associations between entities, providing more comprehensive information support for personalized recommendations.

[0036] Step S104, constructing multiple embedding representations based on the initial implicit collaborative knowledge graph, wherein the multiple embedding representations include account embedding representation, item embedding representation, and entity embedding representation;

[0037] Optionally, accounts, items, and entities can be mapped from high-dimensional feature space to low-dimensional vector space through embedding representation. This process can use deep learning technology, such as neural network, which can effectively compress information while retaining key features. The above methods can reduce computational complexity and improve model training and prediction efficiency. By constructing account embedding representation, item embedding representation, and entity embedding representation, the user's historical behavior, item attributes, and association information between entities can be integrated together, which can enrich the feature representation of each node and promote the complementarity between features, so that the model can understand user preferences and item characteristics from multiple perspectives. While retaining the initial graph structure, the embedding representation transmits and aggregates information in the graph through the propagation algorithm, enhancing the associations and relationships in the graph structure. During the learning process, the model can better utilize the structural information in the graph to capture the implicit preferences and associations between accounts, items, and entities.

[0038] In an optional embodiment, multiple embedding representations are constructed based on the initial implicit collaborative knowledge graph, including: mapping accounts, items and entities in the initial implicit collaborative knowledge graph to a vector space to obtain multiple embedding representations, wherein the multiple embedding representations are in the form of vector matrices.

[0039] Optionally, multiple embedding representations can be in the form of vector matrices. Initialize the embedding vectors of users, items, and entities: map all users, items, and entity nodes in the initial implicit collaborative knowledge graph G to the vector space, and generate multiple embedding representations to be learned by random initialization, namely, the user embedding vector matrix V u , item embedding vector matrix V i and entity embedding vector matrix V e The specific process is described as follows:

[0040]

[0041] in, and V i 0 Represent the embedded vector matrices of initialized users, items and entities respectively, represents the initialization embedding vector of user u1, represents the initialization embedding vector of item i1, Represents the initialization embedding vector of entity e1, and the dimension of the vector is D.

[0042] Optionally, the node information in the initial implicit collaborative knowledge graph can be converted into a low-dimensional vector matrix. This step is to represent non-numerical features (such as account ID, item ID, entity attributes, etc.) as numerical vectors to facilitate processing and analysis by machine learning models. In the vector space, by calculating the vector distance or similarity between nodes (i.e., accounts, items, and entities), the strength of the relationship between nodes can be quantified, including explicit interactions and implicit preferences. This quantification method helps the model understand the account's potential interest in items, as well as the implicit preferences and associations between items and entities. Constructing an embedded representation can be achieved through deep learning techniques (such as graph neural networks), which can automatically learn the feature representation of nodes, rather than just a simple mapping. Representation learning can capture the intrinsic properties of nodes and the complex relationships in the graph structure, providing high-quality feature representations for subsequent model training and prediction.

[0043] Optionally, by converting to an embedding representation of a vector matrix, the recommendation model can more accurately understand user preferences and item characteristics, thereby giving more personalized and user-friendly recommendation results. The embedding representation retains the relationship information in the graph and can understand accounts, items, and entities from multiple perspectives. The original account, item, and entity features may have high dimensions. After converting to a vector matrix, the dimension reduction can significantly reduce the computing resources and storage requirements, thereby improving the overall efficiency and responsiveness of the recommendation system. For new accounts or new items, even if there is a lack of direct interaction data, the embedding representation can use its position in the graph and the entity information associated with it to generate a preliminary feature representation, thereby alleviating the cold start problem in the recommendation system. The constructed embedding representation in the form of a vector matrix can be dynamically updated. As user behavior changes and item attributes are updated, the model can learn new embedding representations, promote the update of the adaptive implicit collaborative knowledge graph, and maintain the timeliness and relevance of the recommendation results. By learning feature representations in vector space, the model can better handle unseen accounts, items, and entities, improve the model's generalization ability to new data, and reduce the risk of overfitting.

[0044] Step S106, constructing an adaptive implicit collaborative knowledge graph based on multiple embedded representations, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in the item, and the implicit preference and association between the item and the entity;

[0045] Optionally, by constructing an adaptive implicit collaborative knowledge graph, the graph can be updated in real time or regularly according to changes in user behavior, updates to item attributes, and additions to entity association information, thereby maintaining the timeliness and accuracy of the graph and providing the recommendation system with the latest and most user-friendly data structure. By leveraging the similarity between account embedding representations and item embedding representations, the construction of an adaptive implicit collaborative knowledge graph can identify potential interests between accounts and items that are not directly observed. For example, even if a user does not directly interact with an item, if the embedding representation of the item is similar to the embedding representation of items that the user liked in the past, the system can infer that the user may be interested in the item. The introduction of entity embedding representation can further enhance the item description. Entity information covers item attributes, categories, brands, etc. This information is connected to the item node through implicit associations, so that the multi-dimensional characteristics of items can be more comprehensively considered when making item recommendations, improving the relevance and diversity of recommendations. The adaptive implicit collaborative knowledge graph not only considers the first-order relationships (direct account-item interactions), but also can establish high-order relationships through multi-hop propagation to capture deep user preferences and item associations, such as indirectly inferring the similarity between users or the correlation between items through multiple entities (brands, types) that users like in common.

[0046] Optionally, the adaptive implicit collaborative knowledge graph can more accurately understand the user's interests and item characteristics by mining implicit preferences and high-order relationships, thereby generating more accurate recommendation results, reducing recommendations that the user is not interested in, and improving user satisfaction and retention. When user-item interaction data is sparse, by building an adaptive implicit collaborative knowledge graph, entity information and implicit preferences can be used to supplement the feature representation of users and items, reducing the impact of data scarcity on recommendation results. For the cold start problem of new users or new items, the system can generate preliminary feature vectors by associating with existing entities, speeding up the adaptation of the recommendation system to new data.

[0047] In an optional embodiment, an adaptive implicit collaborative knowledge graph is constructed based on multiple embedding representations, including: constructing an account-item potential interaction graph based on the account embedding representation and the item embedding representation, wherein the account-item potential interaction graph is used to represent the potential interaction relationship between accounts and items; constructing a knowledge graph entity implicit relationship graph based on the entity embedding representation, wherein the knowledge graph entity implicit relationship graph is used to represent the potential association relationship between entities; and fusing the initial implicit collaborative knowledge graph, the account-item potential interaction graph, and the knowledge graph entity implicit relationship graph to obtain an adaptive implicit collaborative knowledge graph.

[0048] Optionally, a knowledge graph is a structured data storage method for representing entities (such as people, places, objects) and their relationships (such as "belongs to", "is located in"). It usually exists in the form of a graph, where nodes represent entities and edges represent relationships. Graph neural networks are deep learning models that specialize in processing graph-structured data. They are able to learn embedded representations of nodes, and these embeddings can capture local and global neighborhood information of nodes. In adaptive implicit collaborative knowledge graphs, adaptability can refer to the ability of the model to dynamically adjust its parameters or structure based on data to better capture the relationships between entities. Implicit collaboration can refer to inferring similarities or collaborative relationships between users by analyzing their behavior patterns and preferences in the absence of explicit user interaction data. In graph neural networks, knowledge graphs can be used as additional structural information input to help the model better understand the relationships and attributes between nodes, thereby improving the accuracy of predictions. Graph neural networks can be used to learn embedded representations of entities in knowledge graphs, and these embeddings can be used for tasks such as link prediction, entity disambiguation, and relationship extraction, thereby enhancing the representation ability of knowledge graphs.

[0049] Optionally, constructing an account-item potential interaction graph and a knowledge graph entity implicit relationship graph essentially reveals the implicit relationship between accounts and items and entities by analyzing the similarity or correlation between account embedding representations and item embedding representations and entity embedding representations. Through fusion processing, the adaptive implicit collaborative knowledge graph not only contains the explicit interaction information in the initial graph, but also integrates the additional relationships found in the account-item potential interaction graph and the knowledge graph entity implicit relationship graph, so that the graph can more comprehensively reflect the multi-dimensional associations between accounts, items and entities, thereby improving the expressiveness of the recommendation model. Constructing an adaptive implicit collaborative knowledge graph is a dynamic process that can be adjusted in real time as user behavior changes and entity association information is updated. This adaptability can not only capture changes in user interests in a timely manner, but also adjust the relationship between items according to market dynamics and the evolution of entity information, ensuring the timeliness and personalization of recommendation results.

[0050] In an optional embodiment, an account-item potential interaction graph is constructed based on the account embedding representation and the item embedding representation, including: performing an inner product operation on the account embedding representation and the item embedding representation to obtain an account-item preference similarity matrix; from the account-item preference similarity matrix, screening out K items with the highest similarity between the account and the item, where K is an integer greater than or equal to 2; and constructing an account-item potential interaction graph based on the K items.

[0051] Optionally, an inner product operation can be performed on the account embedding representation and the item embedding representation to obtain an account-item preference similarity matrix to quantify the potential interest between the account and the item. By converting the representation of the account and the item in the vector space into a similarity score, it is possible to identify which items the account may be interested in, even if these interests are not directly recorded in the explicit interaction data. By screening the K items with the highest similarity in the account-item preference similarity matrix, a potential interaction graph can be constructed for each account, that is, a graph containing the set of items that the account is most likely to be interested in. This helps the recommendation system generate a more personalized recommendation list that is more in line with the user's potential interests, improving the accuracy of recommendations and user satisfaction. In practical applications, user-item interaction data is often very sparse, that is, most users have not interacted with most items. By constructing an account-item potential interaction graph, the recommendation system can infer which items the account may be interested in based on the embedding representation of the account and the item, thereby alleviating the data sparsity problem to a certain extent, especially in cold start scenarios. The screened K items include not only items that the account has directly interacted with in the past, but also items that the account has not directly contacted but may be interested in. This helps the recommendation system provide diverse recommendations, avoids the recommendation list being too monotonous, and improves the user experience. When constructing the potential interaction graph, the inner product operation and the filter operation can quickly process a large number of account-item pairs and filter out the most relevant items, which helps improve the computational efficiency of the recommendation system, especially when processing large-scale data sets. In addition, the preference similarity matrix obtained by the inner product operation can introduce a regularization mechanism to optimize the model parameters and reduce the risk of overfitting, thereby improving the generalization ability of the model.

[0052] In an optional embodiment, a knowledge graph entity implicit relationship graph can be constructed based on the entity embedding representation, including: performing inner product operations on the entity embedding representation and the entity embedding representation to obtain an entity-entity implicit relationship similarity matrix; from the entity-entity implicit relationship similarity matrix, screening out K entities with the highest similarity between entities, where K is an integer greater than or equal to 2; based on the K entities, constructing a knowledge graph entity implicit relationship graph.

[0053] Optionally, an entity-entity implicit relationship similarity matrix can be generated by performing an inner product operation on the entity embedding representation to quantify the potential associations between entities, which helps capture the complex relationships between item attributes or related entities. For example, two entities may not have a direct connection, but through their embedding representations, the system can identify the potential connection or similarity between them. The K entities with the highest similarity between entities are selected from the similarity matrix to construct the knowledge graph entity implicit relationship graph. The selection of the K value can be a parameter optimized based on business needs and experimental results, which determines the density and range of entity associations in the graph. Constructing an implicit relationship graph can enhance the understanding ability of the recommendation system, enabling it to make recommendations based on deeper associations rather than just explicit user-item interactions. The fusion of the constructed implicit relationship graph with the adaptive implicit collaborative knowledge graph enables the model to dynamically adjust the structure of the relationship graph according to the update of the entity embedding representation, capture changes in user preferences and the evolution of item attributes, and improve the flexibility and adaptability of the recommendation system.

[0054] Optionally, the adaptive implicit collaborative knowledge graph G can be constructed in the following ways, but is not limited to: A :

[0055] (1) Embed the user into the vector matrix V u and the item embedding vector matrix V i Perform inner product operation to obtain user-item preference similarity matrix S ui , the formula is as follows:

[0056]

[0057] Among them, W1 and W2 represent weight matrices respectively.

[0058] For each user, we set the top K maximum values ​​(topK) from a set of data to obtain the user-item preference similarity matrix S. ui The K items with the highest similarity between users and items are selected from the list to construct a user-item potential interaction list. The records in the list are then represented as elements in the user-item adjacency matrix, and finally the user-item potential interaction graph G is obtained. ui ;

[0059] (2) Embed the entity into the vector matrix V e and the entity embedding vector matrix V e Perform inner product operation to obtain entity-entity implicit relationship similarity matrix S ee , and set a minimum threshold for the cosine similarity between entities:

[0060]

[0061] For each entity e, we set topK from the entity-entity implicit relationship similarity matrix S ee The K entities with the highest similarity (excluding the entity itself) are selected, and finally an entity-entity implicit relationship list is constructed, and the elements in it are converted into elements in the entity adjacency matrix, thereby generating the knowledge graph entity implicit relationship graph G e ;

[0062] (3) Transform the user-item potential interaction graph G ui and the knowledge graph entity implicit relationship graph G e It is fused with the constructed implicit collaborative knowledge graph G to construct an adaptive implicit collaborative knowledge graph G A .

[0063] Optionally, the high-level programming language Python can be used to build the graph neural network of this embodiment, and the adaptive implicit collaborative knowledge graph G A , pre-define the propagation depth L, and obtain high-level feature representations of users, items, and entities:

[0064] (1) Adaptive implicit collaborative knowledge graph G A Input into the L-layer neural network, in this embodiment, L is set to 4, and finally output the final user vector matrix Q u ∈R D×M , item vector matrix Q i ∈R D×N and the entity vector matrix Q e ∈R D×T , each layer of the neural network has the same structure, but uses different parameters. The structure of the lth layer of the neural network is as follows:

[0065] a) Take the target user node u l As the center, the information of neighboring nodes in the graph, i.e., the information of item nodes and target nodes themselves, are gathered to the central node, and finally the feature representation of the user vector of the lth layer is obtained:

[0066]

[0067] Among them, N ul Represents user node u l The set of neighbor nodes, |N ul | represents user node u l The number of neighbor nodes, |N i | represents the number of neighbor nodes of item node i, ∝ represents the coefficient used to control popularity;

[0068] b) Take the target item node i lAs the center, the information of neighboring nodes in the graph, namely user nodes and entity nodes, as well as the target node’s own information, are gathered to the central node, and finally the feature representation of the item vector at the lth layer is obtained:

[0069]

[0070] in, Represents item node i l The set of neighbor user nodes, Represents item node i l The set of neighbor entity nodes, |N il | represents item node i l The number of neighbor nodes, |N u | represents the number of neighbor nodes of item node u;

[0071] c) Take the target entity node e l As the center, the information of neighboring nodes in the graph, namely entity nodes and item nodes, as well as the target node’s own information, are gathered to the central node, and finally the feature representation of the l-th layer entity vector is obtained:

[0072]

[0073] in, Represents entity node e l The collection of neighbor item nodes, Represents entity node e l The set of neighbor entity nodes, |N el | represents entity node e l The number of neighbor nodes, |N e | represents the number of neighbor nodes of entity node e.

[0074] Step S108, based on the adaptive implicit collaborative knowledge graph, adopt the item recommendation model to obtain the item recommendation result corresponding to the target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

[0075] Optionally, the adaptive implicit collaborative knowledge graph contains explicit interactions and implicit preference information between accounts (i.e., users), items, and entities. Through the item recommendation model, this information, including account preferences, item attributes, associations between entities, and implicit relationships between them, can be used to predict which items the target account may be interested in. This process is the key to achieving personalized recommendations, and a set of tailored recommendation lists can be generated for each account. After obtaining the recommendation results, the system can collect user feedback on the recommended items, including clicks, purchases, ratings, etc. These feedback information can be used to further optimize the adaptive implicit collaborative knowledge graph, adjust the embedding vectors of accounts, items, and entities, and the parameters of the item recommendation model, thereby forming a closed-loop learning and optimization process to continuously improve the recommendation performance. For new accounts or new items, due to the lack of interaction data, the adaptive implicit collaborative knowledge graph can use the potential associations between accounts and items, entities, and implicit relationships between entities to generate recommendations for new accounts and alleviate the cold start problem. At the same time, for accounts with sparse data, the entity information and implicit preferences in the graph can supplement the lack of direct interaction information and improve the accuracy of recommendations.

[0076] Optionally, after obtaining the item recommendation results corresponding to the target account, the item recommendation results may also be sent to the target user's corresponding client interface for display.

[0077] In an optional embodiment, the item recommendation result corresponding to the target account can be obtained by adopting an item recommendation model based on an adaptive implicit collaborative knowledge graph, including: based on the adaptive implicit collaborative knowledge graph, adopting the graph neural network in the item recommendation model to obtain a first feature representation corresponding to the account vector and a second feature representation corresponding to the item vector; performing an inner product operation on the first feature representation and the second feature representation to obtain the probability of interaction between the account and the item included in the adaptive implicit collaborative knowledge graph; and determining the item recommendation result corresponding to the target account based on the probability of interaction between the account and the item included in the adaptive implicit collaborative knowledge graph.

[0078] Optionally, high-order feature representations of accounts and items can be learned by executing the graph neural network on an adaptive implicit collaborative knowledge graph. These feature representations not only contain explicit interaction information, but also incorporate the account's potential interest in items and the implicit preferences and associations between items and entities, thereby forming a more comprehensive and in-depth representation of users and items. Through the inner product operation, the model is able to calculate the similarity between the first feature representation of the account vector and the second feature representation of the item vector, and then predict the probability of interaction between the account and the item. This probabilistic prediction directly determines which items will be recommended to a specific account. Based on the interaction probability between the account and the item, a list of item recommendations can be generated for each target account. This list will be sorted from high to low according to the predicted interaction probability, ensuring that the items most likely to be of interest to the account are at the front of the list.

[0079] Optionally, the recommendation model can be optimized in the following way. Specifically, the output representation of each layer vector of user u can be obtained by iteratively propagating the graph convolution of layer L in the recommendation model, that is, Similarly, the output representation of each layer of vector of item i can be obtained And the output representation of each layer vector of entity e The final vector of user u is expressed as The final vector of user i is expressed as This embodiment adopts a layer selection mechanism, that is, the user vector and item vector output by the last two layers are used to predict the probability of interaction between users and items, where f is the inner product function:

[0080]

[0081]

[0082] Optionally, the recommendation model can be optimized using the following joint learning objective function optimization model:

[0083]

[0084] Among them, θ refers to all learnable parameters in the model, that is, [W1, W2, V u ,V i ,V e ], λ is the regularization coefficient; Figure 3 It is an optional graph neural network module diagram according to an embodiment of the invention.

[0085] a)L CF The loss function (Bayesian Personalized Ranking, BPR) loss commonly used in recommendation systems and ranking learning problems in the recommendation field is adopted:

[0086]

[0087] Among them, Y={(u,i,j)(u,i)∈Y + ,(u,j)∈Y -}, Y represents the training sample of user-item interaction data, Y+ represents the positive sample, that is, it means that there is interaction between user u and item i, Y - represents a negative sample, that is, a sample with no interaction between user u and item j; σ(·) is the sigmoid activation function;

[0088] b)L KG BPR loss can be used:

[0089]

[0090] Among them, Z={(i,e,v)(i,e)∈Z + ,(i,v)∈Z -}, z represents the training sample of the item-entity implicit knowledge graph, Z + represents a positive sample, that is, a sample that indicates that there is interaction between item i and entity e, Z - Represents a negative sample, that is, a sample in which there is no interaction between item i and entity v.

[0091] c) is the objective function of the enhanced graph learning optimization designed in this embodiment, which can minimize the actual user-item bipartite graph G1 and the user-item potential interaction graph G obtained after similarity calculation. ui The mean square loss between them is used to ensure that the predicted results are consistent with the actual results.

[0092]

[0093] Optionally, initialize the dimension of user, item, and entity embedding vectors to D, with an empirical value of 64, and use a Gaussian distribution with a mean of 0 and a standard deviation of 0.01 to initialize the embedding vector matrix. ui Filter out the K items with the highest similarity between users and items, and the empirical value is [1,10]. By setting topK, we can get the similarity matrix S of the entity-entity implicit relationship. eeThe K entities with the highest similarity are selected from the dataset, and the empirical value is [1,5]. The minimum threshold for the cosine similarity between entities is set, and the empirical value is 0.5. The number of graph neural network layers L is 4. The popularity bias coefficient α in the graph neural network is adjusted between {0.3, 0.4, 0.5}. The training samples are used as data samples for model fitting. Since this embodiment trains the model based on the BPR loss function, for each user in the training set, negative samples with the same number of interaction histories are randomly sampled. The model predicts and outputs the predicted probability of whether the target user clicks on the item, and the items are displayed to the user after being sorted.

[0094] Optionally, the training samples for training and optimizing the recommendation model can be: user-item interaction data, which records the explicit interaction between users and items, such as users clicking, purchasing, and rating items. Item-entity triple data, which contains the relationship between items and related entities (item attribute information or information associated with items). Through these triple data, an item-entity implicit graph can be constructed, which can then be integrated into the user-item binary graph to form an initial implicit collaborative knowledge graph G. User behavior data, in addition to direct interaction data, indirect behavior data such as user search history, browsing behavior, and collections on the platform are also considered as part of the training samples. These data can provide clues to users' implicit preferences.

[0095] Optionally, negative samples in the recommendation system refer to samples where no interaction between users and items is observed. In this method, negative samples refer to data points where there is no interaction record between users and items, which can be items that users have never clicked, purchased, or rated.

[0096] Through the above steps S102 to S108, the purpose of better capturing the missing information of the graph neural network nodes can be achieved, thereby achieving a technical effect of higher accuracy and better effect of recommendation, and further solving the technical problem of poor item recommendation effect caused by the large sparsity of interaction data and knowledge graph.

[0097] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation mode: Figure 4 is a flowchart of an optional item recommendation method according to an embodiment of the present invention. Figure 4 As shown, the method includes:

[0098] S1, constructs an implicit collaborative knowledge graph G based on training samples, specifically including:

[0099] S11. Construct an implicit graph of objects and entities;

[0100] S12. construct a user-item binary graph;

[0101] S13. Fuse the item-entity pairs into the user-item binary graph to construct an implicit collaborative knowledge graph G;

[0102] S2, initialize the user, item and entity embedding vectors to obtain the user embedding vector matrix V u , item embedding vector matrix V i , and the entity embedding vector matrix V e ;

[0103] S3, building an adaptive implicit collaborative knowledge graph, specifically including:

[0104] S31. Perform an inner product operation on the user embedding vector matrix and the item embedding vector matrix to obtain a user-item preference similarity matrix;

[0105] S32. Perform an inner product operation on the entity embedding vector matrix and the entity embedding vector matrix to obtain an entity-entity implicit relationship similarity matrix;

[0106] S33. Transform the user-item potential interaction graph G ui and the knowledge graph entity implicit relationship graph G e Fuse with the implicit collaborative knowledge graph G constructed in step S1 to construct an adaptive implicit collaborative knowledge graph;

[0107] S4, the obtained adaptive implicit collaborative knowledge graph is input into the enhanced graph neural network module. After L layers of graph convolution iterative propagation, the output representation of each layer vector of user u is obtained, that is, Similarly, the output representation of each layer of vector of item i can be obtained And the output representation of each layer vector of entity e Using the user vector and item vector output by the last two layers, the probability of user and item interaction is predicted through inner product operation.

[0108] S5, constructing an objective function of joint learning to optimize model parameters, wherein the specific construction method of the objective function is the same as the above, and will not be repeated here;

[0109] S6, output the probability of the user clicking on the item.

[0110] It should be noted that in this embodiment, an adaptive implicit collaborative knowledge graph is constructed, and the currently learned node (user, item and entity) embedding vector is used to guide the construction of the user-item potential interaction graph and the knowledge graph entity implicit relationship graph, thereby updating the implicit collaborative knowledge graph structure. At the same time, the updated implicit collaborative knowledge graph can also better serve the representation learning of the node embedding vector, further improving the recommendation performance. In addition, the embodiment designs a corresponding graph enhancement function during the optimization process, so as to better optimize the model parameters and more fully capture the potential interaction relationship between users and items and the implicit relationship between entities.

[0111] In this embodiment, an item recommendation device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the terms "module" and "device" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0112] According to an embodiment of the present invention, a device embodiment for implementing the above-mentioned item recommendation method is also provided. Figure 5 is a schematic diagram of the structure of an item recommendation device according to an embodiment of the present invention. Figure 5 As shown, the above-mentioned item recommendation device includes: a construction module 200, a representation module 202, an association module 204, and a target module 206, wherein:

[0113] A construction module 200 is used to construct an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent explicit interaction relationships and implicit preferences and associations between accounts, items, and entities, and the entity is attribute information of the item, or association information associated with the item;

[0114] A representation module 202, connected to the construction module 200, is used to construct a plurality of embedded representations based on the initial implicit collaborative knowledge graph, wherein the plurality of embedded representations include an account embedded representation, an item embedded representation, and an entity embedded representation;

[0115] An association module 204, connected to the representation module 202, is used to construct an adaptive implicit collaborative knowledge graph based on multiple embedded representations, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in the item, and the implicit preference and association between the item and the entity;

[0116] The target module 206 is connected to the association module 204, and is used to obtain the item recommendation result corresponding to the target account based on the adaptive implicit collaborative knowledge graph and the item recommendation model, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

[0117] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0118] It should be noted that the construction module 200, the representation module 202, the association module 204, and the target module 206 correspond to steps S102 to S108 in the embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the embodiment. It should be noted that the modules as part of the device can be run in a computer terminal.

[0119] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0120] The above-mentioned item recommendation device may also include a processor and a memory. The above-mentioned construction module 200, representation module 202, association module 204, target module 206, etc. are all stored in the memory as program modules, and the processor executes the above-mentioned program modules stored in the memory to realize corresponding functions.

[0121] The processor includes a kernel, which retrieves the corresponding program module from the memory. The kernel may be one or more. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory includes at least one memory chip.

[0122] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned item recommendation methods.

[0123] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0124] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: construct an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent the explicit interaction relationship and implicit preferences and associations between accounts, items and entities, and the entity is the attribute information of the item, or the association information associated with the item; based on the initial implicit collaborative knowledge graph, multiple embedding representations are constructed, wherein the multiple embedding representations include account embedding representations, item embedding representations and entity embedding representations; based on the multiple embedding representations, an adaptive implicit collaborative knowledge graph is constructed, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in the item, as well as the implicit preferences and associations between the item and the entity; based on the adaptive implicit collaborative knowledge graph, an item recommendation model is used to obtain the item recommendation results corresponding to the target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

[0125] According to an embodiment of the present application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any one of the above-mentioned item recommendation methods when it is run.

[0126] According to an embodiment of the present application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements any one of the steps of the item recommendation method described above.

[0127] Optionally, the above-mentioned computer program product, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: constructing an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent the explicit interaction relationship and implicit preferences and associations between accounts, items and entities, and the entity is the attribute information of the item, or the association information associated with the item; based on the initial implicit collaborative knowledge graph, constructing multiple embedding representations, wherein the multiple embedding representations include account embedding representations, item embedding representations and entity embedding representations; based on multiple embedding representations, constructing an adaptive implicit collaborative knowledge graph, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in the item, as well as the implicit preferences and associations between the item and the entity; based on the adaptive implicit collaborative knowledge graph, using the item recommendation model to obtain the item recommendation results corresponding to the target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

[0128] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: constructing an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent explicit interactive relationships and implicit preferences and associations between accounts, items, and entities, and the entity is the attribute information of the item, or the association information associated with the item; based on the initial implicit collaborative knowledge graph, constructing multiple embedding representations, wherein the multiple embedding representations include account embedding representations, item embedding representations, and entity embedding representations; based on multiple embedding representations, constructing an adaptive implicit collaborative knowledge graph, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in the item, and the implicit preferences and associations between the item and the entity; based on the adaptive implicit collaborative knowledge graph, using an item recommendation model, obtaining an item recommendation result corresponding to a target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph. The above sequence of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments.

[0129] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above modules can be a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0131] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0132] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0133] If the above-mentioned integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0134] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An item recommendation method, characterized in that: include: Constructing an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent explicit interactive relationships and implicit preferences and associations between accounts, items, and entities, and the entity is attribute information of the item, or association information associated with the item; Based on the initial implicit collaborative knowledge graph, construct multiple embedding representations, wherein the multiple embedding representations include account embedding representations, item embedding representations, and entity embedding representations; Based on the multiple embedding representations, construct an adaptive implicit collaborative knowledge graph, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in items, and implicit preferences and associations between items and entities; Based on the adaptive implicit collaborative knowledge graph, an item recommendation model is adopted to obtain item recommendation results corresponding to a target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

2. The method according to claim 1, characterized in that: The constructing of the initial implicit collaborative knowledge graph includes: Constructing a plurality of triples, wherein the triples include items, entities, and implicit relationships between items and entities; Based on the interactive operation behaviors between accounts and items, a binary graph is constructed; Using items as a bridge to connect accounts and entities, the multiple triples are merged into the binary graph to obtain the initial implicit collaborative knowledge graph.

3. The method according to claim 1, characterized in that The step of constructing a plurality of embedding representations based on the initial implicit collaborative knowledge graph includes: The accounts, items, and entities in the initial implicit collaborative knowledge graph are mapped to a vector space to obtain the multiple embedding representations, wherein the multiple embedding representations are in the form of vector matrices.

4. The method according to claim 1, characterized in that The step of constructing an adaptive implicit collaborative knowledge graph based on the multiple embedded representations includes: Based on the account embedding representation and the item embedding representation, construct an account-item potential interaction graph, wherein the account-item potential interaction graph is used to represent a potential interaction relationship between an account and an item; Based on the entity embedding representation, construct a knowledge graph entity implicit relationship graph, wherein the knowledge graph entity implicit relationship graph is used to represent the potential association relationship between entities; The initial implicit collaborative knowledge graph, the account-item potential interaction graph and the knowledge graph entity implicit relationship graph are fused to obtain the adaptive implicit collaborative knowledge graph.

5. The method according to claim 4, characterized in that The constructing an account-item potential interaction graph based on the account embedding representation and the item embedding representation includes: Performing an inner product operation on the account embedding representation and the item embedding representation to obtain an account-item preference similarity matrix; From the account-item preference similarity matrix, select K items with the highest similarity between the account and the item, where K is an integer greater than or equal to 2; Based on the K items, the account-item potential interaction graph is constructed.

6. The method according to claim 4, characterized in that The step of constructing a knowledge graph entity implicit relationship graph based on the entity embedding representation includes: Performing an inner product operation on the entity embedding representation and the entity embedding representation to obtain an entity-entity implicit relationship similarity matrix; From the entity-entity implicit relationship similarity matrix, select K entities with the highest similarity between entities, where K is an integer greater than or equal to 2; Based on the K entities, an implicit relationship graph of the knowledge graph entities is constructed.

7. The method according to any one of claims 1 to 6, characterized in that The method of using an item recommendation model based on the adaptive implicit collaborative knowledge graph to obtain an item recommendation result corresponding to the target account includes: Based on the adaptive implicit collaborative knowledge graph, the graph neural network in the item recommendation model is used to obtain a first feature representation corresponding to the account vector and a second feature representation corresponding to the item vector; Performing an inner product operation on the first feature representation and the second feature representation to obtain a probability of interaction between accounts and items included in the adaptive implicit collaborative knowledge graph; Based on the probability of interaction between accounts and items included in the adaptive implicit collaborative knowledge graph, the item recommendation result corresponding to the target account is determined.

8. An item recommendation device, characterized in that: include: A construction module, used to construct an initial implicit collaborative knowledge graph, wherein the initial implicit collaborative knowledge graph is used to represent explicit interactive relationships and implicit preferences and associations between accounts, items and entities, and the entity is attribute information of the item, or association information associated with the item; A representation module, configured to construct a plurality of embedding representations based on the initial implicit collaborative knowledge graph, wherein the plurality of embedding representations include an account embedding representation, an item embedding representation, and an entity embedding representation; An association module, configured to construct an adaptive implicit collaborative knowledge graph based on the multiple embedded representations, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in items, and implicit preferences and associations between items and entities; The target module is used to obtain the item recommendation result corresponding to the target account based on the adaptive implicit collaborative knowledge graph and using the item recommendation model, wherein the target account is an account in the adaptive implicit collaborative knowledge graph.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the item recommendation method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the item recommendation method described in any one of claims 1 to 7.

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