Product recommendation method, product recommendation model training method, device and equipment

Abstract

The invention provides a product recommendation method, a product recommendation model training method, devices and equipment, which can be applied to the technical field of artificial intelligence and the technical field of financial science and technology. The method comprises the steps that under the condition that permission of a target object is obtained, object information of the target object is converted into an atlas vector, the object information comprises multiple pieces of sub-object information, the atlas vector comprises multiple node vectors, and the node vectors are used for representing the object sub-information; under the condition that the target object is determined to be a potential object based on the atlas vector, determining a first sub-atlas vector matched with preset product attribute information from the atlas vector based on the node identifiers of the plurality of node vectors; inputting the first sub-atlas vector into a product recommendation model to obtain an object category of the target object and a product recommendation list matched with the object category and the target object; and recommending the to-be-recommended product determined from the product recommendation list to the target object.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence technology and financial technology, and specifically to a product recommendation method, a product recommendation model training method, an apparatus, a device, a medium, and a program product. Background Art

[0002] With the rapid development of financial technology, customers are faced with more and more product choices. Using product recommendation methods to recommend products to customers is one of the current industry service trends. Currently, the common product recommendation method usually recommends products based on the user's historical purchase history, and when making recommendations, the recommended products may be determined for all customers.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related technology: when making product recommendations, the recommended objects lack specificity, and the method of using user historical purchase information to make recommendations may be affected by data sparsity and "cold start" problems. For new users or new products, due to the lack of sufficient data, there may be a problem of low recommendation accuracy and failure to meet customer needs. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a product recommendation method, a product recommendation model training method, an apparatus, a device, a medium and a program product.

[0005] One aspect of the present disclosure provides a product recommendation method, including: with the permission of the target object, converting the object information of the target object into a graph vector, wherein the object information includes multiple sub-object information, the graph vector includes multiple node vectors, and the node vector is used to characterize the object sub-information; when the target object is determined to be a potential object based on the graph vector, based on the node identifiers of the multiple node vectors, determining a first sub-graph vector that matches preset product attribute information from the graph vector; inputting the first sub-graph vector into a product recommendation model to obtain the object category of the target object and a product recommendation list that matches both the object category and the target object; and recommending the to-be-recommended products determined from the product recommendation list to the target object.

[0006] According to an embodiment of the present disclosure, a product recommendation model includes a category determination sub-model and a product recommendation sub-model; a first sub-graph vector is input into the product recommendation model to obtain an object category of a target object and a product recommendation list that matches both the object category and the target object, including: inputting the first sub-graph vector into the category determination sub-model to obtain the object category of the target object; inputting preferred products of similar objects with the object category and the first sub-graph vector into the product recommendation sub-model to obtain a product recommendation list that matches both the object category and the target object.

[0007] According to an embodiment of the present disclosure, preferred products and a first sub-graph vector of similar objects with an object category are input into a product recommendation sub-model to obtain a product recommendation list that matches both the object category and the target object, including: inputting the preferred products and the first sub-graph vector of similar objects into the product recommendation sub-model to obtain a first preferred product feature of the similar objects and a second preferred product feature of the target object; performing similarity calculation on the first preferred product feature of each of a plurality of similar objects and the product feature of each of a plurality of preset products to obtain a first similarity corresponding to each of the plurality of preset products; performing similarity calculation on the second preferred product feature and the product feature of each of the plurality of preset products to obtain a second similarity corresponding to each of the plurality of preset products; determining a recommendation value of each of the plurality of preset products based on a first weight value corresponding to the first preference product feature, a first similarity corresponding to each of the plurality of preset products, a second weight value corresponding to the second preference product feature, and a second similarity corresponding to each of the plurality of preset products; and obtaining a product recommendation list based on the recommendation values ​​of each of the plurality of preset products.

[0008] According to an embodiment of the present disclosure, the product recommendation method also includes: determining a second sub-graph vector that matches preset object attribute information from the graph vector based on node identifiers of multiple node vectors; inputting the second sub-graph vector into a potential object prediction model to obtain a target probability that the target object is a potential object; and determining that the target object is a potential object when it is determined that the target probability is greater than or equal to a preset probability threshold.

[0009] According to an embodiment of the present disclosure, the object information also includes: the association relationship between multiple sub-object information; converting the object information of the target object into a graph vector, including: performing information correction processing on the object information to obtain corrected object information; extracting multiple sub-object information and mapping the association relationship between multiple sub-object information on the corrected object information to obtain a knowledge graph corresponding to the target object, wherein the knowledge graph includes nodes and edges, the nodes represent the sub-object information, and the edges represent the association relationship between multiple sub-object information; mapping the knowledge graph into the vector space to obtain a graph vector.

[0010] According to an embodiment of the present disclosure, information correction processing is performed on object information to obtain corrected object information, including: determining target sub-object information of a numerical type from multiple sub-object information; when it is determined that there is an abnormality in the information format of the target object information, correcting the target object information based on adjacent object information adjacent to the target object information to obtain first target sub-object information; when it is determined that there is an abnormality in the information value of the target object information, determining homogeneous object information having the same information identifier as the target object information; determining a replacement interval based on the information value of the homogeneous object information; correcting the information value of the target object information to a correction value in the replacement interval to obtain second target sub-object information; and determining the corrected object information based on the first target sub-object information and the second target sub-object information.

[0011] Another aspect of the present disclosure provides a product recommendation model training method, the product recommendation model includes a category determination submodel and a product recommendation submodel; including: obtaining a first subgraph vector sample of each of a plurality of potential object samples; for each potential object sample, determining a plurality of category feature values ​​of the potential object sample based on the node identifiers and node values ​​of a plurality of node vector samples included in the first subgraph vector sample; determining an object category label of the potential object sample based on the plurality of category feature values; training an initial category determination submodel based on the first subgraph vector samples of each of the plurality of potential object samples and the object category labels of the plurality of potential object samples to obtain a category determination submodel; training an initial product recommendation submodel based on the first subgraph vector samples of each of the plurality of potential object samples and the preferred product samples of the same type of object samples of the plurality of potential object samples to obtain a product recommendation submodel, wherein the object category of the same type of object sample is the same as the object category label of the potential object sample; obtaining a product recommendation model based on the category determination submodel and the product recommendation submodel.

[0012] Another aspect of the present disclosure provides a product recommendation device, including: a conversion module, which is used to convert the object information of the target object into a graph vector with the permission of the target object, wherein the object information includes multiple sub-object information, the graph vector includes multiple node vectors, and the node vector is used to characterize the object sub-information; a first determination module, which is used to determine a first sub-graph vector matching preset product attribute information from the graph vector based on node identifiers of multiple node vectors when the target object is determined to be a potential object based on the graph vector; a second determination module, which is used to input the first sub-graph vector into a product recommendation model to obtain the object category of the target object and a product recommendation list matching both the object category and the target object; a recommendation module, which is used to recommend the to-be-recommended product determined from the product recommendation list to the target object.

[0013] Another aspect of the present disclosure provides a product recommendation model training device, the product recommendation model includes a category determination submodel and a product recommendation submodel; the device includes: an acquisition module, used to obtain a first sub-graph vector sample of each of a plurality of potential object samples; a third determination module, used to determine, for each potential object sample, a plurality of category feature values ​​of the potential object sample based on the node identifiers and node values ​​of a plurality of node vector samples included in the first sub-graph vector sample; a fourth determination module, used to determine the object category label of the potential object sample based on the plurality of category feature values; a first training module, used to train an initial category determination submodel based on the first sub-graph vector samples of each of the plurality of potential object samples and the object category labels of the plurality of potential object samples to obtain a category determination submodel; a second training module, used to train an initial product recommendation submodel based on the first sub-graph vector samples of each of the plurality of potential object samples and the preferred product samples of the same type of object samples of the plurality of potential object samples to obtain a product recommendation submodel, wherein the object category of the same type of object sample is the same as the object category label of the potential object sample; a model determination module, used to obtain a product recommendation model based on the category determination submodel and the product recommendation submodel.

[0014] Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] Another aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instruction stored thereon, which implements the steps of the above method when the computer program or instruction is executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0017] According to the product recommendation method disclosed in the present invention, by converting object information into a graph vector, and determining whether the target object is a potential object through the graph vector, when the target object is determined to be a potential object, the first sub-graph vector for product recommendation is screened from the graph vector, thereby achieving effective screening of input data and recommended objects. The first sub-graph vector is input into the product recommendation model to obtain the object category to which the target object belongs and a product recommendation list that matches both the object category and the target object, that is, it is achieved to determine the products that may be of interest to the target object from the two perspectives of the preference of similar objects and the preference of the target object itself, thereby at least partially solving the technical problems of lack of pertinence and low accuracy of product recommendation in related technologies, and achieving technical effects such as improving the accuracy of subsequent product recommendations and reducing waste of recommendation resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0019] Figure 1 A diagram schematically illustrates an application scenario of a product recommendation method, a product recommendation model training method, an apparatus, a device, a medium, and a program product according to an embodiment of the present disclosure;

[0020] Figure 2 A flowchart of a product recommendation method according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 A schematic diagram of a knowledge graph according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 4 A flowchart of a product recommendation model training method according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 5 A schematic diagram of category feature values ​​for different potential object samples according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 6 A data flow diagram of a product recommendation model training method according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 7 The structural block diagram of the product recommendation device according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 8 A structural block diagram of a product recommendation model training device according to an embodiment of the present disclosure is schematically shown; and

[0027] Fig. 9A block diagram of an electronic device suitable for implementing a product recommendation method and a product recommendation model training method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0030] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] It should be noted that the product recommendation method and product recommendation model training method disclosed in the present invention can be used in the fields of artificial intelligence technology and financial technology technology, and can also be used in any field other than the fields of artificial intelligence technology and financial technology technology, such as: the field of computer technology. The application fields of the product recommendation method and product recommendation model training method disclosed in the present invention are not limited.

[0033] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0035] The inventors found in the research process that the product recommendation method in the related art may be affected by data sparsity and the "cold start" problem. For new users or new products, the recognition effect is unstable or inaccurate due to the lack of sufficient data. It may also be difficult to handle some complex problems, such as balancing users' long-term and short-term interests, and handling the inconsistency between implicit feedback and explicit feedback.

[0036] In addition, the customer data collected by relevant technologies are mostly static information, which fails to deeply analyze the dynamic financial needs and behavior patterns of customers. In addition, there is a lack of in-depth research on key information such as customers' potential value prediction and risk preferences, which makes it impossible to accurately identify potential high-end customers.

[0037] An embodiment of the present disclosure provides a product recommendation method, comprising: with permission from a target object, converting object information of a target object into a graph vector, wherein the object information includes multiple sub-object information, the graph vector includes multiple node vectors, and the node vector is used to characterize the object sub-information; when the target object is determined to be a potential object based on the graph vector, determining a first sub-graph vector that matches preset product attribute information from the graph vector based on node identifiers of multiple node vectors; inputting the first sub-graph vector into a product recommendation model to obtain an object category of the target object and a product recommendation list that matches both the object category and the target object; and recommending the products to be recommended determined from the product recommendation list to the target object.

[0038] Figure 1 The application scenario diagram of the product recommendation method, product recommendation model training method, device, equipment, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0039] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0040] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0041] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0042] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0043] It should be noted that the product recommendation method and product recommendation model training method provided in the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the product recommendation device and product recommendation model training device provided in the embodiments of the present disclosure can generally be set in the server 105. The product recommendation method and product recommendation model training method provided in the embodiments of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the product recommendation device and product recommendation model training device provided in the embodiments of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0044] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0045] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The product recommendation method and product recommendation model training method of the disclosed embodiment are described in detail.

[0046] Figure 2 The flowchart of the product recommendation method according to the embodiment of the present disclosure is schematically shown.

[0047] like Figure 2 As shown, the method includes operations S210 to S240.

[0048] In operation S210, with the permission of the target object, the object information of the target object is converted into a graph vector, wherein the object information includes a plurality of sub-object information, the graph vector includes a plurality of node vectors, and the node vector is used to represent the object sub-information.

[0049] In operation S220, when the target object is determined to be a potential object based on the graph vector, a first sub-graph vector matching the preset product attribute information is determined from the graph vector based on the node identifiers of the plurality of node vectors.

[0050] In operation S230, the first sub-graph vector is input into a product recommendation model to obtain an object category of the target object and a product recommendation list matching both the object category and the target object.

[0051] In operation S240, the to-be-recommended product determined from the product recommendation list is recommended to the target object.

[0052] According to an embodiment of the present disclosure, the target object may be a customer who is likely to purchase the product.

[0053] According to the embodiments of the present disclosure, the object information of the target object is not limited and can be any information of the target object, for example, the object information can include: object basic information, object transaction data, object interaction information, object social relationship information, etc. The sub-object information can be specific information in the above information, such as: the products traded in the object transaction data, each product can be a sub-object information.

[0054] According to an embodiment of the present disclosure, a node vector may correspond to sub-object information one-to-one; a node identifier may correspond to an information identifier of the sub-object information.

[0055] According to an embodiment of the present disclosure, the basic information of the object may include: the name, age, occupation, income, products held and other information of the target object; the transaction data of the object may include: the deposits, loans, investments, transfers and other information of the target object; the interaction information of the object may include: the online consultation, complaints, feedback and other information of the target object; the social relationship information of the object may include: the unit information of the target object, the unit equity information, the unit senior management information, the information of the guarantee or guarantee provided for the unit or individual, the unit salary payment information, the enterprise WeChat click, browse, purchase, recommendation, chat information, etc.

[0056] According to an embodiment of the present disclosure, before obtaining the object information of the target object, the consent or authorization of the target object may be obtained. For example, before obtaining the object information, a request to obtain the object information may be issued to the target object. When the target object agrees or authorizes that the object information may be obtained, the object information of the target object is obtained.

[0057] According to the embodiments of the present disclosure, a corresponding operation entry can be provided for the target object, so that the target object can choose to agree or reject the automated decision result. That is, before the object information is processed / decided, the instruction of the target object to agree or reject the processing / decision input through the corresponding operation entry can be obtained. If the user agrees to the processing / decision, the object information is processed / decided, that is, step S210 is executed. If the user refuses to process / decision, the expert decision process is entered.

[0058] According to an embodiment of the present disclosure, the graph vector may be obtained by converting the knowledge graph obtained from the object information. The graph vector may include multiple node vectors and a vector representing the association relationship between the multiple node vectors.

[0059] According to an embodiment of the present disclosure, the preset product attribute information may be information that affects the products purchased by the target object. Specifically, the preset product attribute information may include: natural attribute information, financial status information, credit information, product information, customer relationship information, transaction information, etc.

[0060] According to the embodiments of the present disclosure, financial status information may include: income, expenditure, deposits, liabilities, financial investment and other information. Credit information may include: credit rating, overdue situation, repayment on time rate and other information. Product information may include: purchase time, amount, frequency, product type, payment method and other information. Customer relationship information may include: the frequency of customers using mobile banking, telephone consultation, outlets, complaints, customer satisfaction surveys, etc. Transaction information may include: payment application information, etc.

[0061] According to an embodiment of the present disclosure, potential targets may be customer groups that have outstanding performance in asset size, transaction activity, and contribution, or have potential in the above aspects.

[0062] According to the embodiments of the present disclosure, there is no limitation on the object category, which can be determined based on the actual classification situation, and can be a more fine-grained classification of the target object through the first sub-graph vector of the target object.

[0063] According to an embodiment of the present disclosure, multiple node identifiers of a graph vector may be matched with preset product attribute information, thereby determining a first sub-graph vector that matches the preset product attribute information.

[0064] According to the embodiments of the present disclosure, there is no limitation on the implementation method of the product recommendation model, which may be implemented through a multi-level neural network or a multi-level deep learning algorithm.

[0065] According to an embodiment of the present disclosure, the product recommendation list may be a product list obtained by considering two factors, namely, the object category and the first sub-graph vector of the target object.

[0066] According to an embodiment of the present disclosure, the product recommendation list may include recommendation values ​​for each of multiple preset products, and the multiple preset products may be sorted by the recommendation values, so that the product to be recommended may be determined from the multiple preset products and recommended to the target object.

[0067] According to the product recommendation method disclosed in the present invention, by converting object information into a graph vector, and determining whether the target object is a potential object through the graph vector, when the target object is determined to be a potential object, the first sub-graph vector for product recommendation is screened from the graph vector, thereby achieving effective screening of input data and recommended objects. The first sub-graph vector is input into the product recommendation model to obtain the object category to which the target object belongs and a product recommendation list that matches both the object category and the target object, that is, it is achieved to determine the products that may be of interest to the target object from the two perspectives of the preference of similar objects and the preference of the target object itself, thereby at least partially solving the technical problems of lack of pertinence and low accuracy of product recommendation in related technologies, and achieving technical effects such as improving the accuracy of subsequent product recommendations and reducing waste of recommendation resources.

[0068] According to an embodiment of the present disclosure, the product recommendation model includes a category determination sub-model and a product recommendation sub-model; inputting the first sub-graph vector into the product recommendation model to obtain the object category of the target object and a product recommendation list that matches both the object category and the target object may include the following operations.

[0069] The first sub-graph vector is input into the category determination sub-model to obtain the object category of the target object; the preferred products of similar objects with the object category and the first sub-graph vector are input into the product recommendation sub-model to obtain a product recommendation list that matches both the object category and the target object.

[0070] According to the embodiments of the present disclosure, there is no limitation on the implementation methods of the category determination sub-model and the product recommendation sub-model, and they can be implemented through a neural network model or a deep learning algorithm, for example, they can be implemented through restricted Boltzmann machines (RBM), or recurrent neural networks, etc.

[0071] According to the embodiments of the present disclosure, RBM can effectively learn and simulate the intrinsic structure of data, thereby predicting the future behavior trends of customers, and helping to formulate strategies in advance to better meet the needs of potential targets.

[0072] According to an embodiment of the present disclosure, the category determination submodel can be trained in the following manner: obtaining a first sub-graph vector sample of each of multiple potential object samples; for each potential object sample, determining multiple category feature values ​​of the potential object sample based on node identifiers and node values ​​of multiple node vector samples included in the first sub-graph vector sample; determining an object category label of the potential object sample based on the multiple category feature values; training the initial category determination submodel based on the first sub-graph vector samples of each of the multiple potential object samples and the object category labels of the multiple potential object samples to obtain the category determination submodel.

[0073] According to an embodiment of the present disclosure, the same type of objects may be objects having the same object category as the target object, and the preferred products of the same type of objects may be pre-stored in a database.

[0074] According to an embodiment of the present disclosure, the preferred products of similar objects with an object category and the first sub-graph vector are input into a product recommendation sub-model to obtain a product recommendation list that matches both the object category and the target object. In some embodiments, the preferred products of the target object can be obtained by inputting the first sub-graph vector into the product recommendation sub-model, and the preferred products of the target object and the preferred products of similar objects are intersected, and the preferred products preferred by both the target object and the similar objects are assigned higher ratings, and other preferred products are assigned lower ratings, thereby obtaining a product recommendation list.

[0075] According to an embodiment of the present disclosure, the object category of the target object is determined by a category determination sub-model, and a product recommendation list that matches both the object category and the target object is jointly determined by similar objects with the same object category and the first sub-graph vector of the target object itself, thereby determining products that may be of interest to the target object from multiple angles based on the product preferences of objects of the same category and the product preferences of the target object.

[0076] According to an embodiment of the present disclosure, inputting preferred products of similar objects with an object category and a first sub-graph vector into a product recommendation sub-model to obtain a product recommendation list that matches both the object category and the target object may include the following operations.

[0077] The preferred products of the same type of objects and the first sub-graph vector are input into the product recommendation sub-model to obtain the first preferred product features of the same type of objects and the second preferred product features of the target object; the similarity is calculated between the first preferred product features of multiple similar objects and the product features of multiple preset products to obtain the first similarities corresponding to the multiple preset products; the similarity is calculated between the second preferred product features and the product features of multiple preset products to obtain the second similarities corresponding to the multiple preset products; based on the first weight value corresponding to the first preference product feature, the first similarities corresponding to the multiple preset products, the second weight value corresponding to the second preference product feature and the second similarities corresponding to the multiple preset products, the recommendation value of each of the multiple preset products is determined; based on the recommendation values ​​of each of the multiple preset products, a product recommendation list is obtained.

[0078] According to an embodiment of the present disclosure, in one implementation, the product recommendation sub-model can be used to perform feature extraction on the preferred products and the first sub-graph vector of the same type of objects, respectively, to obtain the first preferred product feature and the second preferred product feature.

[0079] According to the embodiments of the present disclosure, there is no limitation on the method of calculating the similarity, which may be cosine similarity, Euclidean distance, etc.

[0080] According to an embodiment of the present disclosure, the product feature of a preset product may be obtained by inputting the preset product into a product recommendation sub-model, which may be obtained in advance and stored in a database.

[0081] According to an embodiment of the present disclosure, the first weight value and the second weight value may be preset, and the second weight value may be greater than the first weight value. By assigning a higher weight to the second weight value of the target object, it is more likely to meet the needs of the target object.

[0082] According to an embodiment of the present disclosure, the first weight value and the first similarities corresponding to multiple preset products can be multiplied respectively to obtain the first initial recommendation values ​​of the multiple preset products; the second weight value and the second similarities corresponding to the multiple preset products can be multiplied respectively to obtain the second initial recommendation values ​​of the multiple preset products; for each preset product, the first initial recommendation value and the second initial recommendation value are added to obtain the recommendation values ​​of each of the multiple preset products, and the recommendation values ​​of each of the multiple preset products are sorted to obtain a product recommendation list.

[0083] According to an embodiment of the present disclosure, in some embodiments, a product recommendation list for different target objects may be as shown in the following Table 1.

[0084] Table 1

[0085]

[0086] According to an embodiment of the present disclosure, by respectively determining the first preferred product features of similar objects and the second preferred product features of the target object, the similarities corresponding to multiple preset products are determined, and combined with the preset first weight value and the second weight value, a recommendation value for each product is obtained, thereby achieving product recommendations based on the quantified recommendation values, thereby facilitating more effective product recommendations.

[0087] According to an embodiment of the present disclosure, the product recommendation method may further include the following operations.

[0088] Based on the node identifiers of multiple node vectors, a second sub-graph vector matching the preset object attribute information is determined from the graph vector; the second sub-graph vector is input into the potential object prediction model to obtain the target probability that the target object is a potential object; when it is determined that the target probability is greater than or equal to a preset probability threshold, the target object is determined to be a potential object.

[0089] According to an embodiment of the present disclosure, the preset object attribute information may be information that has an impact on determining whether the target object is a potential object, wherein the preset object attribute information may include: asset information, transaction frequency, credit score, product holdings, income level, natural attribute information and other information.

[0090] According to the embodiments of the present disclosure, the object contribution of the target object, the object's relative stickiness to the enterprise, the object's transaction activity and other information can be analyzed by presetting the object attribute information, and the analysis result can also be used as one of the inputs of the potential object prediction model, that is, in some embodiments, the second sub-graph vector includes not only vectors related to the preset object attribute information, but also vectors related to the object contribution information, the object's relative stickiness to the enterprise, etc.

[0091] According to an embodiment of the present disclosure, asset information may include: deposits, investment accounts, stocks, bonds, etc. Transaction frequency may include: withdrawals, transfers, investment transactions, etc. High-frequency transactions indicate that customers have a higher demand and participation in financial services. Credit scores may include scores calculated based on a customer's credit record and repayment record, which are used to assess a customer's credit risk and repayment ability. A high credit score is usually associated with a lower risk of default and is an important feature of a potential customer. Product holdings may include the number and types of products purchased and used by a customer in an enterprise, such as savings accounts, credit cards, loan financial products, etc. Diversified product holdings indicate that customers have a higher degree of stickiness and trust in the enterprise. Income level may include a customer's annual income or monthly income, which is an important basis for assessing a customer's economic strength and spending power. High-income customers have more financial resources and spending potential. Natural attribute information may include age, occupation, nature of work unit, professional background, educational background, etc., which can be used as a basis for identifying financial needs and preferences.

[0092] According to the embodiments of the present disclosure, the object contribution information can be evaluated by calculating the net contribution value brought by the customer to the bank, such as profit, income, etc. The object's relative stickiness information to the enterprise can include the number of years of being an enterprise customer, whether to recommend new customers, whether to use the bank's value-added services, etc. for evaluation. Highly sticky customers are more likely to become potential customers and maintain a stable cooperative relationship.

[0093] According to the embodiments of the present disclosure, there is no limitation on the implementation method of the potential object prediction model, which may be a neural network model, such as RBM, convolutional neural network, etc.

[0094] According to an embodiment of the present disclosure, the preset object attribute information can be matched with a plurality of node identifiers, thereby extracting a second sub-graph vector matching the preset object attribute information from the graph vector.

[0095] According to the embodiments of the present disclosure, there is no limitation on the preset probability threshold, which can be determined according to actual conditions, for example, it can be 0.6.

[0096] According to an embodiment of the present disclosure, in some embodiments, the probability that different target objects are potential objects may be as shown in Table 2.

[0097] Table 2

[0098]

[0099] According to the embodiments of the present disclosure, in the process of determining whether a target object is a potential object, by screening the second sub-graph vector related to the object attribute information from the graph vector, redundant input is reduced and prediction accuracy is improved. And by inputting the second sub-graph vector into the potential object prediction model, the target object is considered to be a potential object only when the target probability output by the object prediction sub-model is greater than or equal to a preset threshold, thereby realizing the screening of recommended objects and saving certain recommendation resources.

[0100] According to the embodiments of the present disclosure, by analyzing a large amount of object information and combining the machine learning capabilities of the restricted Boltzmann machine, individuals or groups with the potential to become high-end customers can be identified, thereby helping to more accurately position the market and formulate more targeted marketing strategies.

[0101] According to an embodiment of the present disclosure, the object information also includes: the association relationship between multiple sub-object information; converting the object information of the target object into a graph vector may include the following operations.

[0102] The object information is corrected to obtain the corrected object information; the corrected object information is extracted for multiple sub-object information and the association relationship between the multiple sub-object information is mapped to obtain a knowledge graph corresponding to the target object, wherein the knowledge graph includes nodes and edges, the nodes represent the sub-object information, and the edges represent the association relationship between the multiple sub-object information; the knowledge graph is mapped into the vector space to obtain a graph vector.

[0103] According to the embodiments of the present disclosure, information correction processing may include a variety of different methods for different problems. For example, if the information value in the object information is repeated, it can be corrected by deleting the repeated value; if the information value is missing or wrong, it can be corrected by filling in or deleting. That is, the obvious errors or abnormal values ​​beyond the reasonable range in the sub-object information included in the object information can be deleted.

[0104] According to the embodiments of the present disclosure, entities in object information can be defined, for example, entity definitions can be performed for sub-object information such as individual customers, corporate customers, accounts, products, and services, and the entity can be a node in a knowledge graph. The association relationship between multiple sub-object information in object information can be defined, such as: enterprise representative, shareholder, transfer, guarantee, purchase, consultation, etc. The attributes of the entity can also be defined, such as: customer name, age, etc.

[0105] According to an embodiment of the present disclosure, the defined entities, relationships, and attributes are mapped to obtain a knowledge graph of the target object.

[0106] Figure 3 A schematic diagram of a knowledge graph according to an embodiment of the present disclosure is schematically shown.

[0107] like Figure 3 As shown, in some embodiments, the knowledge graph of the target object A includes multiple nodes and edges. The nodes can be objects, enterprises, products, etc., such as: target object A, target object B, enterprise A, enterprise B, financial management, precious metals, etc., and the edges can be the association relationships between nodes, such as: purchase, transfer, shareholder, agency company, card consumption, purchase, etc.

[0108] According to an embodiment of the present disclosure, Figure 3 The knowledge graph in the figure is only illustrative, and in different embodiments, a knowledge graph with different numbers and different nodes and edges may be constructed.

[0109] According to the embodiments of the present disclosure, object information can be organized by constructing a knowledge graph to facilitate subsequent model learning, thereby improving the model calculation speed.

[0110] According to the embodiments of the present disclosure, there is no limitation on the specific means of mapping the knowledge graph to the value vector space, which may be the use of the TransE algorithm, mapping network, etc.

[0111] According to the embodiments of the present disclosure, in some embodiments, triples can be used to describe the relationship between nodes and edges in the knowledge graph. The relationship in each triple can be regarded as a translation from the head entity to the tail entity. Specifically, the scoring function can be shown as the following formula (1).

[0112] ; (1)

[0113] Among them, h is the head entity, t is the tail entity, and r is the relationship between the head entity and the tail entity.

[0114] According to an embodiment of the present disclosure, a boundary-based loss function (2) can be constructed based on the above formula (1).

[0115] ; (2)

[0116] in, is the positive sample scoring function, is the negative sample scoring function, γ>0 is the boundary parameter, is the negative sample triplet corresponding to T.

[0117] According to the embodiments of the present disclosure, since the entities, attributes and relationships in the knowledge graph are all represented by discrete and explicit symbolic representations, these discrete symbolic representations are difficult to apply to models based on continuous numerical representations. By mapping the entities and relationships in the knowledge graph to a low-dimensional, continuous vector space through representation learning, these continuous vectorized representations can be used as the input of the model, allowing the model to make full use of the prior knowledge in the knowledge graph, thereby obtaining more accurate potential user predictions and product recommendation lists.

[0118] According to the embodiments of the present disclosure, a detailed customer profile can be established through the knowledge graph, including its purchasing behavior, preferences, social relationships, etc. Combined with the product recommendation model and the potential object prediction model, it is possible to deeply explore the potential value of these data, understand the customer's needs and behavior patterns more accurately, and thus provide more personalized services and product recommendations.

[0119] According to an embodiment of the present disclosure, performing information correction processing on object information to obtain corrected object information may include the following operations.

[0120] Determine target sub-object information of numerical type from multiple sub-object information; when it is determined that there is an abnormality in the information format of the target object information, correct the target object information based on adjacent object information adjacent to the target object information to obtain first target sub-object information; when it is determined that there is an abnormality in the information value of the target object information, determine homogeneous object information with the same information identifier as the target object information; determine a replacement interval based on the information value of the homogeneous object information; correct the information value of the target object information to a corrected value in the replacement interval to obtain second target sub-object information; determine the corrected object information based on the first target sub-object information and the second target sub-object information.

[0121] According to the embodiment of the present disclosure, there is no limitation on the target sub-object information of numerical type, and it may be date, transaction amount, etc.

[0122] According to an embodiment of the present disclosure, the information formats of different target object information may be different. For example, the information format of the date may be year / month / day, where the value representing the month cannot exceed 12, and the value representing the day cannot exceed 31.

[0123] According to an embodiment of the present disclosure, if an abnormality is detected in the information format of a date, the dates adjacent to the current date can be determined and used as a date interval, thereby determining a correction date from the date interval and correcting the date.

[0124] According to the embodiments of the present disclosure, if an abnormality is detected in the information value of the target object, such as: the transaction amount is a negative number, the transaction amount is outlier, etc. Among them, the transaction amount outlier can be, for example: in the bank account transaction data of the target object, most of the transaction amounts are between a few hundred yuan and tens of thousands of yuan. If a transaction amount of tens of millions of yuan occurs, it may be an abnormal situation.

[0125] According to the embodiments of the present disclosure, in some embodiments, if there is an abnormality in the transaction amount, all transaction amounts of the target object or the transaction amounts within a period of time can be counted, and the replacement interval can be obtained by calculating the mean, median, standard deviation and other statistics of the transaction amounts. Specifically, in some embodiments, the replacement interval can be [mean - 3 times standard deviation, mean + 3 times standard deviation], etc.

[0126] According to an embodiment of the present disclosure, a value may be randomly determined from the replacement interval and used as the correction value, or the mean value may be used as the correction value, which is not limited.

[0127] According to the embodiments of the present disclosure, by correcting the object information, data quality can be ensured, computing efficiency can be improved, and problems such as missing values ​​and erroneous values ​​in the object information can be avoided, which will seriously affect the accuracy and reliability of the knowledge graph. The corrected object information can reduce noise interference in the calculation process and improve the performance and accuracy of the model.

[0128] Figure 4 The flowchart of the product recommendation model training method according to an embodiment of the present disclosure is schematically shown.

[0129] like Figure 4 As shown, the product recommendation model includes a category determination sub-model and a product recommendation sub-model, and the method includes operations S410 to S460.

[0130] In operation S410, first sub-graph vector samples of each of a plurality of potential object samples are obtained.

[0131] In operation S420, for each potential object sample, multiple category feature values ​​of the potential object sample are determined based on node identifiers and node values ​​of multiple node vector samples included in the first sub-graph vector sample.

[0132] In operation S430 , an object category label of the potential object sample is determined based on the plurality of category feature values.

[0133] In operation S440, based on the first sub-graph vector samples of each of the multiple potential object samples and the object category labels of each of the multiple potential object samples, the initial category determination sub-model is trained to obtain the category determination sub-model.

[0134] In operation S450, the initial product recommendation sub-model is trained based on the first sub-graph vector samples of each of the multiple potential object samples and the preferred product samples of the same type of object samples of each of the multiple potential object samples to obtain a product recommendation sub-model, wherein the object category of the same type of object sample is the same as the object category label of the potential object sample.

[0135] In operation S460 , a product recommendation model is obtained based on the category determination sub-model and the product recommendation sub-model.

[0136] According to an embodiment of the present disclosure, the product recommendation model is used to implement the above-mentioned product recommendation method.

[0137] According to an embodiment of the present disclosure, the first sub-graph vector sample of the potential object sample is obtained by performing steps such as operation S221 to operation S220 on the object information sample of the potential object sample.

[0138] According to an embodiment of the present disclosure, before obtaining the object information, a request for obtaining the object information may be issued to the potential object sample. When the potential object sample agrees or authorizes that the object information can be obtained, the object information of the target object is obtained. Similarly, before processing or making a decision on the object information of the potential object sample, the subsequent processing or decision-making process is performed after obtaining the consent or authorization of the potential object sample, which will not be described in detail in the present disclosure.

[0139] According to the embodiments of the present disclosure, the preferred products of the same type of objects and the preferred product samples of the same type of object samples are similarly acquired and processed after obtaining the consent of the same type of objects and the same type of object samples, which will not be elaborated in this disclosure.

[0140] According to the embodiments of the present disclosure, different category feature values ​​can characterize different aspects of the characteristics of potential object samples. In some embodiments, the category feature values ​​may include financial management ability, purchasing power, consumption level, payment ability, stable transaction factor or capital return ability, etc.

[0141] According to an embodiment of the present disclosure, the category feature value may be obtained by performing statistical processing on the node values ​​of multiple node identifiers corresponding to the category feature value, such as addition, subtraction, multiplication, division, normalization, logarithmic processing, inverse processing, etc.

[0142] According to an embodiment of the present disclosure, in some embodiments, taking the calculation of a stable transaction factor as an example, it can be obtained by performing relevant statistical processing on a node value corresponding to the following node identifier, wherein the node identifier may include income, deposits, liabilities, financial investment, information rating, and the like.

[0143] According to an embodiment of the present disclosure, there may be multiple object categories, and the object category may characterize the type of group to which the object belongs. In some embodiments, the object categories may include: high value category, high honor category, high purchasing power category, high stickiness category, etc. Among them, the high value category may characterize that the customer has a high purchasing power and consumption level. The high credibility category may characterize that the customer has a good reputation and stable payment ability. The high purchasing power category may characterize the purchase of a richer wealth management product. The high stickiness category may characterize that the customer has a more stable liquidity property.

[0144] In the fundamentally disclosed embodiments, different object categories may correspond to different preset weight groups, which include multiple preset category weights, each preset category weight corresponding to a category feature value, and the method for determining the preset weight group is not limited and may be obtained through experiments.

[0145] According to an embodiment of the present disclosure, multiple category feature values ​​are calculated with respective weight groups of each object category to obtain calculation results corresponding to multiple category labels respectively. The largest calculation result is determined from the calculation results corresponding to the multiple object categories respectively, and the object category corresponding to the largest calculation result is considered to be the object category label of the potential object.

[0146] According to an embodiment of the present disclosure, in some embodiments, the determined category feature values ​​and object category labels for different potential object samples may be as shown in Table 3.

[0147] Table 3

[0148]

[0149] Figure 5 A schematic diagram of category feature values ​​for different potential object samples according to an embodiment of the present disclosure is schematically shown.

[0150] like Figure 5 As shown, potential object sample A, potential object sample B, potential object sample C, and potential object sample D have their respective category characteristic values, which are financial management ability, purchasing power, consumption level, payment ability, stable transaction factor or capital repatriation ability.

[0151] According to an embodiment of the present disclosure, the category feature values ​​of potential object samples are calculated by using node identifiers and node values ​​corresponding to the first sub-graph vector samples, thereby achieving quantification of relevant feature tendencies of potential object samples, thereby achieving more fine-grained classification of potential object samples, and labeling potential object samples. By labeling multiple potential object samples with multiple category feature values, an initial category determination sub-model is trained using the first sub-graph vector samples of each of the multiple potential object samples and the object category labels of each of the multiple potential object samples to obtain a category determination sub-module that can achieve more fine-grained classification, and an initial product recommendation sub-model is trained using the first sub-graph vector samples and preferred product samples of similar object samples to obtain a product recommendation sub-model for recommending products.

[0152] According to an embodiment of the present disclosure, in the process of training a category determination sub-model, a product recommendation sub-model or a potential object prediction model, the training samples may be divided into a training set and a test set, such as dividing multiple potential object samples.

[0153] According to an embodiment of the present disclosure, when RBM is used to implement the above model, if the above model has m visible units and n hidden units, v and h represent the visible unit state and the hidden unit state respectively, then the energy function is as shown in the following formula (3).

[0154] ; (3)

[0155] Among them, a i is the weight of the i-th visible unit, b j is the weight of the jth hidden unit, w ij is the weight between the i-th visible unit and the j-th hidden unit.

[0156] According to an embodiment of the present disclosure, the number of nodes in the visible layer and the hidden layer can be determined according to the feature dimension of the training set. For example, if the training set contains 36 features, the number of nodes in the visible layer can be set to 36. In order to achieve the purpose of dimensionality reduction and feature extraction, the number of parameters of the hidden layer is generally less than the number of nodes in the visible layer, and the number of nodes can be continuously adjusted during the model training process.

[0157] According to an embodiment of the present disclosure, the weight matrix is ​​a two-dimensional array, in which rows identify visible layer units and columns identify hidden layer units. Initialization adopts uniform distribution. During the training process of the above model, the weight matrix needs to be updated according to the calculation situation until the model reaches the maximum number of iterations or the performance on the validation set is no longer improved. The bias items of the visible layer and the hidden layer correspond to the corresponding number of nodes. Like the weight matrix, the bias items need to be continuously updated during the training process of the model to achieve a better fitting effect.

[0158] According to the embodiments of the present disclosure, in some embodiments, taking the training of a potential object prediction model as an example, the object information of multiple initial object samples can be respectively divided into training sets and test sets, and the multiple initial object samples each have label data, and the identification data includes whether the initial object sample is a potential object. It should be noted that the object information of the initial object sample is also obtained or processed with the permission of the initial object sample, which will not be repeated here.

[0159] According to an embodiment of the present disclosure, model parameters, including weights and bias terms, can be initialized during the training process. Forward propagation: The training set is used as the visible layer input, and the activation value of the hidden layer is calculated through the weights and bias terms. The activation value of the hidden layer is converted into a probability distribution using an activation function such as a Sigmoid function. Backward propagation: Samples are generated based on the probability distribution of the hidden layer. These samples are used to calculate the reconstruction error, that is, the difference between the original input and the reconstructed input. The reconstruction error is used to calculate the gradient of the loss function with respect to the weights and bias terms. Based on the calculated gradient, an optimization algorithm such as gradient descent or its variants is used to update the weights and bias terms of the potential object prediction model. Forward propagation and back propagation are iteratively performed until the model converges or a preset number of iterations is reached.

[0160] According to an embodiment of the present disclosure, the performance of the potential object prediction model can be evaluated using a test set. The accuracy, recall, F1 score and other indicators of the potential object prediction results are calculated to evaluate the quality of the model. Based on the model evaluation results, the hyperparameters of the model, such as the learning rate, the number of iterations, etc., can be adjusted to improve the model performance.

[0161] Figure 6 The data flow diagram of the product recommendation model training method according to an embodiment of the present disclosure is schematically shown.

[0162] like Figure 6 As shown, in the process of training the product recommendation model, the object information 610 of the potential object samples can be collected first, and the object information 610 of the potential object samples can be corrected to obtain the corrected object information 620, and the knowledge graph sample 630 can be constructed through the corrected object information 620, and the knowledge graph sample can be vectorized to obtain the graph vector sample 640, and the graph vector sample 640 can be divided into a training set 650 and a test set 660, and the initial category determination sub-model and the initial product recommendation sub-model are trained through the training set 650 to obtain the intermediate product recommendation model 670, and the product recommendation model 670 is evaluated through the test set 660, and when it is determined that there is a problem with the intermediate product recommendation model, the intermediate product recommendation model 670 is optimized until it is determined that the intermediate product recommendation model meets the conditions and the product recommendation model 680 is obtained.

[0163] Based on the above product recommendation method and product recommendation model training method, the present disclosure also provides a product recommendation device and a product recommendation model training device. Figure 7~Figure 8 Describe it in detail.

[0164] Figure 7 The structural block diagram of the product recommendation device according to an embodiment of the present disclosure is schematically shown.

[0165] like Figure 7 As shown, the product recommendation device 700 includes a conversion module 710 , a first determination module 720 , a second determination module 730 and a recommendation module 740 .

[0166] The conversion module 710 is used to convert the object information of the target object into a graph vector with the permission of the target object, wherein the object information includes multiple sub-object information, the graph vector includes multiple node vectors, and the node vector is used to represent the object sub-information.

[0167] The first determination module 720 is used to determine a first sub-graph vector matching preset product attribute information from the graph vector based on node identifiers of multiple node vectors when the target object is determined to be a potential object based on the graph vector.

[0168] The second determination module 730 is used to input the first sub-graph vector into a product recommendation model to obtain the object category of the target object and a product recommendation list that matches both the object category and the target object.

[0169] The recommendation module 740 is used to recommend the to-be-recommended product determined from the product recommendation list to the target object.

[0170] According to an embodiment of the present disclosure, the second determination module 730 includes the following submodules.

[0171] The first input submodule is used to input the first sub-graph vector into the category determination submodel to obtain the object category of the target object.

[0172] The second input submodule is used to input the preferred products of the same type of objects with the object category and the first sub-graph vector into the product recommendation submodel to obtain a product recommendation list that matches both the object category and the target object.

[0173] According to an embodiment of the present disclosure, the second input submodule includes the following units.

[0174] The feature determination unit is used to input the preferred products of the same type of objects and the first sub-graph vector into the product recommendation sub-model to obtain the first preferred product features of the same type of objects and the second preferred product features of the target object.

[0175] The first calculation unit is used to calculate the similarity between the first preferred product features of the multiple similar objects and the product features of the multiple preset products to obtain the first similarities corresponding to the multiple preset products respectively.

[0176] The second calculation unit is used to calculate the similarity between the second preferred product feature and the product features of each of the plurality of preset products to obtain second similarities respectively corresponding to the plurality of preset products.

[0177] A recommendation value determination unit is used to determine the recommendation value of each of the multiple preset products based on a first weight value corresponding to the first preferred product feature, a first similarity corresponding to the multiple preset products, a second weight value corresponding to the second preferred product feature, and a second similarity corresponding to the multiple preset products.

[0178] The list determination unit is used to obtain a product recommendation list based on the recommendation values ​​of each of a plurality of preset products.

[0179] According to an embodiment of the present disclosure, the product recommendation device 700 further includes the following modules.

[0180] The fifth determination module is used to determine a second sub-graph vector matching the preset object attribute information from the graph vector based on the node identifiers of the multiple node vectors.

[0181] The probability determination module is used to input the second sub-graph vector into the potential object prediction model to obtain the target probability that the target object is a potential object.

[0182] The object determination module is used to determine that the target object is a potential object when the target probability is greater than or equal to a preset probability threshold.

[0183] According to an embodiment of the present disclosure, the object information further includes: association relationships between a plurality of sub-object information. The conversion module includes the following sub-modules.

[0184] The processing submodule is used to perform information correction processing on the object information to obtain corrected object information.

[0185] The graph generation submodule is used to extract multiple sub-object information and map the association relationship between multiple sub-object information from the corrected object information to obtain a knowledge graph corresponding to the target object, wherein the knowledge graph includes nodes and edges, the nodes represent the sub-object information, and the edges represent the association relationship between multiple sub-object information.

[0186] The vector conversion submodule is used to map the knowledge graph into the vector space to obtain the graph vector.

[0187] According to an embodiment of the present disclosure, the processing submodule includes the following units.

[0188] The first information determining unit is used to determine target sub-object information of a numerical type from a plurality of sub-object information.

[0189] The first correction unit is used to correct the target object information based on adjacent object information adjacent to the target object information to obtain first target sub-object information when it is determined that the information format of the target object information is abnormal.

[0190] The second information determining unit is used to determine homogeneous object information having the same information identifier as the target object information when it is determined that the information value of the target object information is abnormal.

[0191] The interval determination unit is used to determine the replacement interval based on the information value of the homogeneous object information.

[0192] The replacement unit is used to correct the information value of the target object information to the correction value in the replacement interval to obtain the second target sub-object information.

[0193] The third information determining unit is configured to determine the corrected object information based on the first target sub-object information and the second target sub-object information.

[0194] Figure 8 The structural block diagram of the product recommendation model training device according to an embodiment of the present disclosure is schematically shown.

[0195] like Figure 8 As shown, the product recommendation model training device 800 includes: an acquisition module 810, a third determination module 820, a fourth determination module 830, a first training module 840, a second training module 850, and a model determination module 860.

[0196] The acquisition module 810 is used to obtain the first sub-graph vector sample of each of the multiple potential object samples.

[0197] The third determination module 820 is used to determine, for each of the potential object samples, a plurality of category feature values ​​of the potential object sample based on the node identifiers and node values ​​of the plurality of node vector samples included in the first sub-graph vector sample.

[0198] The fourth determination module 830 is configured to determine the object category label of the potential object sample based on the plurality of category feature values.

[0199] The first training module 840 is used to train the initial category determination sub-model based on the first sub-graph vector samples of each of the multiple potential object samples and the object category labels of each of the multiple potential object samples to obtain the category determination sub-model.

[0200] The second training module 850 is used to train the initial product recommendation sub-model based on the first sub-graph vector samples of each of the multiple potential object samples and the preferred product samples of the same type of object samples of each of the multiple potential object samples, so as to obtain the product recommendation sub-model, wherein the object category of the same type of object samples is the same as the object category label of the potential object samples.

[0201] The model determination module 860 is used to determine the sub-model and the product recommendation sub-model based on the category to obtain the product recommendation model.

[0202] According to an embodiment of the present disclosure, any multiple modules among the conversion module 710, the first determination module 720, the second determination module 730, the recommendation module 740, the acquisition module 810, the third determination module 820, the fourth determination module 830, the first training module 840, the second training module 850, and the model determination module 860 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the conversion module 710, the first determination module 720, the second determination module 730, the recommendation module 740, the acquisition module 810, the third determination module 820, the fourth determination module 830, the first training module 840, the second training module 850, and the model determination module 860 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or may be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the conversion module 710, the first determination module 720, the second determination module 730, the recommendation module 740, the acquisition module 810, the third determination module 820, the fourth determination module 830, the first training module 840, the second training module 850, and the model determination module 860 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is executed.

[0203] Fig. 9 A block diagram of an electronic device suitable for implementing a product recommendation method and a product recommendation model training method according to an embodiment of the present disclosure is schematically shown.

[0204] like Fig. 9As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0205] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0206] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0207] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0208] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0209] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program includes a program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the product recommendation method and product recommendation model training method provided by the embodiment of the present disclosure.

[0210] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0211] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0212] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0213] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0214] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0215] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0216] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A product recommendation method, characterized in that: The method comprises: With permission from the target object, convert the object information of the target object into a graph vector, wherein the object information includes a plurality of sub-object information, the graph vector includes a plurality of node vectors, and the node vector is used to represent the object sub-information; In the case where the target object is determined to be a potential object based on the graph vector, determining a first sub-graph vector matching preset product attribute information from the graph vector based on node identifiers of a plurality of the node vectors; Inputting the first sub-graph vector into a product recommendation model to obtain an object category of the target object and a product recommendation list matching both the object category and the target object; The product to be recommended determined from the product recommendation list is recommended to the target object.

2. The method according to claim 1, characterized in that: The product recommendation model includes a category determination sub-model and a product recommendation sub-model; The step of inputting the first sub-graph vector into a product recommendation model to obtain an object category of the target object and a product recommendation list matching both the object category and the target object includes: Inputting the first sub-graph vector into the category determination sub-model to obtain the object category of the target object; The preferred products of the same type of objects with the object category and the first sub-graph vector are input into the product recommendation sub-model to obtain a product recommendation list that matches both the object category and the target object.

3. The method according to claim 2, characterized in that The step of inputting the preferred products of the same type of objects with the object category and the first sub-graph vector into the product recommendation sub-model to obtain a product recommendation list matching both the object category and the target object includes: Inputting the preferred products of the same type of objects and the first sub-graph vector into the product recommendation sub-model to obtain the first preferred product features of the same type of objects and the second preferred product features of the target object; Calculate the similarity between the first preferred product features of the plurality of similar objects and the product features of the plurality of preset products to obtain first similarities corresponding to the plurality of preset products; Calculate the similarity between the second preferred product feature and the product features of each of the plurality of preset products to obtain second similarities corresponding to the plurality of preset products respectively; Determine a recommendation value for each of the plurality of preset products based on a first weight value corresponding to the first preferred product feature, first similarities respectively corresponding to the plurality of preset products, a second weight value corresponding to the second preferred product feature, and second similarities respectively corresponding to the plurality of preset products; Based on the recommendation values ​​of each of the plurality of preset products, the product recommendation list is obtained.

4. The method according to claim 1, characterized in that The method further comprises: Based on the node identifiers of the plurality of node vectors, determining a second sub-graph vector matching the preset object attribute information from the graph vector; Inputting the second sub-graph vector into a potential object prediction model to obtain a target probability that the target object is a potential object; When it is determined that the target probability is greater than or equal to a preset probability threshold, the target object is determined to be a potential object.

5. The method according to claim 1, characterized in that The object information also includes: association relationships between multiple sub-object information; The converting the object information of the target object into a graph vector comprises: Performing information correction processing on the object information to obtain corrected object information; Extracting information of the plurality of sub-object information and mapping the association relationship between the plurality of sub-object information from the modified object information to obtain a knowledge graph corresponding to the target object, wherein the knowledge graph includes nodes and edges, the nodes represent the sub-object information, and the edges represent the association relationship between the plurality of sub-object information; The knowledge graph is mapped into a vector space to obtain the graph vector.

6. The method according to claim 1, characterized in that The performing information correction processing on the object information to obtain corrected object information includes: Determine target sub-object information of numerical type from the plurality of sub-object information; When it is determined that the information format of the target object information is abnormal, the target object information is modified based on adjacent object information adjacent to the target object information to obtain first target sub-object information; When it is determined that the information value of the target object information is abnormal, Determining homogeneous object information having the same information identifier as the target object information; Determining a replacement interval based on the information value of the homogeneous object information; Correcting the information value of the target object information to the correction value in the replacement interval to obtain second target sub-object information; The corrected object information is determined based on the first target sub-object information and the second target sub-object information.

7. A product recommendation model training method, characterized in that: The product recommendation model includes a category determination sub-model and a product recommendation sub-model; the method includes: Obtaining a first sub-atlas vector sample of each of a plurality of potential object samples; For each of the potential object samples, Determine multiple category feature values ​​of the potential object sample based on node identifiers and node values ​​of multiple node vector samples included in the first sub-graph vector sample; Determining an object category label of the potential object sample based on the plurality of category feature values; Based on the first sub-graph vector samples of each of the multiple potential object samples and the object category labels of each of the multiple potential object samples, an initial category determination sub-model is trained to obtain the category determination sub-model; Based on the first sub-graph vector samples of each of the multiple potential object samples and the preferred product samples of the same type of object samples of each of the multiple potential object samples, the initial product recommendation sub-model is trained to obtain the product recommendation sub-model, wherein the object category of the same type of object samples is the same as the object category label of the potential object samples; The product recommendation model is obtained based on the category determination sub-model and the product recommendation sub-model.

8. A product recommendation device, characterized in that: The device comprises: A conversion module, used to convert the object information of the target object into a graph vector with the permission of the target object, wherein the object information includes a plurality of sub-object information, the graph vector includes a plurality of node vectors, and the node vector is used to represent the object sub-information; A first determination module is used to determine, when the target object is determined to be a potential object based on the graph vector, a first sub-graph vector matching the preset product attribute information from the graph vector based on the node identifiers of the plurality of node vectors; A second determination module is used to input the first sub-graph vector into a product recommendation model to obtain an object category of the target object and a product recommendation list that matches both the object category and the target object; The recommendation module is used to recommend the recommended product determined from the product recommendation list to the target object.

9. A product recommendation model training device, characterized in that: The product recommendation model includes a category determination sub-model and a product recommendation sub-model; the device includes: An acquisition module, used to acquire a first sub-atlas vector sample of each of a plurality of potential object samples; A third determination module is used to determine, for each of the potential object samples, a plurality of category feature values ​​of the potential object sample based on the node identifiers and node values ​​of the plurality of node vector samples included in the first sub-graph vector sample; A fourth determination module, configured to determine an object category label of the potential object sample based on the plurality of category feature values; A first training module, configured to train an initial category determination submodel based on first sub-graph vector samples of each of the plurality of potential object samples and object category labels of each of the plurality of potential object samples, to obtain the category determination submodel; A second training module is used to train the initial product recommendation sub-model based on the first sub-graph vector samples of each of the multiple potential object samples and the preferred product samples of the same type of object samples of each of the multiple potential object samples to obtain the product recommendation sub-model, wherein the object category of the same type of object samples is the same as the object category label of the potential object samples; A model determination module is used to determine the sub-model and the product recommendation sub-model based on the category to obtain the product recommendation model.

10. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images