CN119477466A - Information recommendation method, device, electronic device and storage medium based on graph network - Google Patents
Information recommendation method, device, electronic device and storage medium based on graph network Download PDFInfo
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Abstract
本申请实施例提供了基于图网络的信息推荐方法、装置、电子设备及存储介质，属于金融科技技术领域。该方法包括：获取多个用户的用户标识集合以及多个用户的历史保单数据；根据用户标识集合以及历史保单数据构建网络图谱以选取第一节点和第二节点，并确定第一共同节点集合；当第一共同节点集合为非空集合，对第一节点和第二节点进行相似度计算，得到第一相似度值；当第一相似度值大于等于预设相似度阈值，确定第一保险产品集合和第二保险产品集合；向第一节点对应的用户推荐第二保险产品集合中的保险产品，并向第二节点对应的用户推荐第一保险产品集合中的保险产品。本申请实施例能够衡量用户之间的相似性，提高产品推荐的准确性。
The embodiments of the present application provide a method, device, electronic device and storage medium for information recommendation based on a graph network, which belongs to the field of financial technology technology. The method includes: obtaining a user identification set of multiple users and historical policy data of multiple users; constructing a network graph based on the user identification set and the historical policy data to select a first node and a second node, and determining a first common node set; when the first common node set is a non-empty set, performing a similarity calculation on the first node and the second node to obtain a first similarity value; when the first similarity value is greater than or equal to a preset similarity threshold, determining a first insurance product set and a second insurance product set; recommending insurance products in the second insurance product set to the user corresponding to the first node, and recommending insurance products in the first insurance product set to the user corresponding to the second node. The embodiments of the present application can measure the similarity between users and improve the accuracy of product recommendations.
Description
Technical Field
The present application relates to the technical field of financial science and technology, and in particular, to an information recommendation method, an apparatus, an electronic device, and a storage medium based on a graph network.
Background
In the insurance industry, providing personalized insurance product recommendations to customers is a critical task. With the dramatic increase in the number of insurance products, it is often difficult for the applicant to select from a number of options, and conventional recommendation methods often fail to effectively address such information overload issues.
The traditional recommendation method mainly depends on historical purchase records and basic information of clients, and the method can reflect the purchase habits of the clients to a certain extent, but ignores potential association and social influence among the clients, so that recommendation results lack of diversity and innovation, and the opportunity of providing more accurate recommendation is missed. And this approach often suffers from cold start problems when dealing with new customers or products, because of a lack of enough historical data, and it is difficult to provide accurate recommendations.
Disclosure of Invention
The embodiment of the application mainly aims to provide an information recommending method, an information recommending device, electronic equipment and a storage medium based on a graph network, and the accuracy of product recommendation is improved by measuring the similarity among users to provide an accurate insurance product recommending result.
To achieve the above object, a first aspect of an embodiment of the present application provides an information recommendation method based on a graph network, where the method includes:
acquiring a user identification set of a plurality of users and historical policy data of the plurality of users, wherein the historical policy data comprises at least one insurance product;
constructing a network map according to the user identification set and the historical policy data, wherein different nodes in the network map correspond to different users;
Selecting a first node and a second node from the network map, and determining a first common node set connected with the first node and the second node, wherein the common node in the first common node set is simultaneously connected with the first node and the second node;
When the first common node set is a non-empty set, performing similarity calculation on the first node and the second node based on the first common node set and the network map to obtain a first similarity value;
When the first similarity value is greater than or equal to a preset similarity threshold value, determining a first insurance product set corresponding to the first node, and determining a second insurance product set corresponding to the second node;
Recommending the insurance products in the second insurance product set to the users corresponding to the first node, and recommending the insurance products in the first insurance product set to the users corresponding to the second node.
In some embodiments, the insurance product is provided with insurance identifications, the set of user identifications includes a plurality of user identifications, and the building a network map according to the set of user identifications and the historical policy data includes:
Performing duplicate removal operation on the historical policy data based on the insurance identifier to obtain target policy data;
constructing binary group data according to the target policy data and the user identification;
and constructing a network map according to the binary group data.
In some embodiments, the performing a deduplication operation on the historical policy data based on the insurance identifier to obtain target policy data includes:
Classifying the historical policy data based on the insurance identifier to obtain a plurality of policy subgroups;
Detecting the number of insurance products in each insurance policy group, determining the insurance policy group with the number of insurance products larger than the preset number as a first insurance policy group, and determining the insurance policy group with the number of insurance products smaller than the preset number as a second insurance policy group;
acquiring the policy time of each policy product in the first policy group to obtain a policy time set;
comparing all the policy times in the policy time set, screening out the target policy time closest to the current moment, and determining a target insurance product corresponding to the target policy time;
updating the first policy group based on the target insurance product;
And obtaining target policy data according to the updated first policy subgroup and the updated second policy subgroup.
In some embodiments, the constructing a network map from the tuple data comprises:
Acquiring a user identifier in the binary group data and a product identifier of the target policy data;
carrying out association analysis on the user identification and the product identification to obtain an association relation;
And taking the user corresponding to the user identifier as a node, and connecting a plurality of nodes according to the association relation to construct a network map.
In some embodiments, the performing similarity calculation on the first node and the second node based on the first common node set and the network map to obtain a first similarity value includes:
For each common node in the first common node set, determining the number of connecting lines of the common node according to the network map;
Carrying out logarithmic operation on the common nodes according to the number of the connecting lines to obtain an operation result;
and accumulating the operation results of all the common nodes to obtain a first similarity value.
In some embodiments, after performing similarity calculation on the first node and the second node based on the first common node set and the network map, to obtain a first similarity value, the method further includes:
When the first similarity value is smaller than a preset similarity threshold value, selecting a third node from the network map, and determining a second common node set connected with the first node and the third node, wherein the common node in the second common node set is simultaneously connected with the first node and the third node;
When the second common node set is a non-empty set, performing similarity calculation on the first node and the third node based on the second common node set and the network map to obtain a second similarity value;
When the second similarity value is greater than or equal to the preset similarity threshold, determining a first insurance product set corresponding to the first node, and determining a third insurance product set corresponding to the third node;
Recommending the insurance products in the third insurance product set to the users corresponding to the first node, and recommending the insurance products in the first insurance product set to the users corresponding to the third node.
In some embodiments, the determining a first set of insurance products corresponding to the first node and determining a second set of insurance products corresponding to the second node includes:
Determining a first connection line connected with the first node in the network map, and determining a first product identifier of the first connection line;
determining a first insurance product corresponding to the first product identifier to generate a first insurance product set;
Determining a second connecting line connected with the second node in the network map, and determining a second product identifier of the second connecting line;
a second insurance product corresponding to the second product identity is determined to generate a second insurance product set.
To achieve the above object, a second aspect of an embodiment of the present application provides an information recommendation device based on a graph network, the device including:
The system comprises a data acquisition module, a storage module and a storage module, wherein the data acquisition module is used for acquiring a user identification set of a plurality of users and historical policy data of the plurality of users, and the historical policy data comprises at least one insurance product;
The map construction module is used for constructing a network map according to the user identification set and the historical policy data, and different nodes in the network map correspond to different users;
The node determining module is used for selecting a first node and a second node from the network map, and determining a first common node set connected with the first node and the second node, wherein the common node in the first common node set is simultaneously connected with the first node and the second node;
The similarity calculation module is used for calculating the similarity of the first node and the second node based on the first common node set and the network map to obtain a first similarity value when the first common node set is a non-empty set;
the set determining module is used for determining a first insurance product set corresponding to the first node and determining a second insurance product set corresponding to the second node when the first similarity value is greater than or equal to a preset similarity threshold value;
and the product recommending module is used for recommending the insurance products in the second insurance product set to the users corresponding to the first node and recommending the insurance products in the first insurance product set to the users corresponding to the second node.
To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the graph network-based information recommendation method according to the first aspect.
To achieve the above object, a fourth aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the graph network-based information recommendation method according to the first aspect.
The information recommending method, the device, the electronic equipment and the storage medium based on the graph network, firstly, the user identification sets of a plurality of users and the historical insurance data of the plurality of users, namely the historical insurance products purchased by the users, are obtained, then, a network graph is constructed according to the user identification sets and the historical insurance data, the relationship among the users can be intuitively displayed through the network graph, then, a first node and a second node are arbitrarily selected in the network graph, and a first common node set connected with the first node and the second node is determined, so that the node commonly connected with the first node and the second node can be determined, the common insurance products owned by the users corresponding to the first node and the users corresponding to the second node can be further determined, when the first common node set is a non-empty set, indicating that the users corresponding to the first node and the users corresponding to the second node have common insurance products, at this time, similarity calculation can be performed on the first node and the second node based on the first common node set and the network map to judge the similarity degree between the first node and the second node, so as to obtain a first similarity value, realize the measurement of the similarity between different users, further analyze the potential association between the users, and when the first similarity value is greater than or equal to a preset similarity threshold value, indicate that the potential association exists between the users corresponding to the first node and the users corresponding to the second node, and the insurance products held by the users corresponding to the two nodes are similar, at this time, determine the first insurance product set corresponding to the first node, and determine the second insurance product set corresponding to the second node, so as to respectively determine the insurance products purchased or being held by different users, and finally, recommending the insurance products in the second insurance product set to the users corresponding to the first node, recommending the insurance products in the first insurance product set to the users corresponding to the second node, providing more accurate and comprehensive personalized recommendation by utilizing the similarity and the relevance among the users, and increasing the user satisfaction. According to the embodiment of the application, the potential association between the users is analyzed by calculating the similarity between the users, so that the requirements and the preferences of the users can be more accurately and comprehensively captured, and more personalized insurance product recommendation is provided.
Drawings
Fig. 1 is a flowchart of an information recommendation method based on a graph network according to an embodiment of the present application;
fig. 2 is a flowchart of step S102 in fig. 1;
fig. 3 is a flowchart of step S201 in fig. 2;
fig. 4 is a flowchart of step S203 in fig. 2;
FIG. 5 is a flowchart of a method for performing similarity calculation on a first node and a second node based on a first common node set and a network map according to an embodiment of the present application;
FIG. 6 is a flowchart of a method for recommending information based on a graph network according to another embodiment of the present application;
FIG. 7 is a flowchart of a method for determining a first insurance product set and a second insurance product set according to an embodiment of the present application;
Fig. 8 is a schematic structural diagram of an information recommendation device based on a graph network according to an embodiment of the present application;
Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
First, several nouns involved in the present application are parsed:
Natural language processing (Natural Language Processing, NLP) NLP is a branch of artificial intelligence, which is a interdisciplinary of computer science and linguistics, and is often referred to as computational linguistics, where NLP is processed, understood, and applied in human language (e.g., chinese, english, etc.). Natural language processing includes parsing, semantic analysis, chapter understanding, and the like. Natural language processing is commonly used in the technical fields of machine translation, handwriting and print character recognition, voice recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining, and the like, and relates to data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, linguistic research related to language calculation, and the like.
According to the information recommendation method and device based on the graph network, the electronic equipment and the storage medium, which are provided by the embodiment of the application, the accurate insurance product recommendation result is provided by measuring the similarity between users, and the accuracy of product recommendation is improved.
The information recommendation method based on the graph network in the embodiment of the application is described first by describing the following embodiment.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligent software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a module management technology of an online guest receiving system, a natural language processing technology, machine learning/deep learning and other directions.
The embodiment of the application provides an information recommendation method based on a graph network, and relates to the technical field of financial science and technology. The information recommendation method based on the graph network provided by the embodiment of the application can be applied to a terminal, a server and software running in the terminal or the server. In some embodiments, the terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., the server may be configured as an independent physical server, may be configured as a server cluster or a distributed system formed by a plurality of physical servers, and may be configured as a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligent platforms, and the software may be an application for implementing an information recommendation method based on a graph network, but is not limited to the above form.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. Such as a personal computer, a server computer, a hand-held or portable device, a tablet device, a multiprocessor system, a microprocessor-based system, a set top box, a programmable consumer electronics, a network PC, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In the embodiments of the present application, when related processing is performed according to user information, user behavior data, user history data, user location information, and other data related to user identity or characteristics, permission or consent of the user is obtained first, and the collection, use, processing, and the like of the data comply with related laws and regulations and standards of related countries and regions. In addition, when the embodiment of the application needs to acquire the sensitive personal information of the user, the independent permission or independent consent of the user is acquired through popup or jump to a confirmation page and the like, and after the independent permission or independent consent of the user is definitely acquired, the necessary relevant data of the user for enabling the embodiment of the application to normally operate is acquired.
In the insurance industry, providing personalized insurance product recommendations to customers is a critical task. With the dramatic increase in the number of insurance products, it is often difficult for the applicant to select from a number of options, and conventional recommendation methods often fail to effectively address such information overload issues.
The traditional recommendation method mainly depends on historical purchase records and basic information of clients, and the method can reflect the purchase habits of the clients to a certain extent, but ignores potential association and social influence among the clients, so that recommendation results lack of diversity and innovation, and the opportunity of providing more accurate recommendation is missed. And this approach often suffers from cold start problems when dealing with new customers or products, because of a lack of enough historical data, and it is difficult to provide accurate recommendations.
In order to solve the above-mentioned problems, the present embodiment provides an information recommendation method, an apparatus, an electronic device and a storage medium based on a graph network, firstly, obtain a user identifier set of a plurality of users and historical policy data of the plurality of users, that is, historical insurance products purchased by the users, then construct a network graph according to the user identifier set and the historical policy data, intuitively display a relationship between the users through the network graph, then, randomly select a first node and a second node in the network graph, and determine a first common node set connected with the first node and the second node, thereby being capable of determining a node commonly connected with the first node and the second node, further being capable of determining a common insurance product owned by the user corresponding to the first node and the user corresponding to the second node, when the first common node set is a non-empty set, indicating that the user corresponding to the first node and the user corresponding to the second node have a common insurance product, at this time, be capable of performing similarity calculation on the first node and the second node based on the first common node set and the network, to determine a potential similarity value between the first node and the second node, and the first node being capable of further determining a potential similarity value between the first node and the second node, and the first node being relatively similar to have a potential similarity value, and the first node being relatively high in the similarity value, and the first node being relatively similar to the first node, therefore, the insurance products purchased or being held by different users can be respectively determined, finally, the insurance products in the second insurance product set are recommended to the users corresponding to the first node, the insurance products in the first insurance product set are recommended to the users corresponding to the second node, and more accurate and comprehensive personalized recommendation is provided by utilizing the similarity and the relevance among the users, so that the user satisfaction is increased. According to the embodiment of the application, the potential association between the users is analyzed by calculating the similarity between the users, so that the requirements and the preferences of the users can be more accurately and comprehensively captured, and more personalized insurance product recommendation is provided.
The following is a detailed description with reference to the accompanying drawings.
Fig. 1 is an optional flowchart of a method for recommending information based on a graph network according to an embodiment of the present application, where the method in fig. 1 may include, but is not limited to, steps S101 to S106.
Step S101, acquiring a user identification set of a plurality of users and historical policy data of the plurality of users, wherein the historical policy data comprises at least one insurance product.
In step S101 of some embodiments, a set of user identifiers of a plurality of users is obtained, where the set of user identifiers stores user identifiers of different users, such as an identification card number, a mobile phone number, and so on, and historical policy data of the plurality of users, that is, insurance products that are being held or were held by different users, and purchase records of the insurance products, and so on, are obtained, so that subsequent analysis of insurance preferences of different users is facilitated.
And step S102, constructing a network map according to the user identification set and the historical policy data.
In step S102 of some embodiments, a network map is constructed according to the user identification set and the historical policy data, and relationships between users, such as social networks, similarity of purchase behaviors, and the like, can be intuitively displayed through the map, so as to help discover potential connections in the user group.
Step S103, selecting a first node and a second node from the network map, and determining a first common node set connected with the first node and the second node.
The common nodes in the first common node set are connected to the first node and the second node at the same time.
In step S103 of some embodiments, a first node and a second node are selected in a network map, where the first node and the second node in the embodiments of the present application may be selected randomly or sequentially, and the embodiments of the present application are not limited in particular, and a common node connected to the first node and the second node at the same time is determined in the network map, and the common node is sorted into a first common node set, so as to implement collection of products held together by a user corresponding to the first node and a user corresponding to the second node.
Step S104, when the first common node set is a non-empty set, similarity calculation is performed on the first node and the second node based on the first common node set and the network map, so as to obtain a first similarity value.
In step S104 of some embodiments, when the first common node set is a non-empty set, it is indicated that there is a common node between the first node and the second node, that is, the users corresponding to the two nodes hold a common product, and at this time, similarity calculation is performed on the first node and the second node based on the first common node set and the network map to obtain a first similarity value, so as to determine a similarity between the user corresponding to the first node and the user corresponding to the second node, so that subsequent correlation analysis is conveniently performed on the user corresponding to the first node and the user corresponding to the second node based on the similarity, and further, the similarity between the two users is determined.
Step S105, when the first similarity value is greater than or equal to a preset similarity threshold, determining a first insurance product set corresponding to the first node, and determining a second insurance product set corresponding to the second node.
In step S105 of some embodiments, when the first similarity value is greater than or equal to the preset similarity threshold, it is indicated that the products held by the user corresponding to the first node and the user corresponding to the second node are relatively similar, and the first insurance product set corresponding to the first node and the second insurance product set corresponding to the second node may be directly determined, so as to implement accurate recommendation of the first insurance product and the second insurance product, and comprehensively and accurately recommend relevant products interested in the user to the user, thereby improving user experience.
It should be noted that, the preset similarity threshold may be set according to the requirement of the user, and the embodiment of the present application is not particularly limited.
Step S106, recommending the insurance products in the second insurance product set to the users corresponding to the first node, and recommending the insurance products in the first insurance product set to the users corresponding to the second node.
In step S106 of some embodiments, insurance products in the second insurance product set are recommended to users corresponding to the first node, and insurance products in the first insurance product set are recommended to users corresponding to the second node, so that more accurate and comprehensive personalized recommendation is provided by using similarity and relevance among users, and user satisfaction is increased.
Referring to fig. 2, in some embodiments, step S102 may further include, but is not limited to, steps S201 to S203.
It should be noted that, the insurance product is provided with insurance identifications, and the user identification set includes a plurality of user identifications.
Step S201, performing duplicate removal operation on the historical policy data based on the insurance identifier to obtain target policy data.
Step S202, constructing the binary group data according to the target policy data and the user identification.
And step S203, constructing a network map according to the binary data.
In steps S201 to S203 of some embodiments, in the process of constructing a network map according to a user identifier set and historical policy data, different types of insurance products are provided with different insurance identifiers, that is, the insurance products are in one-to-one correspondence with the insurance identifiers, so that the embodiment of the application can perform deduplication operation on the historical policy data based on the insurance identifiers, thereby avoiding interference of multiple repeated policy data of the same insurance product, reducing the number of data to be processed, improving the efficiency of processing the data, obtaining target policy data, deduplicating to ensure consistency and accuracy of the data, avoiding analysis and decision errors caused by the repeated data, then constructing binary group data according to the target policy data and the user identifier, obtaining a data set corresponding to the user identifier and the target policy data, and finally constructing a network map according to the binary group data, so as to intuitively display relationships among users, such as social networks, purchase behavior similarity and the like, and help to find potential connections in a user group.
Referring to fig. 3, in some embodiments, step S201 may further include, but is not limited to, steps S301 to S306.
Step S301, classifying the historical policy data based on the insurance identifications to obtain a plurality of policy subgroups.
Step S302, detecting the number of insurance products in each policy group, determining the policy group with the number of insurance products larger than the preset number as a first policy group, and determining the policy group with the number of insurance products smaller than or equal to the preset number as a second policy group.
Step S303, acquiring the policy time of each policy product in the first policy group to obtain a policy time set.
Step S304, comparing all the policy times in the policy time set, screening out the target policy time nearest to the current moment, and determining the target insurance product corresponding to the target policy time.
Step S305, updating the first policy group according to the target insurance product.
Step S306, obtaining the target policy data according to the updated first policy group and the updated second policy group.
In steps S301 to S306 of some embodiments, the embodiment of the present application classifies the history policy data based on the policy identifier, classifies the history policy data into a plurality of policy subgroups of different categories, for example, a pension policy, an automobile policy, a child care policy, etc., and because the condition of renewing the same policy may occur, a plurality of repeated singles may occur at this time to cause redundancy of data, in order to avoid the occurrence of a plurality of renewing policies for the same policy product, the embodiment of the present application needs to detect the number of policies in each policy subgroup, and determine a policy subgroup with the number of policies greater than the preset number as a first policy subgroup, that is, a policy subgroup with the number of policies less than or equal to the preset number as a second policy subgroup, to implement screening of the insured products, then, acquiring the insurance time of each insurance product in the first insurance policy group, namely the participation time of the user, obtaining the insurance time set, thereby realizing statistics of all participation times, comparing all insurance times in the insurance time set, screening out the target insurance time closest to the current moment, namely the last participation or renewing date, determining the target insurance product corresponding to the target insurance time, thereby screening out the target insurance product required by the user, finally updating the first insurance policy group according to the insurance product, namely replacing all insurance products in the first insurance policy group by the target insurance product, thereby ensuring that only one insurance product exists in the first insurance policy group, ensuring consistency and accuracy of the same insurance product, ensuring that the renewed insurance product is only taken once in the current effect, the repeated interference of the data is avoided, the target policy data is obtained according to the updated first policy group and the updated second policy group, analysis and decision errors caused by the repeated data are avoided, the complexity and cost of data processing are reduced, and the operation efficiency is improved.
It should be noted that, in the embodiment of the present application, the preset number may be set according to the needs of the user, for example, the preset number is set to 1,2, or 3, etc., and the embodiment of the present application is illustrated by taking the preset number as 1 as an example, that is, the policy group with the number of insurance products greater than 1 is determined as the first policy group, and the policy group with the number of insurance products less than or equal to 1 is determined as the second policy group.
Referring to fig. 4, in some embodiments, step S203 may further include, but is not limited to, steps S401 to S403.
Step S401, obtaining a user identifier in the binary group data and a product identifier of the target policy data.
Step S402, carrying out association analysis on the user identification and the product identification to obtain an association relationship.
Step S403, the user corresponding to the user identifier is used as a node, and a plurality of nodes are connected according to the association relationship so as to construct a network map.
In steps S401 to S403 of some embodiments, in a process of constructing a network map according to binary data, the embodiment of the application firstly obtains a user identifier and a product identifier of target policy data in the binary data, then performs association analysis on the user identifier and the product identifier, analyzes a corresponding relationship between a user and an insurance product, thereby being capable of determining whether the user holds or has purchased the insurance product, obtaining an association relationship between the user and the insurance product, finally uses the user corresponding to the user identifier as a node, connects a plurality of nodes according to the association relationship, and uses edges existing between the two nodes as the same insurance product commonly held between the two users, namely, each edge connection represents that the two users hold the same insurance product, so as to clearly describe the corresponding relationship between the user and the insurance product, intuitively display the relationship between the users through the map, such as social network, purchase behavior similarity and the like, and be helpful for finding potential connections in a user group.
It can be understood that when three edges exist between two nodes, it is indicated that the users corresponding to the two nodes hold three identical insurance products together, respectively, and when two edges exist between the two nodes, it is indicated that the users corresponding to the two nodes hold two identical insurance products together, respectively, and so on, and the embodiment of the application is not limited in particular.
Referring to fig. 5, fig. 5 is a flowchart of a method for performing similarity calculation on a first node and a second node based on a first common node set and a network map according to an embodiment of the application, where the method includes, but is not limited to, steps S501 to S503.
Step S501, for each common node in the first common node set, determining the number of connection lines of the common node according to the network map.
Step S502, carrying out logarithmic operation on the common nodes according to the number of the connecting lines to obtain an operation result.
Step S503, accumulating the operation results of all the common nodes to obtain a first similarity value.
In steps S501 to S503 of some embodiments, in a process of performing similarity calculation on a first node and a second node based on a first common node set and a network map, since the common nodes in the first common node set are connected with the first node and the second node at the same time, the embodiments of the present application first determine the number of connecting lines of each common node in the first common node set, so as to determine the number of common insurance products held by users corresponding to the first node and users corresponding to the second node, then perform logarithmic operation on the common nodes according to the number of connecting lines, specifically, take the common nodes of the first node a and the second node B as C and D as an example, and the common node C has three connecting lines, and the common node D has four connecting lines, at this time, 1/log|n (C) |=1/log 3= 2.0959, and 1/|n (B) |=1 log 4= 1.6610, so as to obtain two operation results of 2.0959 and 1.6610, and then accumulate the operation results of all the common nodes, that is, E (66d, E) |1.10=2+34, so as to further obtain the similarity between the corresponding first node and the second node and the first node can be further analyzed to obtain the similarity between the corresponding first node and the second node, and the corresponding user similarity.
Referring to fig. 6, fig. 6 is a flowchart of a method for recommending information based on a graph network according to another embodiment of the present application, including but not limited to steps S601 to S604.
It should be noted that, after similarity calculation is performed on the first node and the second node based on the first common node set and the network map, a first similarity value is obtained.
Step S601, when the first similarity value is smaller than a preset similarity threshold, selecting a third node from the network map, and determining a second common node set connected with the first node and the third node.
The common node in the second common node set is connected to both the first node and the third node.
Step S602, when the second common node set is a non-empty set, similarity calculation is performed on the first node and the third node based on the second common node set and the network map, so as to obtain a second similarity value.
Step S603, when the second similarity value is greater than or equal to the preset similarity threshold, determining a first insurance product set corresponding to the first node, and determining a third insurance product set corresponding to the third node.
Step S604, recommending the insurance products in the third insurance product set to the users corresponding to the first node, and recommending the insurance products in the first insurance product set to the users corresponding to the third node.
In steps S601 to S604 of some embodiments, after performing similarity calculation on the first node and the second node based on the first common node set and the network map to obtain a first similarity value, comparing the first similarity value with a preset similarity threshold, when the first similarity value is smaller than the preset similarity threshold, indicating that the association between the user corresponding to the first node and the user corresponding to the second node is lower, two users basically do not have similar insurance products, if the users corresponding to the first node or the second node subsequently continue to have similar insurance products, the recommendation accuracy is reduced, the user experience sense is affected, in order to avoid the situation that the first similarity value is smaller than the preset similarity threshold, a third node is selected in the network map, and a second common node set connected with the first node and the third node is determined, so that the second common node set is a non-aggregate, and the association between the user corresponding to the first node and the third node can be directly calculated based on the second common node and the first node, and the second node is more than the preset similarity value, and the similarity between the first node and the second node is more similar to the first node is more than the first common node, and the second node is more similar to the first node is more than the preset similarity threshold, and the similarity value is more similar to the first node is calculated, and the first similarity value is more similar to the first node corresponding to the first node, the first insurance product set corresponding to the first node can be directly determined, and the third insurance product set corresponding to the third node is determined, so that insurance products purchased or held by different users can be respectively determined, insurance products in the third insurance product set are recommended to users corresponding to the first node, insurance products in the first insurance product set are recommended to users corresponding to the third node, and more accurate and comprehensive personalized recommendation is provided by using similarity and relevance among the users, so that user satisfaction is increased.
It should be noted that, when the second similarity value is smaller than the preset similarity threshold, other nodes in the network map are selected to perform similarity calculation, that is, steps S601 to S604 are repeated, which is not repeated herein.
Referring to fig. 7, fig. 7 is a flowchart of a method for determining a first insurance product set and a second insurance product set according to an embodiment of the present application, and the method in fig. 7 may include, but is not limited to, steps S701 to S704.
Step S701, determining a first connection line connected to a first node in a network map, and determining a first product identifier of the first connection line.
Step S702, determining a first insurance product corresponding to the first product identifier to generate a first insurance product set.
Step S703, determining a second connection line connected to the second node in the network map, and determining a second product identifier of the second connection line.
Step S704, determining a second insurance product corresponding to the second product identifier to generate a second insurance product set.
In steps S701 to S704 of some embodiments, in determining the first insurance product set and the second insurance product set, embodiments of the present application may determine one or more first connection lines connected to the first node in the network map, determine a first product identifier of the first connection line, and determine one or more first insurance products corresponding to the first product identifier, thereby generating the first insurance product set. Likewise, a second connecting line connected with the second node is determined in the network map, a second product identifier corresponding to the second connecting line is determined, and one or more second insurance products corresponding to the second product identifier are determined, so that a second insurance product set is generated, accurate recommendation of the first insurance product and the second insurance product is realized, relevant products interested in the user can be comprehensively and accurately recommended, and user experience is improved.
Referring to fig. 8, an embodiment of the present application further provides an information recommendation device based on a graph network, where the device includes:
A data obtaining module 801, configured to obtain a user identifier set of a plurality of users and historical policy data of the plurality of users, where the historical policy data includes at least one insurance product;
A map construction module 802, configured to construct a network map according to the user identifier set and the historical policy data;
A node determining module 803, configured to select a first node and a second node from the network map, and determine a first common node set connected to the first node and the second node, where the common node in the first common node set is connected to the first node and the second node at the same time;
The similarity calculation module 804 is configured to calculate, when the first common node set is a non-empty set, a similarity between the first node and the second node based on the first common node set and the network map, so as to obtain a first similarity value;
the set determining module 805 is configured to determine a first insurance product set corresponding to the first node and determine a second insurance product set corresponding to the second node when the first similarity value is greater than or equal to a preset similarity threshold;
the product recommendation module 806 is configured to recommend the insurance products in the second insurance product set to the user corresponding to the first node, and recommend the insurance products in the first insurance product set to the user corresponding to the second node.
The specific implementation of the information recommending apparatus based on the graph network is basically the same as the specific embodiment of the information recommending method based on the graph network, and is not described herein.
The embodiment of the application also provides electronic equipment, which comprises a memory, a processor, a program stored on the memory and capable of running on the processor and a data bus for realizing connection communication between the processor and the memory, wherein the program is executed by the processor to realize the information recommendation method based on the graph network. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
Referring to fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:
The processor 901 may be implemented by a general purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solution provided by the embodiments of the present application;
The Memory 902 may be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 902 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present disclosure are implemented by software or firmware, relevant program codes are stored in the memory 902, and the processor 901 invokes the graph network-based information recommendation method for executing the embodiments of the present disclosure;
An input/output interface 903 for inputting and outputting information;
The communication interface 904 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);
A bus 905 that transfers information between the various components of the device (e.g., the processor 901, the memory 902, the input/output interface 903, and the communication interface 904);
Wherein the processor 901, the memory 902, the input/output interface 903 and the communication interface 904 are communicatively coupled to each other within the device via a bus 905.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the information recommendation method based on the graph network when being executed by a processor.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The information recommending method, the device, the electronic equipment and the storage medium based on the graph network provided by the embodiment of the application firstly acquire the user identification sets of a plurality of users and the historical insurance data of the plurality of users, namely the historical insurance products which the users have purchased, then construct a network graph according to the user identification sets and the historical insurance data, intuitively display the relation among the users through the network graph, then randomly select a first node and a second node in the network graph, and determine a first common node set connected with the first node and the second node, thereby determining the node commonly connected with the first node and the second node, further determining the common insurance products which the users corresponding to the first node and the second node have, when the first common node set is a non-empty set, indicating that the users corresponding to the first node and the users corresponding to the second node have common insurance products, at this time, similarity calculation can be performed on the first node and the second node based on the first common node set and the network map to judge the similarity degree between the first node and the second node, so as to obtain a first similarity value, realize the measurement of the similarity between different users, further analyze the potential association between the users, and when the first similarity value is greater than or equal to a preset similarity threshold value, indicate that the potential association exists between the users corresponding to the first node and the users corresponding to the second node, and the insurance products held by the users corresponding to the two nodes are similar, at this time, determine the first insurance product set corresponding to the first node, and determine the second insurance product set corresponding to the second node, so as to respectively determine the insurance products purchased or being held by different users, and finally, recommending the insurance products in the second insurance product set to the users corresponding to the first node, recommending the insurance products in the first insurance product set to the users corresponding to the second node, providing more accurate and comprehensive personalized recommendation by utilizing the similarity and the relevance among the users, and increasing the user satisfaction. According to the embodiment of the application, the potential association between the users is analyzed by calculating the similarity between the users, so that the requirements and the preferences of the users can be more accurately and comprehensively captured, and more personalized insurance product recommendation is provided.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the solutions shown in fig. 1-9 are not limiting on the embodiments of the application and may include more or fewer steps than shown, or certain steps may be combined, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" is used to describe an association relationship of an associated object, and indicates that three relationships may exist, for example, "a and/or B" may indicate that only a exists, only B exists, and three cases of a and B exist simultaneously, where a and B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b or c may represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. The storage medium includes various media capable of storing programs, such as a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106776707A (en) * | 2016-11-11 | 2017-05-31 | 百度在线网络技术（北京）有限公司 | The method and apparatus of information pushing |
| US20200320646A1 (en) * | 2018-04-26 | 2020-10-08 | Tencent Technology (Shenzhen) Company Limited | Interest recommendation method, computer device, and storage medium |
| CN113643103A (en) * | 2021-08-31 | 2021-11-12 | 平安医疗健康管理股份有限公司 | Product recommendation method, device, equipment and storage medium based on user similarity |
| KR20240076664A (en) * | 2022-11-22 | 2024-05-30 | 숭실대학교산학협력단 | Product recommendation system and method based on knowledge graph and neural network collaborative filtering |
-
2024
- 2024-11-05 CN CN202411572970.9A patent/CN119477466A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106776707A (en) * | 2016-11-11 | 2017-05-31 | 百度在线网络技术（北京）有限公司 | The method and apparatus of information pushing |
| US20200320646A1 (en) * | 2018-04-26 | 2020-10-08 | Tencent Technology (Shenzhen) Company Limited | Interest recommendation method, computer device, and storage medium |
| CN113643103A (en) * | 2021-08-31 | 2021-11-12 | 平安医疗健康管理股份有限公司 | Product recommendation method, device, equipment and storage medium based on user similarity |
| KR20240076664A (en) * | 2022-11-22 | 2024-05-30 | 숭실대학교산학협력단 | Product recommendation system and method based on knowledge graph and neural network collaborative filtering |
Non-Patent Citations (2)
| Title |
|---|
| 挂枝儿: "基于图算法的协同过滤（Collaborative Filtering）- 笔记", Retrieved from the Internet <URL:https://zhuanlan.zhihu.com/p/397164766> * |
| 致GREAT: "图神经网络07-基于Graph的推荐引擎构建", Retrieved from the Internet <URL:https://developer.volcengine.com/articles/7387236336505192511> * |
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