Personalized page configuration method, device, electronic device, medium and program product

Abstract

The present disclosure provides a method, device, electronic device, medium and computer program product for personalized page configuration based on knowledge graph. The method and device can be used in the field of big data technology. The method includes: obtaining page journey information based on user consent, wherein the page journey information includes source page, target page, jump relationship and jump amount; constructing a knowledge graph according to the page journey information, wherein the nodes of the knowledge graph are constructed according to the source page and the target page, the edges of the knowledge graph are constructed according to the jump relationship, and the jump amount is the attribute of the edge; calculating the in-degree of each node in the knowledge graph; calculating the PageRank value of each node in the knowledge graph; multiplying the in-degree of the same node by the PageRank value to obtain the product value; sorting all nodes of the knowledge graph according to the product value to obtain a sorting result; extracting m nodes according to the sorting result, and taking the pages corresponding to the m nodes as important pages; optionally configuring business activity pages in the important pages.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and more specifically, to a method, device, electronic device, medium, and computer program product for personalized page configuration based on a knowledge graph. Background Art

[0002] In the methods of measuring page importance, existing technologies generally give priority to setting the starting point and end point of a single-line process through methods such as funnel models. For example, in a user transfer scenario, the process goes from "login homepage" to "transfer homepage" to "transfer application page" to "complete transfer", where the "transfer application page" includes: "fill in the transfer amount and transaction object", "submit verification" and "submit transfer application", "transfer homepage" is the starting point, and "complete transfer" is the end point. The funnel model method is used to study the conversion rate to determine the importance of the page and visualize the process.

[0003] However, in real-world bank transfer scenarios, multiple page jumps and interactions, as well as multiple transfer types, can lead to missing information when analyzing page importance using methods like funnel conversion rates that prioritize access paths. Multiple page jumps and interactions mean that different pages can lead to the same final transfer function. For example, some customers may first view their account details after logging in, then browse recent transfer details, before completing a transfer. Others, however, may log in and directly execute a transfer. Among the multiple transfer types, only domestic remittances are divided into multiple types, such as interbank quick remittances, registered account transfers, and bank remittances. Therefore, given the high diversity of customer operations, a funnel model must exhaustively explore these customer transaction paths. Exhaustive examples of these transaction paths include at least the number of multiple page jump interactions multiplied by the number of transfer types. Summary of the Invention

[0004] In view of this, the present disclosure provides a method, device, electronic device, computer-readable storage medium and computer program product for personalized page configuration based on a knowledge graph, which accurately evaluates page importance and facilitates the realization of business value.

[0005] One aspect of the present disclosure provides a personalized page configuration method based on a knowledge graph, comprising: obtaining page journey information based on user consent, wherein the page journey information includes a source page, a target page, a jump relationship, and a jump amount; constructing a knowledge graph according to the page journey information, wherein nodes of the knowledge graph are constructed according to the source page and the target page, edges of the knowledge graph are constructed according to the jump relationship, and the jump amount is an attribute of the edge; calculating the in-degree of each of the nodes in the knowledge graph; calculating the PageRank value of each of the nodes in the knowledge graph; multiplying the in-degree of the same node by the PageRank value to obtain a product value; sorting all the nodes of the knowledge graph according to the product value to obtain a sorting result; extracting m nodes according to the sorting result, and taking the pages corresponding to the m nodes as important pages, wherein m is an integer greater than or equal to 1; and optionally configuring a business activity page in the important page.

[0006] According to the personalized page configuration method based on the knowledge graph of the embodiment of the present disclosure, it is convenient to construct a knowledge graph by obtaining the user's journey information. The node's in-degree and PageRank value can be calculated through the knowledge graph. By multiplying the in-degree and PageRank value of the same node, the importance ranking of the node can be obtained. Since the in-degree is the number of inbound nodes of a node, the more inbound nodes, the greater the in-degree, which means there are more demands to jump to the node, and the more important the node may be. The in-degree is multiplied by the PageRank value so that the in-degree can be used as a weighted value of the PageRank value, making the PageRank value more accurate in evaluating the importance of the web page. After obtaining the important page, relevant business activities can be configured on the important page, thereby improving the exposure of the information and realizing business value.

[0007] In some embodiments, the page journey information further includes a user category, where the user category is an attribute of the edge.

[0008] In some embodiments, the source page and the target page both have a page name and a unique page number, and the page name and the page number are attributes of the node.

[0009] In some embodiments, the edge is a directed edge from the source page to the target page constructed according to the jump relationship, the jump amount is the number of outgoing links from the starting node to the ending node, and the PageRank value of node i is r(i). Where B(i) is the set of links to node i, j is a node in B(i), r(j) is the PageRank value of node j, N(j) is the number of links out of node j, and the starting value of r(j) is N is the total number of nodes in the knowledge graph.

[0010] In some embodiments, sorting all the nodes of the knowledge graph according to the product value to obtain a sorting result includes: sorting all the nodes of the knowledge graph in ascending order according to the product value to obtain a sorting result; extracting m nodes according to the sorting result includes: extracting the m nodes ranked after m in the sorting result.

[0011] In some embodiments, sorting all the nodes of the knowledge graph according to the product value to obtain a sorting result includes: sorting all the nodes of the knowledge graph in descending order according to the product value to obtain a sorting result; extracting m nodes according to the sorting result includes: extracting the top m m nodes in the sorting result.

[0012] In some embodiments, the method further includes: rendering the nodes corresponding to the source page with a first rendering effect; rendering the nodes corresponding to the target page with a second rendering effect; rendering the nodes corresponding to the important page with a third rendering effect; and visualizing the rendered knowledge graph.

[0013] Another aspect of the present disclosure provides a personalized page configuration device based on a knowledge graph, comprising: an acquisition module, the acquisition module being used to execute acquisition of page journey information based on user consent, wherein the page journey information includes a source page, a target page, a jump relationship, and a jump amount; a construction module, the construction module being used to execute construction of a knowledge graph according to the page journey information, wherein nodes of the knowledge graph are constructed according to the source page and the target page, edges of the knowledge graph are constructed according to the jump relationship, and the jump amount is an attribute of the edge; a first calculation module, the first calculation module being used to execute calculation of the in-degree of each node in the knowledge graph; a second calculation module, The second calculation module is used to calculate the PageRank value of each node in the knowledge graph; the third calculation module is used to multiply the in-degree of the same node by the PageRank value to obtain the product value; the sorting module is used to sort all the nodes of the knowledge graph according to the product value to obtain the sorting result; the extraction module is used to extract m nodes according to the sorting result, and use the pages corresponding to the m nodes as important pages, where m is an integer greater than or equal to 1; and the configuration module is used to optionally configure business activity pages in the important pages.

[0014] Another aspect of the present disclosure provides an electronic device, comprising one or more processors and one or more memories, wherein the memories are used to store executable instructions, and when the executable instructions are executed by the processors, implement the above method.

[0015] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described above when executed.

[0016] Another aspect of the present disclosure provides a computer program product, comprising a computer program, wherein the computer program comprises computer-executable instructions, and the instructions are used to implement the method described above when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above 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:

[0018] Figure 1 Schematically illustrates an exemplary system architecture to which the method and apparatus according to an embodiment of the present disclosure may be applied;

[0019] Figure 2 The flowchart of the method for configuring personalized pages based on the knowledge graph according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 3 A flowchart schematically illustrates a method of sorting all nodes of a knowledge graph according to product values to obtain a sorting result according to an embodiment of the present disclosure;

[0021] Figure 4 Schematically shows a flow chart of extracting m nodes according to a sorting result according to an embodiment of the present disclosure;

[0022] Figure 5 A flowchart schematically illustrates a method of sorting all nodes of a knowledge graph according to product values to obtain a sorting result according to an embodiment of the present disclosure;

[0023] Figure 6 Schematically shows a flow chart of extracting m nodes according to a sorting result according to an embodiment of the present disclosure;

[0024] Figure 7 The flowchart of the method for configuring personalized pages based on the knowledge graph according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 8 A block diagram of a knowledge graph-based personalized page configuration device according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 9The block diagram of an electronic device according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, 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 well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0028] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved all comply with the provisions of relevant laws and regulations, adopt necessary confidentiality measures, and do not violate public order and good morals. In the technical solutions disclosed herein, the acquisition, collection, storage, use, processing, transmission, provision, disclosure, and application of data all comply with the provisions of relevant laws and regulations, adopt necessary confidentiality measures, and do not violate public order and good morals.

[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 presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] When using expressions such as "at least one of A, B, or C," they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly specifying the number of the indicated technical features. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more of the aforementioned features.

[0031] In the methods of measuring page importance, existing technologies generally give priority to setting the starting point and end point of a single-line process through methods such as funnel models. For example, in a user transfer scenario, the process goes from "login homepage" to "transfer homepage" to "transfer application page" to "complete transfer", where the "transfer application page" includes: "fill in the transfer amount and transaction object", "submit verification" and "submit transfer application", "transfer homepage" is the starting point, and "complete transfer" is the end point. The funnel model method is used to study the conversion rate to determine the importance of the page and visualize the process.

[0032] However, in real-world bank transfer scenarios, multiple page jumps and interactions, as well as multiple transfer types, can lead to missing information when analyzing page importance using methods like funnel conversion rates that prioritize access paths. Multiple page jumps and interactions mean that different pages can lead to the same final transfer function. For example, some customers may first view their account details after logging in, then browse recent transfer details, before completing a transfer. Others, however, may log in and directly execute a transfer. Among the multiple transfer types, only domestic remittances are divided into multiple types, such as interbank quick remittances, registered account transfers, and bank remittances. Therefore, given the high diversity of customer operations, a funnel model must exhaustively explore these customer transaction paths. Exhaustive examples of these transaction paths include at least the number of multiple page jump interactions multiplied by the number of transfer types.

[0033] The embodiments of the present disclosure provide a method, device, electronic device, computer-readable storage medium and computer program product for configuring personalized pages based on a knowledge graph. The method for configuring personalized pages based on a knowledge graph includes: obtaining page journey information based on user consent, wherein the page journey information includes a source page, a target page, a jump relationship and a jump amount; constructing a knowledge graph based on the page journey information, wherein nodes of the knowledge graph are constructed based on the source page and the target page, edges of the knowledge graph are constructed based on the jump relationship, and the jump amount is an attribute of the edge; calculating the in-degree of each node in the knowledge graph; calculating the PageRank value of each node in the knowledge graph; multiplying the in-degree of the same node by the PageRank value to obtain a product value; sorting all nodes of the knowledge graph according to the product value to obtain a sorting result; extracting m nodes according to the sorting result, and taking the pages corresponding to the m nodes as important pages, wherein m is an integer greater than or equal to 1; and optionally configuring a business activity page in the important page.

[0034] It should be noted that the knowledge graph-based personalized page configuration method, device, electronic device, computer-readable storage medium and computer program product disclosed in the present invention can be used in the field of big data, and can also be used in any field other than the field of big data, such as the financial field. The field of the present invention is not limited here.

[0035] Figure 1 The following schematically illustrates an exemplary system architecture 100 to which a method, apparatus, electronic device, computer-readable storage medium, and computer program product for configuring personalized pages based on a knowledge graph can be applied according to an embodiment of the present disclosure. Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0036] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0037] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0038] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0039] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0040] It should be noted that the personalized page configuration method based on the knowledge graph provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the personalized page configuration device based on the knowledge graph provided in the embodiment of the present disclosure can generally be set in the server 105. The personalized page configuration method based on the knowledge graph provided in the embodiment 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 terminal devices 101, 102, 103 and / or the server 105. Accordingly, the personalized page configuration device based on the knowledge graph provided in the embodiment 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 terminal devices 101, 102, 103 and / or the server 105.

[0041] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0042] The following will be based on Figure 1 The scene described by Figures 2 to 7The personalized page configuration method based on the knowledge graph in the embodiment of the present disclosure is described in detail.

[0043] Figure 2 A flowchart of a method for configuring personalized pages based on a knowledge graph according to an embodiment of the present disclosure is schematically shown.

[0044] like Figure 2 As shown, the knowledge graph-based personalized page configuration method of this embodiment includes operations S210 to S280.

[0045] In operation S210, based on the user's consent, page journey information is obtained. The page journey information includes the source page, the target page, the jump relationship, and the jump amount. Specifically, the source page can be understood as the current page, the target page can be understood as the page to be jumped to based on the operation, the jump relationship can be understood as the jump from the source page to the target page, and the jump amount can be understood as the number of jumps with the same jump relationship.

[0046] In operation S220, a knowledge graph is constructed based on the page journey information, wherein nodes of the knowledge graph are constructed based on the source page and the target page, edges of the knowledge graph are constructed based on the jump relationship, and the jump amount is an attribute of the edge.

[0047] In operation S230, the in-degree of each node in the knowledge graph is calculated. It should be noted that the knowledge graph may include multiple nodes, and each node may be constructed by a source page as well as a target page, that is, each node has both outbound and inbound nodes. Here, the number of inbound nodes is defined as the in-degree.

[0048] In operation S240, the PageRank value of each node in the knowledge graph is calculated. It should be noted that PageRank is a technology that is calculated based on the hyperlinks between web pages. The PageRank value can be used to reflect the relevance and importance of web pages.

[0049] In operation S250, the in-degree of the same node is multiplied by the PageRank value to obtain the product value. It can be understood that since the in-degree is the number of inbound nodes of a node, the more inbound nodes, the larger the in-degree, which means that there are more demands to jump to the node, and the more important the node may be. The in-degree is multiplied by the PageRank value so that the in-degree can be used as a weighted value of the PageRank value, making the PageRank value more accurate in evaluating the importance of the web page.

[0050] As a possible implementation method, the edge can be a directed edge from the source page to the target page constructed based on the jump relationship. The jump amount is the number of edges from the starting node to the end node. It can be understood that directed edges can facilitate the counting of the number of inbound and outbound links of the node, and at the same time make it easier for staff to clearly obtain the jump relationship. The PageRank value of node i is r(i), Where B(i) is the set of links to node i, j is a node in B(i), r(j) is the PageRank value of node j, N(j) is the number of links out of node j, and the starting value of r(j) is N is the total number of nodes in the knowledge graph. It can be convenient to calculate the PageRank value of each node in the knowledge graph, so as to multiply the in-degree of the same node by the PageRank value to obtain the product value.

[0051] In operation S260, all nodes of the knowledge graph are sorted according to the product value to obtain a sorting result.

[0052] In operation S270 , m nodes are extracted according to the sorting result, and pages corresponding to the m nodes are taken as important pages, where m is an integer greater than or equal to 1.

[0053] As a possible way to achieve this, Figure 3 As shown, operation S260 sorts all nodes of the knowledge graph according to the product value, and obtaining the sorting result includes operation S261.

[0054] In operation S261, all nodes of the knowledge graph are sorted in ascending order according to the product value to obtain a sorting result. In this way, it is easy to sort all nodes of the knowledge graph according to the product value to obtain a sorting result.

[0055] like Figure 4 As shown, operation S270 of extracting m nodes according to the sorting result includes operation S271.

[0056] In operation S271 , m nodes ranked last in the sorting result are extracted, thereby facilitating the extraction of m nodes according to the sorting result.

[0057] As another possible implementation, Figure 5 As shown, operation S260 sorts all nodes of the knowledge graph according to the product value, and obtaining the sorting result includes operation S262.

[0058] In operation S262, all nodes of the knowledge graph are sorted in descending order according to the product value to obtain a sorting result. In this way, it is also easy to sort all nodes of the knowledge graph according to the product value to obtain a sorting result.

[0059] like Figure 6 As shown, operation S270 of extracting m nodes according to the sorting result includes operation S272.

[0060] In operation S272 , the top m nodes in the sorting result are extracted, thereby also facilitating the extraction of m nodes according to the sorting result.

[0061] In operation S280, a business activity page is selectively configured among the important pages.

[0062] For example, in the payroll client, you first enter the "login page", and after logging in, you will see the "home page". The home page has transfer and remittance functions, financial management functions, my account functions, convenient service functions, etc. Based on the user's click request, you can jump from the "home page" to the "transfer and remittance page", from the "home page" you can jump to the "financial management page", from the "home page" you can jump to the "my account page", from the "home page" you can jump to the "convenience service page", etc. From the "transfer and remittance page", you can also jump to "fill in the transfer amount and transaction object", from "fill in the transfer amount and transaction object" you can also jump to "submit verification", from "submit verification" you can also jump to "submit transfer application", etc.

[0063] Based on the user's consent, the above-mentioned journey information can be obtained, among which the "login page" is the source page. In the jump relationship from the "login page" to the "transfer and remittance page", "financial management page", "my account page" or "convenience service page", the "transfer and remittance page", "financial management page", "my account page" and "convenience service page" can be used as the target page; in the jump relationship from the "transfer and remittance page" to "fill in the transfer amount and transaction object", the "transfer and remittance page" is the source page, and the "fill in the transfer amount and transaction object" is the target page. The same applies to "submit verification" and "submit transfer application", which will not be repeated here.

[0064] Among them, the "login page," "transfer and remittance page," "financial management page," "my account page," "convenience service page," "fill in the transfer amount and transaction object," "submit verification," and "submit transfer application" can be used as nodes to construct a knowledge graph, and jump relationships can be used as edges to connect nodes. After constructing the knowledge graph, the in-degree of each node can be calculated, and the PageRank value of each node can also be calculated. The product value can be obtained by multiplying the in-degree and PageRank value of the same node. Based on the product value, it can be sorted in ascending or descending order, which is not specifically limited here.

[0065] If the product values are sorted in ascending order, the pages corresponding to the nodes with lower rankings have higher importance, so the last m nodes of the sorting result can be selected, and the pages corresponding to the last m nodes can be regarded as important pages; if the product values are sorted in descending order, the pages corresponding to the nodes with higher rankings have higher importance, so the first m nodes of the sorting result can be selected, and the pages corresponding to the first m nodes can be regarded as important pages.

[0066] Here, m can be 1, 2, 3, 4, and so on. It can be any integer greater than or equal to 1. Assume that the knowledge graph nodes are sorted in descending order, and the pages corresponding to the top three nodes are selected as important pages. Therefore, business marketing activities can be configured on these top three important pages to realize business value. For example, a business marketing activity could include content such as fund management. If a user is interested in the current activity, the user's transfer operation will be interrupted, ensuring that the user's assets remain within the bank through payroll processing.

[0067] According to the personalized page configuration method based on the knowledge graph of the embodiment of the present disclosure, it is convenient to construct a knowledge graph by obtaining the user's journey information. The node's in-degree and PageRank value can be calculated through the knowledge graph. By multiplying the in-degree and PageRank value of the same node, the importance ranking of the node can be obtained. Since the in-degree is the number of inbound nodes of a node, the more inbound nodes, the greater the in-degree, which means there are more demands to jump to the node, and the more important the node may be. The in-degree is multiplied by the PageRank value so that the in-degree can be used as a weighted value of the PageRank value, making the PageRank value more accurate in evaluating the importance of the web page. After obtaining the important page, relevant business activities can be configured on the important page, thereby improving the exposure of the information and realizing business value.

[0068] In some embodiments of the present disclosure, page journey information may also include user classification, with user classification being an edge attribute. User classification can be understood as a user category, and classification rules can be customized as needed. For example, users can be categorized as one-star, two-star, three-star, and four-star users, ranked by rating: one-star < two-star < three-star < four-star. Another example is that users can be segmented based on their payroll amounts, assuming they can be divided into "users with a salary of less than 3,000," "users with a salary of 3,000-5,000," "users with a salary of 5,000-8,000," "users with a salary of 8,000-10,000," "users with a salary of 10,000-15,000," and "users with a salary of more than 15,000." Using user classification as an edge attribute allows staff to configure relevant business activities based on specific user groups, making business activity configuration more precise.

[0069] According to some embodiments of the present disclosure, both the source page and the target page have a page name and a unique page number, and the page name and the page number are attributes of the node. It is understandable that the unique page number can ensure the uniqueness of the source page and the target page, thereby facilitating the construction of a knowledge graph, and the constructed knowledge graph has better accuracy. Using the page number as an attribute of the node avoids duplication of information in the knowledge graph, resulting in calculation errors of the node in-degree and PageRank value. As a result, the important pages obtained by the present disclosure are more accurate. The page name can facilitate the identification of the source page and the target page, and is also convenient for the construction of a knowledge graph. Using the page name as an attribute of the node can facilitate the identification and use of the node by the staff.

[0070] like Figure 7 As shown, according to some embodiments of the present disclosure, the personalized page configuration method based on the knowledge graph also includes operations S310 to S340.

[0071] In operation S310 , a node corresponding to the source page is rendered using a first rendering effect, wherein the first rendering effect may be bolding, color, and / or transparency, etc., which is not specifically limited here.

[0072] In operation S320 , a node corresponding to the target page is rendered using a second rendering effect, wherein the second rendering effect may be bolding, color, and / or transparency, etc., which is not specifically limited here.

[0073] In operation S330 , the node corresponding to the important page is rendered using a third rendering effect, wherein the third rendering effect may be bolding, color and / or transparency, etc., which is not specifically limited here.

[0074] Among them, the first rendering effect, the second rendering effect, and the third rendering effect can all be the same; the first rendering effect, the second rendering effect, and the third rendering effect can also all be different; the first rendering effect, the second rendering effect, and the third rendering effect can also have two of them be the same, and the other one is different from the other two. Here, the first rendering effect, the second rendering effect, and the third rendering effect can be set as needed.

[0075] In operation S340 , the rendered knowledge graph is visualized, so that the staff can easily know the nodes corresponding to the source page, the target page, and the important pages, thereby facilitating the staff's reference to better configure related business activities.

[0076] The following describes in detail a method for configuring a personalized page based on a knowledge graph according to an embodiment of the present disclosure. It is worth noting that the following description is merely an illustrative example and is not intended to limit the present disclosure.

[0077] The personalized page configuration method based on the knowledge graph may include big data mining of payroll user client journey information and knowledge graph construction.

[0078] The big data mining of payroll client journey information includes:

[0079] (1) Obtaining the target transaction page of the customer group: Through data mining, the code visits of the target transaction page of the customer group can be obtained. For example, the target page numbers of the payroll payment customer group are basically the successful page codes of inter-bank quick remittance, registered account transfer, bank remittance, etc.

[0080] (2) Customer group segmentation: Based on user-specific attributes, multiple sub-customer groups can be further divided for subsequent query of sub-customer group travel behavior. For example, for users in the payroll customer group, since some users receive multiple payroll payments in a month, it is necessary to prioritize querying the aggregated payroll amount for that month. All users are binned based on the payroll amount, and the payroll users are subdivided into multiple customer groups. For example, if the payroll amount is between 2,000 and 6,000, it is group 0, and the payroll amount is between 6,000 and 10,000, it is group 1, and so on. The payroll users can be divided into 8 sub-customer groups.

[0081] (3) Obtaining the journey information of the corresponding sub-customer group users on the client: Because each page on the client has a unique page code, the journey information of the corresponding sub-customer group users can be summarized. For example, suppose there are pages "A", "B" and "C", which are coded as "0000", "0001" and "0002" respectively, and there is a jump relationship between the pages. The page jump relationship from "A" to "B" is represented by "0000-0001", and the number of users corresponding to the sub-customer group jumping to different pages can be counted. For example: In the current month, there are X users in sub-customer group 0 who jump from page "A" to "B", and X can be any integer.

[0082] At this point, the data required to build the knowledge graph has been prepared, with a total of five dimensions: "A" is called the source page code, "B" is called the target page code, "0000-0001" is called the page jump, and the sub-customer group number 0 is called the salary classification label and user visits.

[0083] The knowledge graph construction includes:

[0084] (1) Node: Obtain all page codes and corresponding Chinese descriptions from the page code attribute table (each page code is unique in the page code attribute table), use the page code as a node in the knowledge graph, and the corresponding Chinese description as the node attribute information.

[0085] (2) Edge: The page jump relationship between the source page code and the target page code. For example, "0000-0001" indicates a directed edge connecting the source page code "0000" to the target page code "0001". The salary category label and user visit volume are set as the edge attribute information.

[0086] (3) Calculate the importance of the knowledge graph page for payroll customers:

[0087] Calculate the in-degree of each node in the knowledge graph.

[0088] Calculate the PageRank value of each node in the knowledge graph.

[0089] Multiply the in-degree and PageRank value of the same node to get the product value.

[0090] Among them, the PageRank value of node i is r(i), Where B(i) is the set of links to node i, j is a node in B(i), r(j) is the PageRank value of node j, N(j) is the number of links out of node j, and the starting value of r(j) is N is the total number of nodes in the knowledge graph.

[0091] The calculation process is mainly divided into the following two stages:

[0092] 1) Initial stage: In the constructed knowledge graph, each linked node has the same PageRank value. The PageRank value of each node can be initialized to 1 / N, where N is the total number of nodes in the knowledge graph. Each node is assumed to have the same weight, that is, each node can be randomly accessed at the beginning.

[0093] 2) Update PageRank: During a round of calculations to update a node's PageRank, each node evenly distributes its current PageRank value to its outbound links, giving each link a corresponding weight. Each node then sums the weights of all incoming links pointing to it to obtain a new PageRank value. Once each node has obtained its updated PageRank value, a round of PageRank calculations is complete.

[0094] The in-degree values of the same nodes are multiplied by the PageRank value and sorted in descending order. The data with the highest value, "Safe Exit Page", is filtered. The remaining TOP5 (important pages in the client journey of payroll payment customers) are "My Account List", "Transfer and Remittance - Remittance Homepage", "Transfer and Remittance Details Page", "Wealth Management - Wealth Management List" and "Favorite Pages".

[0095] The knowledge graph is partially visualized. For example, nodes with red circles can be identified as target page nodes for customers (i.e., the aforementioned inter-bank quick remittance, registered account transfer, ICBC remittance, etc.); nodes with white circles can be identified as important customer pages (i.e., "My Account List", "Transfer and Remittance - Remittance Homepage", "Transfer and Remittance Details Page", "Wealth Management - Wealth Management List", "Favorite Page").

[0096] (4) Business promotion: Based on the importance of the pages in the current customer journey, business marketing activities can be configured to realize business value. For example, for the payroll customer group, the target transaction page function is transfer (such as the success page of inter-bank quick remittance, registered account transfer, ICBC remittance, etc.). Therefore, the top 5 important pages are "My Account List", "Transfer and Remittance - Remittance Home", "Transfer and Remittance Details Page", "Financial Management - Financial Management List", and "Favorite Page". That is, most users will mainly pass through the above 5 pages during the transfer journey (for example, the user first views the details of the last transfer and remittance, and then executes the current transfer, etc.). Therefore, when configuring business marketing activities (such as fund financial management) on the above 5 pages, the user may be interested in the current activity, which will interrupt the user's transfer operation, so that the assets of the payroll user can be retained within the bank.

[0097] Leveraging big data mining and knowledge graph algorithms, we calculate the importance of pages within a specific customer journey. We deploy relevant business activities on these pages to increase information exposure and business value for specific customer groups.

[0098] Based on the above-mentioned personalized page configuration method based on knowledge graph, the present disclosure also provides a personalized page configuration device 10 based on knowledge graph. Figure 8 The personalized page configuration device 10 based on the knowledge graph is described in detail.

[0099] Figure 8 The structural block diagram of the knowledge graph-based personalized page configuration device 10 according to an embodiment of the present disclosure is schematically shown.

[0100] The knowledge graph-based personalized page configuration device 10 includes an acquisition module 1, a construction module 2, a first calculation module 3, a second calculation module 4, a third calculation module 5, a sorting module 6, an extraction module 7 and a configuration module 8.

[0101] The acquisition module 1 is used to perform operation S210: acquiring page journey information based on user consent, wherein the page journey information includes a source page, a target page, a jump relationship, and a jump amount.

[0102] Construction module 2, construction module 2 is used to perform operation S220: construct a knowledge graph based on page journey information, wherein the nodes of the knowledge graph are constructed based on the source page and the target page, the edges of the knowledge graph are constructed based on the jump relationship, and the jump amount is the attribute of the edge.

[0103] The first computing module 3 is used to perform operation S230: calculating the in-degree of each node in the knowledge graph.

[0104] The second calculation module 4 is used to perform operation S240: calculate the PageRank value of each node in the knowledge graph.

[0105] The third calculation module 5 is used to perform operation S250: multiply the in-degree of the same node by the PageRank value to obtain a product value.

[0106] Sorting module 6, sorting module 6 is used to perform operation S260: sort all nodes of the knowledge graph according to the product value to obtain a sorting result.

[0107] Extraction module 7, the extraction module 7 is used to perform operation S270: extract m nodes according to the sorting result, and use pages corresponding to the m nodes as important pages, where m is an integer greater than or equal to 1.

[0108] Configuration module 8, configuration module 8 is used to perform operation S280: optionally configure a business activity page in the important pages.

[0109] Since the above-mentioned knowledge graph-based personalized page configuration device 10 is set based on the knowledge graph-based personalized page configuration method, the beneficial effects of the above-mentioned knowledge graph-based personalized page configuration device 10 are the same as those of the knowledge graph-based personalized page configuration method, and will not be repeated here.

[0110] In addition, according to an embodiment of the present disclosure, any multiple modules among the acquisition module 1, the construction module 2, the first calculation module 3, the second calculation module 4, the third calculation module 5, the sorting module 6, the extraction module 7, and the configuration module 8 can be combined into a single module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module.

[0111] According to an embodiment of the present disclosure, at least one of the acquisition module 1, the construction module 2, the first calculation module 3, the second calculation module 4, the third calculation module 5, the sorting module 6, the extraction module 7 and the configuration module 8 can 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 can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware and firmware, or in an appropriate combination of any of them.

[0112] Alternatively, at least one of the acquisition module 1, the construction module 2, the first calculation module 3, the second calculation module 4, the third calculation module 5, the sorting module 6, the extraction module 7 and the configuration module 8 can be at least partially implemented as a computer program module, which can perform the corresponding function when it is run.

[0113] Figure 9 A block diagram of an electronic device suitable for implementing a personalized page configuration method based on a knowledge graph according to an embodiment of the present disclosure is schematically shown.

[0114] like Figure 9 As 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 into a random access memory (RAM) 903. The processor 901 may, for example, include 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.

[0115] Various programs and data required for the operation of the electronic device 900 are stored in the RAM 903. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. The processor 901 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and / or the RAM 903. It should be noted that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0116] 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 I / O interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage portion 908 including a hard disk; and a communication portion 909 including a network interface card such as a LAN card or a modem. 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 in the drive 910 as needed, so that a computer program read therefrom can be installed into the storage portion 908 as needed.

[0117] 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 and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0118] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it 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, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, according to an embodiment of the present disclosure, the 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.

[0119] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing 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 cause the computer system to implement the method of the embodiments of the present disclosure.

[0120] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 901 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0121] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 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, or any suitable combination thereof.

[0122] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from a removable medium 911. When the computer program is executed by the processor 901, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0123] 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 computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving 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 via the Internet).

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation 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 flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] Those skilled in the art will appreciate that various combinations and / or combinations of features described in the various embodiments and / or claims of this disclosure may be made, even if such combinations or combinations are not explicitly described in this disclosure. In particular, various combinations and / or combinations of features described in the various embodiments and / or claims of this disclosure may be made, without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0126] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A personalized page configuration method based on knowledge graph, characterized in that: include: Obtaining page journey information based on user consent, wherein the page journey information includes source page, target page, jump relationship, and jump amount; Constructing a knowledge graph based on the page journey information, wherein nodes of the knowledge graph are constructed based on the source page and the target page, edges of the knowledge graph are constructed based on the jump relationship, and the jump amount is an attribute of the edge; Calculating the in-degree of each node in the knowledge graph, wherein each node in the knowledge graph has an outgoing node and an incoming node, and the in-degree is the number of the incoming nodes; Calculating the PageRank value of each node in the knowledge graph; Multiplying the in-degree of the same node by the PageRank value to obtain a product value; Sort all the nodes of the knowledge graph according to the product value to obtain a sorting result; Extracting m nodes according to the sorting result, and taking pages corresponding to the m nodes as important pages, where m is an integer greater than or equal to 1; and A business activity page may be optionally configured among the important pages.

2. The method according to claim 1, characterized in that The page journey information further includes a user category, where the user category is an attribute of the edge.

3. The method according to claim 1, characterized in that The source page and the target page both have a page name and a unique page number, and the page name and the page number are attributes of the node.

4. The method according to claim 1, wherein The edge is a directed edge from the source page to the target page constructed according to the jump relationship, and the jump amount is the number of links from the starting node to the end node. The PageRank value of , ,in, For nodes Linked collection, for A node in For nodes The PageRank value of For nodes The number of outbound links, The starting value is , N is the total number of nodes in the knowledge graph.

5. The method according to claim 1, wherein Sorting all the nodes of the knowledge graph according to the product value to obtain a sorting result includes: sorting all the nodes of the knowledge graph in ascending order according to the product value to obtain a sorting result; The extracting m nodes according to the sorting result includes: extracting the m nodes ranked last in the sorting result.

6. The method according to claim 1, characterized in that Sorting all the nodes of the knowledge graph according to the product value to obtain a sorting result includes: sorting all the nodes of the knowledge graph in descending order according to the product value to obtain a sorting result; The extracting m nodes according to the sorting result includes: extracting the top m nodes in the sorting result.

7. The method according to claim 1, characterized in that Also includes: Rendering the node corresponding to the source page using the first rendering effect; Rendering the node corresponding to the target page using a second rendering effect; Rendering the node corresponding to the important page using a third rendering effect; as well as The knowledge graph after visual rendering.

8. A personalized page configuration device based on knowledge graph, characterized in that: include: an acquisition module, the acquisition module being configured to acquire page journey information based on user consent, wherein the page journey information includes a source page, a target page, a jump relationship, and a jump amount; A construction module, the construction module is used to execute construction of a knowledge graph based on the page journey information, wherein nodes of the knowledge graph are constructed based on the source page and the target page, edges of the knowledge graph are constructed based on the jump relationship, and the jump amount is an attribute of the edge; A first calculation module, configured to calculate the in-degree of each node in the knowledge graph, wherein each node in the knowledge graph has an outbound node and an inbound node, and the in-degree is the number of the inbound nodes; A second calculation module, the second calculation module is used to calculate the PageRank value of each node in the knowledge graph; A third calculation module, configured to multiply the in-degree of the same node by the PageRank value to obtain a product value; A sorting module, configured to sort all the nodes of the knowledge graph according to the product value to obtain a sorting result; an extraction module, the extraction module being configured to extract m nodes according to the sorting result, and use pages corresponding to the m nodes as important pages, where m is an integer greater than or equal to 1; and A configuration module is used to selectively configure a business activity page in the important page.

9. An electronic device, characterized in that: include: one or more processors; One or more memories, configured to store executable instructions, wherein when the executable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores executable instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises a computer program comprising one or more executable instructions, wherein the executable instructions are executed by a processor to implement the method according to any one of claims 1 to 7.

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