Data table sorting display method and device, equipment and storage medium

Abstract

The application provides a data table sorting display method and device, equipment and a storage medium. A plurality of data tables, user attributes, user operation data and marking results of different data table importance degrees of a plurality of users are obtained. A preset first number of business hot words matched with the user attributes are obtained from a preset database according to the user attributes of the users. The importance degrees of the data tables in each user group are obtained by accumulating the marking results of each user group, and the users are displayed with the sorted data tables corresponding to the user groups to which the users belong, so that the actual application of each user can be better approached, and more accurate data table sorting can be provided for the users.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to a method, apparatus, device and storage medium for sorting and displaying data tables. Background Technology

[0002] As enterprises become increasingly digitalized, they generate a wide variety of business data. Therefore, they need to use Business Intelligence (BI) analytics tools to process and analyze this data, improve business efficiency, and ultimately identify areas for optimization and new business directions. As a company's business grows and a particular business expands, the amount of data generated increases dramatically. For users who need to view or analyze this data, the ability to efficiently and quickly locate frequently used data tables becomes essential.

[0003] Existing data table sorting methods typically use preset information and corresponding popularity calculation rules to calculate the results and then sort the data tables. For example, data tables are sorted by scoring the importance of the data, thus prioritizing the display of the most valuable data tables. This approach helps users quickly find the most valuable data tables from a large number of data tables.

[0004] However, in practice, when enterprises use BI analytics tools to process data, they may have multiple users under a single account. As enterprises become increasingly comprehensive, involving more and more business segments, the focus of each user becomes more dispersed. Furthermore, the importance ratings of data by one user are difficult to apply to other users. For example, data that is important to users in the catering business segment may not be important to users in the education business segment. Therefore, existing data table sorting methods are only suitable for macro-level sorting within a single business area and are difficult to apply to multi-user, multi-domain data analysis applications. This is especially true for new users with sparse behavioral parameters, as the data table content is difficult to align with their actual application domain. Summary of the Invention

[0005] This application provides a data table sorting and display method, apparatus, device, and storage medium to solve the problems that existing technologies are not applicable to multi-user, multi-domain data analysis, are difficult to closely match users' actual application fields, and have low accuracy in displaying data table content.

[0006] Firstly, this application provides a method for sorting and displaying a data table, including:

[0007] Obtain multiple data tables, as well as user attributes, user operation data, and the labeling results of each user on the importance of different data tables, wherein the importance includes several importance levels;

[0008] Based on the user's user attributes, a preset first number of business hot words that match the user attributes are obtained from a preset database. The popularity data of the business hot words and the user operation data are summarized to generate user behavior data. The database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and the user attributes.

[0009] Clustering algorithms are used to classify user behavior data and tagging results of multiple users to obtain several user groups, each of which includes at least one user.

[0010] The tagging results of each user group are summed up to obtain the ranking of the importance of each data table in each user group, and the user is then shown the ranked data table corresponding to the user group to which they belong.

[0011] Optionally, in the method described above, the user operation data includes the frequency of user use of the data table within a preset time range and the number of days between the user's last use of the data table. The step of summarizing the popularity data of the business keywords and the user operation data to generate user behavior data includes:

[0012] Calculate the reference value of the data table for each user based on the popularity data of each of the aforementioned business hot words;

[0013] The frequency of each user's use of each data table, the number of days since the last use of the data table, and the popularity reference value of the data table are used as a set of user behavior data.

[0014] Optionally, in the method described above, calculating the popularity reference value of the data table for each user based on the popularity data of each of the business hot keywords includes:

[0015] Use the business hot keywords corresponding to user attributes as target business hot keywords;

[0016] For each data table, retrieve the target business hot keywords from the data table, and sum up the popularity data of each target business hot keyword to serve as the popularity reference value of the data table for that user.

[0017] Optionally, in the method described above, the use of clustering algorithms to classify the user behavior data of multiple users and the tagging results of each user to obtain several user groups, including:

[0018] Construct a four-element feature for each user based on their user behavior data and user tagging results;

[0019] The four-element features of multiple users are input into a clustering algorithm to obtain clustering results. Based on the clustering results, each user is grouped to generate several user groups.

[0020] Optionally, in the method described above, the step of inputting the four-element features of multiple users into the clustering algorithm to obtain the clustering result includes:

[0021] The four-element features of multiple users are used as samples and input into the K-Means clustering algorithm, with a third preset number of cluster centers.

[0022] When each cluster center meets the preset clustering conditions, the samples are converged to obtain the sample set corresponding to each cluster center, and the sample set of each cluster center is used as the clustering result.

[0023] Optionally, in the method described above, the step of summing the labeling results of each user group to obtain the ranking of the importance of each data table in each user group includes:

[0024] Based on the importance level, different weight values ​​are assigned to different degrees of importance, and the labeling results of each user's importance of the data table are converted into the first evaluation value;

[0025] The first evaluation values ​​of each user in the user group for the same data table are summed to obtain the second evaluation value of the data table in the user group.

[0026] Based on the second evaluation value, the data tables are arranged from high to low to generate a ranking of the importance of each data table in the user group.

[0027] Optionally, the method described above further includes:

[0028] Obtain the user attributes of new users, and retrieve a preset second number of business hot words that match the user attributes of new users from the preset database as reference behavior data for new users;

[0029] The new user reference behavior data is classified using a pre-trained classifier to obtain the target user group corresponding to the new user. The classifier is used to match the user group to which the user belongs based on the user behavior data.

[0030] Display the sorted data table corresponding to the target user group.

[0031] Secondly, this application provides a data table sorting and display device, comprising:

[0032] The data acquisition module is used to acquire multiple data tables, as well as user attributes, user operation data, and the labeling results of each user on the importance of different data tables, including several importance levels.

[0033] The user behavior data processing module is used to obtain a preset first number of business hot words that match the user attributes from a preset database based on the user attributes of the user, and to summarize the popularity data of the business hot words and the user operation data to generate user behavior data. The database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and the user attributes.

[0034] The classification module is used to classify the user behavior data of multiple users and the labeling results of each user using a clustering algorithm, and obtain several user groups, each user group including at least one user;

[0035] The display module is used to sum up the tagging results of each user group to obtain the ranking of the importance of each data table in each user group, and then display the ranked data table corresponding to the user group to which the user belongs.

[0036] Thirdly, this application provides an electronic device, including a memory, a processor, and computer-executable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-executable instructions to implement the data table sorting and display method described in any one of the first aspects above.

[0037] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data table sorting and display method described in any one of the first aspects above.

[0038] This application provides a data table sorting and display method, apparatus, device, and storage medium. It acquires multiple data tables, user attributes of multiple users, user operation data, and the labeling results of each user on the importance of different data tables, where the importance includes several importance levels. Based on the user attributes, it retrieves a preset first number of business hot words matching the user attributes from a preset database. It then summarizes the popularity data of the business hot words and the user operation data to generate user behavior data. The database stores multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and user attributes. Clustering algorithms are used to classify user behavior data and tagging results of multiple users, resulting in several user groups, each containing at least one user. The tagging results of each user group are summed to obtain a ranking of the importance of each data table within each user group. The user is then presented with the ranked data table corresponding to their user group. By clustering user behavior data and tagging results of multiple users to obtain multiple user groups, the provided data tables are more closely aligned with each user's actual application. Furthermore, by introducing hot keywords based on user attributes, the user can be quickly classified, thus providing a more accurate data table ranking. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] Figure 1 This is a schematic diagram illustrating an application scenario of the data table sorting and display method provided in this embodiment of the application.

[0041] Figure 2 A flowchart illustrating the data table sorting and display method provided in this application embodiment.

[0042] Figure 3 A schematic diagram of a data table sorting and display device provided in an embodiment of this application.

[0043] Figure 4 A schematic diagram of the structure of an electronic device for a data table-based sorting and display device provided in an embodiment of this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0046] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0047] In related technologies, data table sorting methods use preset information and corresponding popularity calculation rules to calculate the results, and then sort the data tables based on these results. However, for a company's data system or BI analysis tool, there may be multiple users from different business segments. These users from different segments have different focuses. In the overall data, there may be many data tables with high popularity / high access volume, but they are not necessarily valuable for every user. For example, even if a data table related to the catering industry is very popular, it may be unnecessary for accounts related to the tool industry.

[0048] To address the aforementioned technical problems, this application aims to propose a data table sorting and display method, apparatus, device, and storage medium. The core concept of this method is to collect multiple data tables, user attributes of multiple users, user operation data, and the labeling results of each user's importance to different data tables, and then cluster the data of multiple users to obtain multiple user types. A data table is then obtained for each user type, enabling the display of the corresponding data table sorting for each group of users. The provided data table is more closely aligned with each user's actual application, and by introducing hot keywords based on user attributes, the user can be quickly classified, thus providing a more accurate data table sorting.

[0049] To better understand the solutions of the embodiments of this application, an application scenario involved in the embodiments of this application will be introduced below.

[0050] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of the data table sorting and display method provided in the embodiments of this application, such as... Figure 1As shown, the system includes a client 100 and a server 200. The client 100 can be used to obtain user attributes, user operation data, and the labeling results of each user's importance to different data tables, and transmit these data to the server 200. Multiple users can use multiple clients 100. The client 100 can include personal computers, tablets, smart panels, etc., and is not limited to these in this embodiment.

[0051] The server 200 can receive data from each user sent by the client 100, and retrieve a preset first number of business hot words that match the user attributes from a preset database according to the user attributes. The preset database can be updated in real time by connecting to the Internet or it can be the content of a local database. The server 200 summarizes the popularity of the business hot words and user operation data to generate user behavior data, and uses a clustering algorithm to classify the user behavior data of multiple users and the tagging results of each user to obtain several user groups. For each user group, the server displays the corresponding sorted data table.

[0052] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0053] Figure 2 A flowchart illustrating the data table sorting and display method provided in this application embodiment. For example... Figure 2 As shown, the method in this embodiment includes:

[0054] S201: Obtain multiple data tables, as well as user attributes, user operation data, and the labeling results of each user on the importance of different data tables, wherein the importance includes several importance levels.

[0055] The execution entity of this application embodiment can be a server or a data table sorting and display system in the server, wherein the data table sorting and display system can be implemented by software.

[0056] The user attributes described in this embodiment are the tags set for users during registration based on their business domain. For example, user attributes can be catering, tools, education, etc. To better adapt to enterprise needs, these user attributes can also be modified and adjusted as needed.

[0057] User operation data includes the frequency of user use of the data table within a preset time range and the number of days since the user last used the data table. The user can label the importance of different data as core, high, medium, or low.

[0058] S202: Based on the user's user attributes, obtain a preset first number of business hot words that match the user attributes from a preset database, summarize the popularity data of the business hot words and the user operation data to generate user behavior data, wherein the database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and the user attributes.

[0059] It is understandable that the matching business keywords will differ for users with different user attributes. For example, when a user's attribute is "food and beverage," the matching business keywords might be "milk," "eggs," and "claypot rice"; when a user's attribute is "education," the matching business keywords might be "physics," "mathematics," and "Chinese language." The preset first value can be 3, or more. Summarizing the popularity data of these business keywords and the user operation data is mainly to address the problem of insufficient user behavior parameters; therefore, business keywords are added as a reference for expansion.

[0060] S203: Use clustering algorithms to classify the user behavior data of multiple users and the tagging results of each user to obtain several user groups, each user group including at least one user.

[0061] In this step, users with high similarity between user behavior data and tagging results are grouped together using clustering algorithms, which aligns with actual business needs.

[0062] S204: Accumulate the tagging results of each user group to obtain the ranking of the importance of each data table in each user group, and display the ranked data table corresponding to the user group to which the user belongs to.

[0063] In this step, the importance of the data tables for each user group is integrated, and the data tables corresponding to that group are displayed to the users in the user group. Therefore, the content seen by different users is adapted to their own needs.

[0064] The data table sorting and display method provided in this embodiment obtains multiple data tables, user attributes of multiple users, user operation data, and the labeling results of each user on the importance of different data tables, where the importance includes several importance levels; based on the user attributes, it retrieves a preset first number of business hot words matching the user attributes from a preset database, summarizes the popularity data of the business hot words and the user operation data, and generates user behavior data. The database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and user attributes; and utilizes a clustering algorithm. The system categorizes user behavior data and tagging results from multiple users to obtain several user groups, each containing at least one user. It then sums the tagging results of each user group to rank the importance of each data table within that group. The system displays the ranked data table corresponding to the user's user group. By clustering user behavior data and tagging results from multiple users to obtain multiple user groups, the system provides data tables that better reflect each user's actual application. Furthermore, by introducing hot keywords based on user attributes, the system can quickly categorize users, thus providing them with more accurate data table ranking.

[0065] The technical solution for the above data table sorting and display method is described in detail below.

[0066] In one possible implementation, the user operation data includes the frequency of user use of the data table within a preset time range and the number of days between the user's last use of the data table. The data table sorting and display method provided in this embodiment obtains the target business hot words corresponding to the user attributes, calculates the popularity reference value of the data table for the user based on the target business hot words, and then obtains user behavior data.

[0067] Specifically, the popularity data of the business hot words and the user operation data are aggregated to generate user behavior data, including: calculating the popularity reference value of the data table for each user based on the popularity data of each of the business hot words corresponding to each user; and taking the usage frequency of each user for each data table, the number of days since the last use of the data table, and the popularity reference value of the data table as a set of user behavior data.

[0068] In this process, the business hot words corresponding to user attributes can be used as target business hot words. For each data table, the target business hot words in the data table are obtained, and the popularity data of each target business hot word is accumulated as the popularity reference value of the data table for the user.

[0069] Understandably, based on different user attributes, different business hot words can be retrieved from the database, thus forming the target business hot words corresponding to that user attribute. These target business hot words can be a set of business hot words, existing in the form of a list or library. Each data table may include one or more of the target business hot words. Therefore, for a user with that user attribute, the popularity reference value for that data table is the cumulative value of the popularity data for each target business hot word. For example, for a user with the user attribute of "catering," their target business hot words might include "barbecued pork rice" (popularity data 300), "egg waffles" (popularity data 260), and "Guilin rice noodles" (popularity data 200). If data table A only contains the target business hot word "barbecued pork rice," then the popularity reference value for data table A for this user is 300. If data table B contains the target business hot words "egg waffles" and "Guilin rice noodles," then the popularity reference value for data table B for this user is 260 + 200 = 460.

[0070] In this embodiment, by obtaining target business hot words corresponding to user attributes, and calculating the popularity reference value of the user based on the target business hot words data table, user behavior data is obtained. By introducing business hot words and analyzing the popularity reference value of the user based on the business hot words data table, user behavior can be analyzed more comprehensively. Furthermore, when user operation data is insufficient, using business hot words as a reference can improve the accuracy of subsequent user grouping.

[0071] In one possible implementation, the data table sorting and display method provided in this embodiment constructs multi-dimensional features for each user, performs a clustering algorithm on the multi-dimensional features of multiple users, and thus groups multiple users to generate several user groups.

[0072] Specifically, the method of classifying user behavior data and tagging results of multiple users using a clustering algorithm to obtain several user groups includes: constructing a four-element feature for each user based on user behavior data and user tagging results; inputting the four-element features of multiple users into the clustering algorithm to obtain clustering results; and grouping each user according to the clustering results to generate several user groups.

[0073] The specific clustering algorithm process may include: taking the four-element features of multiple users as samples, inputting them into the K-Means clustering algorithm, and presetting a third number of cluster centers; when each cluster center meets the preset clustering conditions, the samples are converged to obtain the sample set corresponding to each cluster center, and the sample set of each cluster center is taken as the clustering result.

[0074] Understandably, in addition to K-Means clustering, other classification algorithms can be used, such as K-nearest neighbor classification algorithm, random forest classification algorithm, etc. Alternatively, one can choose not to rely on algorithms, but to classify and evaluate the RFM triples by setting certain division criteria based on the actual business situation.

[0075] In this embodiment, by constructing multi-dimensional features for each user and performing clustering algorithms on the multi-dimensional features of multiple users, multiple users are grouped into several user groups. This approach is applicable to complex data analysis scenarios involving multiple users and multiple business domains, enabling data table sorting to be more accurate and closer to the actual applications of users in each user group.

[0076] In one possible implementation, the data table sorting and display method provided in this embodiment calculates the ranking of the importance of each data table in the user group by assigning different weight values ​​to different levels of importance according to the importance level, and summing the labeling results of each user group.

[0077] The step of summing the tagging results of each user group to obtain the ranking of the importance of each data table in each user group includes: assigning different weight values ​​to different importance levels according to the importance level; converting the tagging results of each user on the importance of the data table into a first evaluation value; summing the first evaluation values ​​of each user in the user group for the same data table to obtain a second evaluation value of the data table in the user group; and arranging each data table from high to low according to the second evaluation value to generate the ranking of the importance of each data table in the user group.

[0078] Understandably, taking the labeling results as having four levels—core, high, medium, and low—as an example, we can assign weights of 5, 3, 2, and 1 to these four levels of importance, respectively. If a user group has three users, and for data table C, user a labels data table C as core, user b labels it as high, and user c labels it as high, then the second evaluation value of data table C is the sum of all the evaluation values, which is 5 + 3 + 3 = 11. Based on the labeling results of each user in the user group for different data tables, we can obtain the overall importance of the data table to the user group.

[0079] In this embodiment, by assigning different weight values ​​to different levels of importance according to the importance level, the labeling results of each user group are summed up to calculate the ranking of the importance of each data table in the user group, so that the data table ranking can be more accurate and closer to the actual application of users in each user group.

[0080] In one possible implementation, considering the possibility of newly added users or users whose business areas have been adjusted according to work needs, these users first need to be grouped, and then the data table is displayed according to their groups. Therefore, the data table sorting and display method provided in this embodiment may further include: obtaining the user attributes of new users; obtaining a preset second number of business hot words that match the user attributes of new users from a preset database as reference behavior data for new users; classifying the reference behavior data of new users using a pre-trained classifier to obtain the target user group corresponding to the new user, wherein the classifier is used to match the user group to which the user belongs based on the user behavior data; and displaying the sorted data table corresponding to the target user group.

[0081] It is understood that the pre-trained classifier can also be a classifier trained based on samples and clustering results from a clustering algorithm. Thus, the classifier can obtain the corresponding user group based on the input user behavior data.

[0082] It should be noted that a second preset number of business hot words matching the user attributes of new users are obtained from the preset database as reference behavior data for new users. The second preset number can be different from the first preset number. Since new users generally do not have user operation data or their user operation data is sparse, the second preset number can be larger than the first preset number. In other words, more business hot words can be obtained as reference behavior data for new users, thereby achieving better matching results.

[0083] In this embodiment, the business hot words corresponding to the new user are obtained based on the user attributes of the new user as reference behavior data for the new user. Then, the new user is grouped according to the reference behavior data of the new user and the sorted data table corresponding to the user group to which the new user belongs is displayed. This can effectively solve the problem of inaccurate data table display due to insufficient operation data of new users. It can display the closest and most accurate data table sorting according to the user attributes of the new user.

[0084] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0085] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0086] Figure 3 This is a schematic diagram of a data table sorting and display device provided in an embodiment of this application. Figure 3 As shown, the data table sorting and display device includes:

[0087] The data acquisition module 31 is used to acquire multiple data tables, as well as user attributes, user operation data, and the labeling results of each user on the importance of different data tables, wherein the importance includes several importance levels.

[0088] The user behavior data processing module 32 is used to obtain a preset first number of business hot words that match the user attributes from a preset database according to the user attributes of the user, and to summarize the popularity data of the business hot words and the user operation data to generate user behavior data. The database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and the user attributes.

[0089] Classification module 33 is used to classify the user behavior data of multiple users and the labeling results of each user using a clustering algorithm to obtain several user groups, each user group including at least one user;

[0090] The display module 34 is used to sum up the tagging results of each user group to obtain the ranking of the importance of each data table in each user group, and to display the ranked data table corresponding to the user group to which the user belongs.

[0091] In one possible design, the user operation data includes the frequency of user usage of the data table within a preset time range and the number of days since the user last used the data table. The user behavior data processing module 32 is specifically used for:

[0092] Calculate the reference value of the data table for each user based on the popularity data of each of the aforementioned business hot words;

[0093] The frequency of each user's use of each data table, the number of days since the last use of the data table, and the popularity reference value of the data table are used as a set of user behavior data.

[0094] In one possible design, the user behavior data processing module 32 is specifically used for:

[0095] Use the user's corresponding business hot keywords as target business hot keywords;

[0096] For each data table, retrieve the target business hot keywords from the data table, and sum up the popularity data of each target business hot keyword to serve as the popularity reference value of the data table for that user.

[0097] In one possible design, the classification module 33 is specifically used for:

[0098] Construct a four-element feature for each user based on their user behavior data and user tagging results;

[0099] The four-element features of multiple users are input into a clustering algorithm to obtain clustering results. Based on the clustering results, each user is grouped to generate several user groups.

[0100] In one possible design, the classification module 33 is specifically used for:

[0101] The four-element features of multiple users are used as samples and input into the K-Means clustering algorithm, with a third preset number of cluster centers.

[0102] When each cluster center meets the preset clustering conditions, the samples are converged to obtain the sample set corresponding to each cluster center, and the sample set of each cluster center is used as the clustering result.

[0103] In one possible design, the display module 34 is specifically used for:

[0104] Based on the importance level, different weight values ​​are assigned to different degrees of importance, and the labeling results of each user's importance of the data table are converted into the first evaluation value;

[0105] The first evaluation values ​​of each user in the user group for the same data table are summed to obtain the second evaluation value of the data table in the user group.

[0106] Based on the second evaluation value, the data tables are arranged from high to low to generate a ranking of the importance of each data table in the user group.

[0107] In one possible design, the classification module 33 and the display module 34 are also specifically used for:

[0108] Obtain the user attributes of new users, and retrieve a preset second number of business hot words that match the user attributes of new users from the preset database as reference behavior data for new users;

[0109] The new user reference behavior data is classified using a pre-trained classifier to obtain the target user group corresponding to the new user. The classifier is used to match the user group to which the user belongs based on the user behavior data.

[0110] Display the sorted data table corresponding to the target user group.

[0111] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0112] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0113] Figure 4 This is a schematic diagram of the structure of an electronic device for a data table-based sorting and display device provided in an embodiment of this application. For example... Figure 4 As shown, the electronic device of this embodiment includes: at least one processor 40 ( Figure 4 (Only one is shown) a processor, a memory 41, and a computer program stored in the memory 41 that can run on at least one processor 40, which executes the computer program to implement the steps in any of the above method embodiments.

[0114] The electronic device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of an electronic device and does not constitute a limitation on electronic devices. It may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0115] The processor 40 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0116] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0117] In some embodiments, memory 41 may be an internal storage unit of an electronic device, such as the memory of the electronic device. In other embodiments, memory 41 may be an external storage device of the electronic device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, memory 41 may include both internal and external storage units of the electronic device. Memory 41 is used to store operating systems, applications, bootloaders, data, and other programs, such as program code for computer programs. Memory 41 can also be used to temporarily store data that has been output or will be output.

[0118] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0119] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0120] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the aforementioned electronic device.

[0121] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0122] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as these combinations of technical features do not contradict each other, they should be considered within the scope of this specification.

[0123] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0124] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for sorting and displaying data tables, characterized in that, include: The system retrieves multiple data tables, user attributes, user operation data, and the labeling results of each user on the importance of different data tables. The importance includes several levels. The user attributes are labels set for users during registration based on their business domain. The user attributes include at least catering, tools, and education. The user operation data includes the frequency of user use of the data tables within a preset time range and the number of days since the user last used the data table. Based on the user's user attributes, a preset first number of business hot words that match the user attributes are obtained from a preset database; the business hot words corresponding to the user attributes are used as target business hot words; for each data table, the target business hot words in the data table are obtained, and the popularity data of each target business hot word is accumulated as a reference value for the popularity of the data table for the user; The database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and user attributes. The frequency of each user's use of each data table, the number of days between the last time the user used the data table, and the popularity reference value of the data table are used as a set of user behavior data. Clustering algorithms are used to classify user behavior data and tagging results of multiple users to obtain several user groups, each of which includes at least one user. The tagging results of each user group are summed up to obtain the ranking of the importance of each data table in each user group, and the user is shown the ranked data table corresponding to the user group to which the user belongs. The step of summing up the labeling results of each user group to obtain the ranking of the importance of each data table in each user group includes: Based on the importance level, different weight values ​​are assigned to different degrees of importance, and the labeling results of each user's importance of the data table are converted into the first evaluation value; The first evaluation values ​​of each user in the user group for the same data table are summed to obtain the second evaluation value of the data table in the user group. Based on the second evaluation value, the data tables are arranged from high to low to generate a ranking of the importance of each data table in the user group.

2. The method according to claim 1, characterized in that, The method utilizes clustering algorithms to classify user behavior data and tagging results of multiple users, resulting in several user groups, including: Construct a four-element feature for each user based on their user behavior data and user tagging results; The four-element features of multiple users are input into a clustering algorithm to obtain clustering results. Based on the clustering results, each user is grouped to generate several user groups.

3. The method according to claim 2, characterized in that, The step of inputting the four-element features of multiple users into a clustering algorithm to obtain clustering results includes: The four-element features of multiple users are used as samples and input into the K-Means clustering algorithm, with a third preset number of cluster centers. When each cluster center meets the preset clustering conditions, the samples are converged to obtain the sample set corresponding to each cluster center, and the sample set of each cluster center is used as the clustering result.

4. The method according to claim 1, characterized in that, The method also includes: Obtain the user attributes of new users, and retrieve a preset second number of business hot words that match the user attributes of new users from the preset database as reference behavior data for new users; The new user reference behavior data is classified using a pre-trained classifier to obtain the target user group corresponding to the new user. The classifier is used to match the user group to which the user belongs based on the user behavior data. Display the sorted data table corresponding to the target user group.

5. A data table sorting and display device, characterized in that, include: The data acquisition module is used to acquire multiple data tables, as well as user attributes, user operation data, and the labeling results of each user on the importance of different data tables. The importance includes several importance levels. The user attributes are labels set for users based on their business domains during user registration. The user attributes include at least catering, tools, and education. The user operation data includes the frequency of user use of the data tables within a preset time range and the number of days since the user last used the data table. The user behavior data processing module is used to retrieve a preset first number of business hot words that match the user attributes from a preset database based on the user attributes of the user, and take the business hot words corresponding to the user attributes as target business hot words. For each data table, the target business hot words in the data table are retrieved, and the popularity data of each target business hot word is accumulated as a reference value for the popularity of the data table for the user. The database is used to store multiple business hot words, the popularity data of each business hot word, and the matching relationship between each business hot word and user attributes. The frequency of each user's use of each data table, the number of days between the last time the user used the data table, and the popularity reference value of the data table are used as a set of user behavior data. The classification module is used to classify the user behavior data of multiple users and the labeling results of each user using a clustering algorithm, and obtain several user groups, each user group including at least one user; The display module is used to sum up the tagging results of each user group to obtain the ranking of the importance of each data table in each user group, and to display the ranked data table corresponding to the user group to which the user belongs. The display module is specifically used for: Based on the importance level, different weight values ​​are assigned to different degrees of importance, and the labeling results of each user's importance of the data table are converted into the first evaluation value; The first evaluation values ​​of each user in the user group for the same data table are summed to obtain the second evaluation value of the data table in the user group. Based on the second evaluation value, the data tables are arranged from high to low to generate a ranking of the importance of each data table in the user group.

6. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 4.

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