Common menu recommendation method and corresponding device

Abstract

The invention discloses a common menu recommendation method and a corresponding device, and relates to the technical field of menu design, the method comprises the following steps: obtaining behavior data of a user accessing a menu; the behavior data comprises the number of times of accessing a certain menu and the recent access time; based on the behavior data, calculating to obtain a recommendation value; generating a menu sequence based on the recommendation value; the menu sorting is a routing address sorting corresponding to the menu; based on the menu sequence, matching a menu table in a menu management module to obtain menu list information commonly used by a user; the menu table comprises a plurality of menus; each menu comprises a name, a routing address and an icon; and rendering the menu list information into a page to realize recommendation of common menus. According to the method and the device, the access frequency can be counted according to the behavior data of the user, and the related menu information is dynamically pushed, so that the user can quickly jump to the required function page, the operation efficiency of the user on the service platform is improved, and the user experience is remarkably optimized.

Description

Technical Field

[0001] This application relates to the field of menu design technology, and in particular to a commonly used menu recommendation method and corresponding apparatus. Background Technology

[0002] In the digital age, various online service platforms have flourished, providing users with a wide range of functions and services. Take the national social security card service platform as an example; it contains numerous menu options and functional modules. However, traditional system designs generally overlook the problem of excessively long user operation paths.

[0003] In existing implementations most similar to this invention, the menu design is typically fixed and cannot be dynamically adjusted based on user habits and frequency. This forces users to click through different menu levels multiple times each time they log in to find their frequently used functions, which not only reduces efficiency but also significantly impacts user experience. Summary of the Invention

[0004] The purpose of this application is to provide a method and corresponding device for recommending frequently used menus, which can statistically analyze the access frequency based on user behavior data and dynamically push relevant menu information, allowing users to quickly jump to the required function page, thereby improving the user's operating efficiency on the service platform and significantly optimizing the user experience.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] Firstly, this application provides a method for recommending commonly used menus, which includes the following steps.

[0007] Obtain user behavior data for accessing menus; the behavior data includes: the number of times a certain menu is accessed and the time of the most recent access.

[0008] Based on the behavioral data, a recommendation value is calculated.

[0009] Based on the recommended values, a menu sort is generated; the menu sort is based on the routing addresses corresponding to the menus.

[0010] Based on the menu sorting, the menu table in the menu management module is matched to obtain the list of frequently used menus by the user; the menu table includes several menus; each menu includes: name, routing address and icon.

[0011] The menu list information is rendered onto the page to recommend frequently used menu items.

[0012] Optionally, before obtaining user access menu behavior data, the commonly used menu recommendation method further includes the following steps.

[0013] Configure several menus in the menu management module.

[0014] Create a custom annotation with parameters and mark the custom annotation on the query interface of each menu; the custom annotation is a parameter with a route address.

[0015] Optionally, after obtaining user access menu behavior data and before calculating the recommendation value based on the behavior data, the commonly used menu recommendation method further includes the following steps.

[0016] The behavioral data is stored and updated using an ordered set from Redis.

[0017] Optionally, user access behavior data for the menu can be obtained, specifically including the following steps.

[0018] Retrieve custom annotations in Spring AOP aspects.

[0019] Based on the custom annotation, user behavior data for accessing the menu is obtained.

[0020] Alternatively, retrieve the logged-in user's information in the surrounding notification.

[0021] Based on the logged-in user's information, user behavior data for accessing the menu is obtained.

[0022] Optionally, the formula for calculating the recommended value is as follows.

[0023] .

[0024] time_weight=math.exp(-(current_time-last_access_time) / 3600).

[0025] Where score is the recommended value; count is the number of times a menu item is accessed; time_weight is the weight of the most recent access time; math.exp() is the exponential operation with base e; current_time is the current time; and last_access_time is the most recent access time.

[0026] Optionally, the menu sorting is generated based on the recommended values, which specifically includes the following steps.

[0027] The recommended values ​​are sorted using a dynamic sorting algorithm to generate a menu sorting.

[0028] Secondly, this application provides a commonly used menu recommendation device, which is used to implement the commonly used menu recommendation method described in any of the above claims, and the commonly used menu recommendation device includes the following modules.

[0029] The behavior data acquisition module is used to acquire user behavior data when accessing menus; the behavior data includes: the number of times a certain menu is accessed and the most recent access time.

[0030] The recommendation value calculation module is used to calculate the recommendation value based on the behavioral data.

[0031] The sorting module is used to generate a menu sort based on the recommended values; the menu sort is the sorting of the routing addresses corresponding to the menus.

[0032] The matching module is used to match the menu table in the menu management module based on the menu sorting to obtain the list of menus frequently used by the user; the menu table includes several menus; each menu includes: name, routing address and icon.

[0033] The recommendation module is used to render the menu list information onto the page to recommend frequently used menu items.

[0034] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the common menu recommendation method described in any one of the above.

[0035] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the commonly used menu recommendation method described in any one of the above descriptions.

[0036] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the commonly used menu recommendation method described in any one of the above descriptions.

[0037] Based on the specific embodiments provided in this application, the following technical effects are disclosed.

[0038] This application provides a method and corresponding apparatus for recommending commonly used menus. The method includes: acquiring user behavior data on menu access; the behavior data includes the number of times a certain menu has been accessed and the most recent access time; this helps to gain a deeper understanding of user behavior patterns and preferences, providing a strong basis for subsequent personalized recommendations. Based on the behavior data, a recommendation value is calculated; based on the recommendation value, a menu ranking is generated; the menu ranking is based on the routing addresses corresponding to the menus; this enables quantitative evaluation of menus, making the recommendation process more objective and accurate. Arranging menus according to the recommendation value prioritizes them, allowing users to quickly find their frequently used and urgently needed menus among numerous options. This ranking method aligns with users' actual usage habits, improving the efficiency of menu searching and saving time and effort. Based on the menu ranking, a menu table in the menu management module is matched to obtain a list of commonly used menus; the menu table includes several menus; each menu includes: a name, a routing address, and an icon; this ensures the accuracy and completeness of the recommendation results, not only letting users know which menus are frequently used, but also displaying the menu names, access via routing addresses, and icon-assisted identification in a user-friendly manner, enhancing the user experience. The menu list information is rendered onto the page to recommend frequently used menu items. This provides a visual display of personalized recommendations, allowing users to directly experience the thoughtful services offered by the system based on their behavioral habits. This helps improve user satisfaction and engagement, making the user experience more convenient and efficient. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a diagram illustrating the application environment of a commonly used menu recommendation method in one embodiment of this application.

[0041] Figure 2 This is a flowchart illustrating a commonly used menu recommendation method provided in an embodiment of this application.

[0042] Figure 3 This is a schematic diagram of the homepage interface of the National Social Security Card Service Platform provided in one embodiment of this application.

[0043] Figure 4 This is a schematic diagram of the functional modules of a commonly used menu recommendation device provided in an embodiment of this application.

[0044] Figure 5This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] Based on research and technical analysis, there are currently no alternative solutions that can achieve the objectives of this application with equivalent efficiency and effectiveness. During the research process, the use of a relational database to store menu access data was considered; however, relational databases perform far worse than key-value stores when handling frequent statistical and sorting operations, failing to meet the requirements of real-time performance and efficiency. This application aims to solve the problem in existing platform technologies where menus cannot be dynamically adjusted according to user habits and frequency. By effectively identifying and displaying frequently used functions, the system can statistically analyze access frequency based on user behavior data and dynamically push relevant menu information, allowing users to quickly jump to the desired function page, thereby improving user operational efficiency on the service platform and significantly optimizing the user experience.

[0047] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] The commonly used menu recommendation method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send acquired user menu access behavior data to server 104. This behavior data includes the number of times a menu item is accessed and the most recent access time. After receiving the behavior data, server 104 calculates a recommendation value based on the behavior data. Based on the recommendation value, a menu ranking is generated. This menu ranking is based on the routing addresses corresponding to the menus. Based on the menu ranking, the menu table in the menu management module is matched to obtain a list of frequently used menus. The menu table includes several menus; each menu includes a name, routing address, and icon. The menu list information is rendered onto the page to recommend frequently used menus. Server 104 can then feed back the obtained recommended menus to terminal 102. In addition, in some embodiments, the frequently used menu recommendation method can also be implemented by the server 104 or the terminal 102 separately. For example, the terminal 102 can directly recommend frequently used menus based on the user's menu access behavior data, or the server 104 can obtain the user's menu access behavior data from the data storage system and recommend frequently used menus based on the user's menu access behavior data.

[0049] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, and tablets. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0050] In one exemplary embodiment, such as Figure 2 As shown, a method for recommending frequently used menus is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the following steps are included.

[0051] S1: Obtain user access behavior data for the menu; the behavior data includes: the number of times a certain menu is accessed and the most recent access time.

[0052] S2: Based on the behavioral data, a recommended value is calculated.

[0053] S3: Based on the recommended values, generate a menu sort; the menu sort is the sorting of the routing addresses corresponding to the menus.

[0054] S4: Based on the menu sorting, match the menu table in the menu management module to obtain the list of frequently used menus by the user; the menu table includes several menus; each menu includes: name, routing address and icon.

[0055] S5: Render the menu list information onto the page to recommend frequently used menu items.

[0056] By implementing steps S1 to S5 above, users can quickly jump to the required function page, thereby improving the user's operational efficiency on the service platform and significantly optimizing the user experience.

[0057] As an optional implementation, the commonly used menu recommendation method further includes the following steps before obtaining user access menu behavior data.

[0058] Configure several menus in the menu management module.

[0059] Create a custom annotation with parameters and mark the custom annotation on the query interface of each menu; the custom annotation is a parameter with a route address.

[0060] As an optional implementation, after obtaining user access menu behavior data and before calculating the recommendation value based on the behavior data, the commonly used menu recommendation method further includes the following steps.

[0061] The behavioral data is stored and updated using an ordered set from Redis.

[0062] As an optional implementation, step S1 involves obtaining user access menu behavior data, specifically including the following steps.

[0063] Retrieve custom annotations in Spring AOP aspects.

[0064] Based on the custom annotation, user behavior data for accessing the menu is obtained.

[0065] Alternatively, retrieve the logged-in user's information in the surrounding notification.

[0066] Based on the logged-in user's information, user behavior data for accessing the menu is obtained.

[0067] As an optional implementation, the formula for calculating the recommended value is as follows.

[0068] .

[0069] time_weight=math.exp(-(current_time-last_access_time) / 3600).

[0070] Where score is the recommended value; count is the number of times a menu item is accessed; time_weight is the weight of the most recent access time; math.exp() is the exponential operation with base e; current_time is the current time; and last_access_time is the most recent access time.

[0071] As an optional implementation, in step S3, a menu sort is generated based on the recommended values, specifically including the following:

[0072] The recommended values ​​are sorted using a dynamic sorting algorithm to generate a menu sorting.

[0073] Specifically, this application discloses a common menu recommendation method based on custom annotations and Redis caching, the method including the following steps.

[0074] Step 1: In the platform's menu management module, the names, routing addresses, icons, and other information for each menu item must be configured according to strict specifications. Names should follow concise, clear, and business-identifiable naming conventions. Icons must be uniformly formatted to ensure consistent display. Routing addresses should be generated according to URL naming rules to ensure their uniqueness and stability, facilitating accurate page redirection in subsequent system operations.

[0075] Step 2: Create a custom annotation with parameters in the code. This annotation should include the `@DynamicMenu` attribute and the parameter `route`: a unique identifier for the menu path (e.g., ` / menu / management`). Then, attach this annotation to the query interface of each menu item, with the parameter being the menu route address configured in the menu management module. The purpose of this annotation is to uniquely identify the menu path, providing a key identifier for subsequent recording of user behavior data. This annotation decouples business logic from recommendation logic, supporting rapid feature expansion. The specific parameters of the custom annotation are as follows (e.g., `@DynamicMenu(route=" / menu / management")`).

[0076] Step 3: Based on Spring AOP (Aspect-Oriented Programming) technology, the `@Around` annotation in the aspect class specifies the pointcut as a method annotated with `DynamicMenu`. In the around advice, reflection is used to obtain the target method, and then the custom annotation on that method is retrieved to obtain the attribute values ​​of the annotation. Additionally, the logged-in user's information can also be obtained in the around advice, thus providing data on the user's menu access behavior.

[0077] This step specifically includes the following:

[0078] 1. Create aspect classes to handle annotations.

[0079] Declare an aspect class using the @Aspect annotation, which is responsible for intercepting all methods marked with @DynamicMenu. Define the @Around advice in the aspect class, with the pointcut expression being @annotation(com.example.DynamicMenu).

[0080] 2. Obtain the target method and annotation information.

[0081] In the surrounding notification, the intercepted method signature is obtained through the ProceedingJoinPoint object, the reflection object Method of the target method is obtained through the method signature, the @DynamicMenu annotation instance on the target method is obtained using the reflection API, and the menu route address attribute value is extracted from the annotation instance.

[0082] 3. Obtain information about the currently logged-in user.

[0083] The token information can be used to obtain the information of the currently logged-in user, from which the user ID information can be extracted.

[0084] 4. Record user behavior data.

[0085] By combining annotation attributes (menu information) and user information, a complete behavior record can be constructed. Behavior data can include: user ID, operation time, and the accessed menu route address.

[0086] Spring AOP's around advice is a type of advice in aspect-oriented programming that allows custom logic to be inserted before and after the execution of the target method. In Spring AOP, around advice is defined using the `@Around` annotation, and join point information is obtained through the `ProceedingJoinPoint` parameter. In Spring AOP, the target method refers to the original business method that is intercepted and enhanced by the aspect.

[0087] Step 4: Use a Redis Sorted Set to store and update the number of times a user accesses a menu item and its last access time. The storage structure is as follows: (e.g., ZADD user:123 menu1 100), where (score = recommended value, member = routing address). A dynamic sorting algorithm is used to sort the recommended values, generating a menu order that prioritizes frequently used and recently accessed features. Calculate the last access time weight: time_weight = math.exp(-(current_time - last_access_time) / 3600), and then calculate the final score. The decay period can be adjusted according to business needs. Here, `count` represents the number of times a menu item is accessed; `time_weight` is the weight of the most recent access time; `math.exp()` is an exponential operation with base e; `current_time` is the current time; and `last_access_time` is the most recent access time.

[0088] Step 5: After a user logs in, the system retrieves the user's menu access ranking data from the Redis cache, i.e., menu sorting. Then, based on the routing address, it matches the menu table to obtain the user's frequently used menu list information, including menu name, icon, and routing address, and renders it on the page to recommend frequently used menus. By clicking on the menu in the frequently used function modules on the homepage, the system can accurately match and quickly jump to the corresponding function page based on the obtained menu routing address.

[0089] like Figure 3 As shown, taking the National Social Security Card Service Platform as an example, in practical application: the system dynamically adjusts the display order and content of frequently used function modules based on the user's real-time menu access records. For example, if a user frequently uses the menu management function, the system will display this function at the forefront of frequently used function modules for easy access. Simultaneously, as user habits change, the system will dynamically update frequently used function modules in real time to ensure that recommended functions always meet user needs.

[0090] Based on the same inventive concept, this application also provides a common menu recommendation device for implementing the aforementioned common menu recommendation method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more common menu recommendation device embodiments provided below can be found in the limitations of the common menu recommendation method described above, and will not be repeated here.

[0091] In one exemplary embodiment, such as Figure 4 As shown, a commonly used menu recommendation device is provided, which includes the following modules.

[0092] The behavior data acquisition module M1 is used to acquire user behavior data when accessing menus; the behavior data includes: the number of times a certain menu is accessed and the most recent access time.

[0093] The recommendation value calculation module M2 is used to calculate the recommendation value based on the behavioral data.

[0094] The sorting module M3 is used to generate a menu sort based on the recommended values; the menu sort is the sorting of the routing addresses corresponding to the menus.

[0095] The matching module M4 is used to match the menu table in the menu management module based on the menu sorting to obtain the list of menus frequently used by the user; the menu table includes several menus; each menu includes: name, routing address and icon.

[0096] The recommendation module M5 is used to render the menu list information onto the page to recommend frequently used menu items.

[0097] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores user access data for menus. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a common menu recommendation method.

[0098] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0099] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.

[0100] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0101] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0102] 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, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0103] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0104] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A commonly used menu recommendation method, characterized in that, The commonly used menu recommendation methods include: Obtain user behavior data for accessing menus; the behavior data includes: the number of times a certain menu item is accessed and the time of the most recent access; Based on the behavioral data, a recommendation value is calculated; Based on the recommended values, a menu sort is generated; the menu sort is based on the routing addresses corresponding to the menus. Based on the menu sorting, the menu table in the menu management module is matched to obtain the list of frequently used menus by the user; the menu table includes several menus; each menu includes: name, routing address, and icon; The menu list information is rendered onto the page to recommend frequently used menu items.

2. The commonly used menu recommendation method according to claim 1, characterized in that, Before acquiring user behavior data regarding menu access, the commonly used menu recommendation method also includes: Configure several menus in the menu management module; Create a custom annotation with parameters and mark the custom annotation on the query interface of each menu; the custom annotation is a parameter with a route address.

3. The commonly used menu recommendation method according to claim 1, characterized in that, After acquiring user access menu behavior data and before calculating the recommendation value based on the behavior data, the commonly used menu recommendation method further includes: The behavioral data is stored and updated using an ordered set from Redis.

4. The commonly used menu recommendation method according to claim 1, characterized in that, Obtain user behavior data when accessing the menu, specifically including: Retrieve custom annotations in Spring AOP aspects; Based on the custom annotation, user behavior data for accessing the menu is obtained; Alternatively, retrieve the logged-in user's information within a surrounding notification; Based on the logged-in user's information, user behavior data for accessing the menu is obtained.

5. The commonly used menu recommendation method according to claim 1, characterized in that, The formula for calculating the recommendation value is: ； time_weight=math.exp(-(current_time-last_access_time) / 3600); Where score is the recommended value; count is the number of times a menu item is accessed; time_weight is the weight of the most recent access time; math.exp() is the exponential operation with base e; current_time is the current time; and last_access_time is the most recent access time.

6. The commonly used menu recommendation method according to claim 1, characterized in that, Based on the recommended values, a menu sort is generated, specifically including: The recommended values ​​are sorted using a dynamic sorting algorithm to generate a menu sorting.

7. A commonly used menu recommendation device, characterized in that, The commonly used menu recommendation device is used to implement the commonly used menu recommendation method according to any one of claims 1-6, and the commonly used menu recommendation device includes: The behavior data acquisition module is used to acquire user behavior data when accessing menus; the behavior data includes: the number of times a certain menu is accessed and the most recent access time; The recommendation value calculation module is used to calculate the recommendation value based on the behavioral data; The sorting module is used to generate a menu sort based on the recommended values; the menu sort is sorted by the routing addresses corresponding to the menus. The matching module is used to match the menu table in the menu management module based on the menu sorting to obtain the list of menus frequently used by the user; the menu table includes several menus; each menu includes: name, routing address and icon; The recommendation module is used to render the menu list information onto the page to recommend frequently used menu items.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the common menu recommendation method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the common menu recommendation method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the common menu recommendation method as described in any one of claims 1-6.

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