A product list configuration method and device, a storage medium and an electronic device
By configuring a target product list, determining operational parameters, and using a greedy algorithm to process basic data, the target product list is generated and displayed. This solves the problem of heavy workload in the operation process, improves operational efficiency, and enables rapid updates to website content.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAOVO TECH
- Filing Date
- 2021-09-27
- Publication Date
- 2026-06-02
AI Technical Summary
In existing intelligent recommendation systems for the Internet or mobile Internet, the lack of intelligent recommendation functions in the operation process leads to heavy workload and low efficiency, especially when extracting and filtering the required data content from massive amounts of data.
By configuring a target product list, the target parameters for product promotion are determined, including weight, promotion content, target users, and conversion rate. A greedy algorithm is used to process the basic data, generate a target product list, and display it on the operations page to improve operational efficiency.
By configuring a target product list, the tedious work of operations staff is reduced, work efficiency is improved, website content iteration and updates are accelerated, and the user experience for operations staff is enhanced.
Smart Images
Figure CN115880014B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of information technology, and in particular to a product list configuration method, apparatus, storage medium, and electronic device. Background Technology
[0002] Most existing intelligent recommendation systems involving the Internet or mobile Internet are designed for advertising and content recommendations based on user browsing history. They are all based on big data analysis and intelligent product recommendation methods for e-commerce platforms.
[0003] The intelligent recommendation method includes the following steps: account information acquisition, historical shopping information filtering, historical shopping record analysis, historical shopping record time analysis, and product recommendation. Then, the acquired information is tagged and categorized, user profiles are created, and relevant marketing content is pushed to users for browsing.
[0004] They are mainly targeted at end users.
[0005] While current technologies have perfected user data collection and tagging, they lack intelligent recommendation capabilities for operational tasks, focusing primarily on initial data collection and subsequent analysis. This makes the operations department's workload extremely heavy and redundant, forcing them to extract necessary data from massive datasets, filter it, and then create operational plans. This extraction of essential data from vast amounts of data results in low operational efficiency. Summary of the Invention
[0006] This application provides a product list configuration method, apparatus, storage medium, and electronic device, which can improve operational efficiency by configuring a target product list.
[0007] In a first aspect, embodiments of this application provide a product list configuration method, the method comprising:
[0008] In response to an input operation, the target parameters for product promotion are determined; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize user spending behavior;
[0009] Based on the target parameters, the predetermined basic data is processed to obtain at least one target product list; wherein, the basic data includes lifestyle data and user data.
[0010] Secondly, embodiments of this application provide a product list configuration device, the device comprising:
[0011] The target parameter determination module is used to determine the target parameters for product promotion in response to input operations; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize user consumption.
[0012] The target product list acquisition module is used to process predetermined basic data according to the target parameters to obtain at least one target product list; wherein, the basic data includes lifestyle data and user data.
[0013] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the product list configuration method as described in embodiments of this application.
[0014] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the product list configuration method as described in embodiments of this application.
[0015] The technical solution provided in this application, in response to an input operation, determines the target parameters for product promotion, and processes pre-determined basic data based on the target parameters to obtain at least one target product list. This technical solution, by configuring the target product list, can improve operational efficiency and accelerate the iterative updates of website content. Attached Figure Description
[0016] Figure 1 This is a flowchart of the product list configuration method provided in Embodiment 1 of this application;
[0017] Figure 2 This is a schematic diagram of the basic architecture of the product configuration provided in Embodiment 1 of this application;
[0018] Figure 3 This is a schematic diagram of the product list configuration device provided in Embodiment 2 of this application;
[0019] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this application. Detailed Implementation
[0020] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.
[0021] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.
[0022] Example 1
[0023] Figure 1 This is a flowchart of a product list configuration method provided in Embodiment 1 of this application. This embodiment can be applied to situations where product promotion and operation plans are determined. This method can be executed by the product list configuration device provided in this embodiment. The device can be implemented by software and / or hardware and can be integrated into devices such as smart terminals used for data processing.
[0024] like Figure 1 As shown, the product list configuration method includes:
[0025] S110. In response to the input operation, determine the target parameters for product promotion; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize the user's consumption behavior;
[0026] In this embodiment, the weights can be set based on the operators' needs for product promotion. For example, a higher weight can be set for order volume, meaning that the operational plan is mainly determined for products with a high order volume. Alternatively, a higher weight can be set for page views, meaning that the operational plan is determined for products with a high page view volume.
[0027] The promotional content can include lifestyle-related content or video content. For example, lifestyle-related content could include shopping coupons or vouchers; video content could include video membership top-ups, etc.
[0028] In this solution, users can be categorized based on their geographic location, spending habits, spending amount, product browsing history, and other information. Each user category can be assigned a tag, and operations staff only need to input the tag for that user category to identify target users.
[0029] In this embodiment, the conversion rate can be set according to product promotion needs. For example, a higher conversion rate can be set, that is, by determining an operational plan to increase users' consumption of the product.
[0030] In this plan, operations personnel enter operation templates into the system according to certain rules, and determine the target parameters for product promotion based on the content of the operation templates.
[0031] In this technical solution, optionally, the weights include at least one of click weights, page view weights, and order weights; the promotional content includes at least one of product content and video content.
[0032] In this solution, clicks can refer to the number of times a user clicks on a product; page views can refer to the number of times a user views a product; and orders can refer to the number of times a user purchases a product. Different products have different promotional needs, so the weights can be set according to the specific promotional requirements.
[0033] By determining the weight and promotional content, operational plans can be tailored to different settings, which can improve operational efficiency and accelerate the iteration and updating of website content.
[0034] S120. Based on the target parameters, process the predetermined basic data to obtain at least one target product list; wherein the basic data includes lifestyle data and user data; wherein the target product list includes product images, product names, and product details.
[0035] Basic data refers to data categorized based on user browsing behavior. It includes two main categories: lifestyle data and user data. Lifestyle data encompasses various categories such as food and daily necessities data, which can be segmented based on product consumption volume, clicks, page views, and order volume to obtain different categories of product data. User data can be segmented based on user region, consumption habits, spending amount, and browsing type to obtain different categories of user data.
[0036] In this solution, product images, product names, and product details can be displayed on different pages through user clicks, allowing users to view product information and thus increasing the purchase rate.
[0037] In this embodiment, based on the target parameters, the goals to be achieved in product promotion can be determined. Based on these goals, basic data is filtered to identify multiple target product lists that meet the objectives, which are then used by operations personnel for product promotion. The target product lists can be sorted from highest to lowest quality. Operations personnel can select from the target product lists according to their needs. They can also fine-tune and modify the selected target product lists to ensure that the final promotion operation plan is the most satisfactory.
[0038] In this technical solution, optionally, based on the target parameters, predetermined basic data is processed to obtain at least one target product list, including:
[0039] Based on the target parameters, a greedy algorithm is used to iteratively filter the basic data to obtain at least one target product list.
[0040] The idea behind the greedy algorithm is to use a top-down approach and make successive greedy choices in an iterative manner. Each greedy choice simplifies the problem to be solved into a smaller subproblem. Through each greedy choice, an optimal solution determined from the basic data can be obtained.
[0041] In this solution, the target parameters are submitted to the intelligent algorithm module. The intelligent algorithm module will compare and calculate the basic data based on the obtained target parameters to obtain multiple target product lists for the operations staff to refer to.
[0042] Configuring a target product list can improve operational efficiency and accelerate the iteration and updating of website content.
[0043] In this technical solution, optionally, based on the target parameters, a greedy algorithm is used to iteratively filter the basic data to obtain at least one target product list, including:
[0044] Based on the weights, the filtering results of the basic data are adjusted to obtain optimal data, and at least one target product list is constructed based on the optimal data.
[0045] In this embodiment, operators can set different weights according to product promotion needs. For example, the order volume can be set to 60%. Different weights correspond to different operational plans. The basic data screening results can be adjusted in combination with these weights to obtain the optimal data that meets the weight requirements.
[0046] By configuring a target product list and adjusting it based on weights, operational efficiency can be improved and website content updates can be accelerated.
[0047] Optionally, in this technical solution, after obtaining at least one list of target products, the method further includes:
[0048] The list of at least one target product is displayed on the operations page so that operations personnel can promote the products based on the list of target products.
[0049] In this solution, the target product list can be displayed on the operations page in a nine-grid layout, or in a vertical layout. The displayed content can be continuously updated.
[0050] By configuring a target product list, the most tedious data comparison and selection work for the operations team is eliminated, effectively improving operational efficiency and accelerating website content iteration and updates. Furthermore, displaying the target product list enhances the user experience for operations staff.
[0051] In this technical solution, the optional process for determining the basic data includes:
[0052] The massive amount of data is labeled to obtain labeled data, and the labeled data is classified to obtain basic data; wherein, the massive amount of data is used to represent the collected data.
[0053] In this solution, the massive amounts of data can be labeled and classified by reading preset field information to obtain labeled basic data.
[0054] Processing massive amounts of data can reduce the processing time for basic data and improve work efficiency.
[0055] Optionally, in this technical solution, before tagging the massive amount of data to obtain the tag data, the method further includes:
[0056] The system collects users' browsing information, browsing habits, browsing content, and browsing duration based on a distributed file system, resulting in massive amounts of data.
[0057] In this embodiment, a server cluster collects user browsing information, browsing habits, browsing content, and browsing duration data during website visits. The massive amounts of data obtained are then read and processed by the Hadoop Distributed File System framework and a Redis cluster.
[0058] Processing massive amounts of data can reduce the processing time for basic data and improve work efficiency.
[0059] For example, Figure 2 This is a schematic diagram of the basic architecture of the product configuration provided in Embodiment 1 of this application, such as... Figure 2 As shown, the data collection cluster unit collects user browsing information, browsing habits, browsing content, and browsing duration. This data is then read and processed by the Hadoop and Redis clusters in the data processing unit to obtain basic data. The business logic unit iteratively filters this basic data to configure a target product list. The data layer is used to store the data.
[0060] The technical solution provided in this application, in response to an input operation, determines the target parameters for product promotion and processes pre-determined basic data based on these parameters to obtain at least one target product list. By implementing this technical solution, the workload of operations personnel and repetitive tasks can be reduced. The system automatically generates a set of front-end product display pages based on various weighted indicators assigned by operations personnel, thereby significantly improving the efficiency of product operations and accelerating the iteration and updating of website content.
[0061] Example 2
[0062] Figure 3 This is a schematic diagram of the product list configuration device provided in Embodiment 2 of this application, as shown below. Figure 3 As shown, the product list configuration device includes:
[0063] The target parameter determination module 310 is used to determine the target parameters for product promotion in response to an input operation; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize the user's consumption behavior;
[0064] The target product list acquisition module 320 is used to process predetermined basic data according to the target parameters to obtain at least one target product list; wherein, the basic data includes lifestyle data and user data.
[0065] In this technical solution, optionally, the target product list is obtained by module 320, which includes:
[0066] The target product list acquisition submodule is used to iteratively filter the basic data using a greedy algorithm based on the target parameters to obtain at least one target product list.
[0067] In this technical solution, optionally, the target product list acquisition submodule is used specifically for:
[0068] Based on the weights, the filtering results of the basic data are adjusted to obtain optimal data, and at least one target product list is constructed based on the optimal data.
[0069] Optionally, in this technical solution, the device further includes:
[0070] The target product list display module is used to display the at least one target product list on the operation page, so that operators can promote the products based on the target product list.
[0071] In this technical solution, optionally, the weights include at least one of click weights, page view weights, and order weights; the promotional content includes at least one of product content and video content.
[0072] In this technical solution, optionally, the target product list obtaining module 320 also includes:
[0073] The basic data acquisition submodule is used to label massive amounts of data to obtain labeled data, and to classify the labeled data to obtain basic data; wherein, the massive data is used to represent the collected data.
[0074] In this technical solution, the optional basic data acquisition submodule is specifically used for:
[0075] The system collects users' browsing information, browsing habits, browsing content, and browsing duration based on a distributed file system, resulting in massive amounts of data.
[0076] The above-mentioned products can perform the methods provided in the embodiments of this application, and have the corresponding functional modules and beneficial effects of performing the methods.
[0077] Example 3
[0078] This application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a product list configuration method, the method comprising:
[0079] In response to an input operation, the target parameters for product promotion are determined; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize user spending behavior;
[0080] Based on the target parameters, the predetermined basic data is processed to obtain at least one target product list; wherein the basic data includes lifestyle data and user data; wherein the target product list includes product images, product names, and product details.
[0081] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a computer system in which a program is executed, or it may reside in a different second computer system connected to the computer system via a network (such as the Internet). The second computer system can provide program instructions to the computer for execution. The term “storage medium” can include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) that can be executed by one or more processors.
[0082] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the product list configuration operation as described above, but can also execute related operations in the product list configuration method provided in any embodiment of this application.
[0083] Example 4
[0084] This application provides an electronic device that can integrate the product list configuration device provided in this application. Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this application. Figure 4 As shown, this embodiment provides an electronic device 400, which includes: one or more processors 420; and a storage device 410 for storing one or more programs. When the one or more programs are executed by the one or more processors 420, the one or more processors 420 implement the product list configuration method provided in this application embodiment. The method includes:
[0085] In response to an input operation, the target parameters for product promotion are determined; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize user spending behavior;
[0086] Based on the target parameters, the predetermined basic data is processed to obtain at least one target product list; wherein the basic data includes lifestyle data and user data; wherein the target product list includes product images, product names, and product details.
[0087] Of course, those skilled in the art will understand that the processor 420 also implements the technical solution of the product list configuration method provided in any embodiment of this application.
[0088] Figure 4 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0089] like Figure 4 As shown, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of processors 420 in the electronic device can be one or more. Figure 4 Taking a processor 420 as an example; the processor 420, storage device 410, input device 430, and output device 440 in the electronic device can be connected via a bus or other means. Figure 4 For example, China and Israel are connected via bus 450.
[0090] Storage device 410, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and module units, such as the program instructions corresponding to the product list configuration method in the embodiments of this application.
[0091] Storage device 410 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, storage device 410 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, storage device 410 may further include memory remotely located relative to processor 420, which can be connected via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0092] Input device 430 can be used to receive input digital, character, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 440 may include electronic devices such as a display screen and a speaker.
[0093] The electronic equipment provided in this application embodiment can improve operational efficiency.
[0094] The product list configuration device, storage medium, and electronic device provided in the above embodiments can execute the product list configuration method provided in any embodiment of this application, and have the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in the above embodiments can be found in the product list configuration method provided in any embodiment of this application.
[0095] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the appended claims.
Claims
1. A product list configuration method characterized by, include: In response to an input operation, the target parameters for product promotion are determined; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize user spending behavior; Based on the target parameters, the predetermined basic data is processed to obtain at least one target product list; wherein, the basic data includes lifestyle data and user data; wherein, the target product list includes product images, product names, and product details; the lifestyle data includes food data and daily necessities data; the user data is determined based on the user's browsing information, browsing habits, browsing content, and browsing duration; Specifically, based on the target parameters, predetermined basic data is processed to obtain at least one target product list, including: Based on the target parameters, a greedy algorithm is used to iteratively filter the basic data to obtain at least one target product list; The method further includes, after obtaining at least one list of target products: The list of at least one target product is displayed on the operations page so that operations personnel can promote the products based on the list of target products.
2. The method according to claim 1, characterized in that, Based on the target parameters, a greedy algorithm is used to iteratively filter the basic data to obtain at least one target product list, including: Based on the weights, the filtering results of the basic data are adjusted to obtain optimal data, and at least one target product list is constructed based on the optimal data.
3. The method according to claim 1, characterized in that, The weights include at least one of click weights, page view weights, and order weights; the promotional content includes at least one of product content and video content.
4. The method according to claim 1, characterized in that, The process of determining the basic data includes: The massive amount of data is labeled to obtain labeled data, and the labeled data is classified to obtain basic data; wherein, the massive amount of data is used to represent the collected data.
5. The method according to claim 4, characterized in that, Before labeling the massive amount of data to obtain the labeled data, the method also includes: The system collects users' browsing information, browsing habits, browsing content, and browsing duration based on a distributed file system, resulting in massive amounts of data.
6. A product list configuration device, characterized in that, include: The target parameter determination module is used to determine the target parameters for product promotion in response to input operations; wherein, the target parameters include weight, promotion content, target users, and conversion rate; the weight is used to characterize the degree of emphasis in product promotion; the conversion rate is used to characterize user consumption. The target product list acquisition module is used to process pre-determined basic data according to the target parameters to obtain at least one target product list; wherein, the basic data includes lifestyle data and user data; the lifestyle data includes food data and daily necessities data; the user data is determined based on the user's browsing information, browsing habits, browsing content, and browsing duration; The module for obtaining the target product list includes: The target product list acquisition submodule is used to iteratively filter the basic data using a greedy algorithm based on the target parameters to obtain at least one target product list. The device further includes: The target product list display module is used to display the at least one target product list on the operation page, so that operators can promote the products based on the target product list.
7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the product list configuration method as described in any one of claims 1-5.
8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the product list configuration method as described in any one of claims 1-5.