Shopping basket purchasing behavior-based category optimization method and system, and storage medium

Abstract

The invention discloses a category optimization method and system based on a shopping basket purchasing behavior and a storage medium, and the method comprises the following steps: S1, obtaining historical transaction data, and estimating the preference of a consumer; s2, grouping the commodities according to earnings; s3, extracting historical data, and performing category optimization on each group of products through the optimization model; s4, selecting a commodity group optimization result with the maximum expected income as final category recommendation; and S5, sending the final category recommendation result to a management system for intelligent category display decision making. According to the method, the calculation efficiency of category optimization is improved, the operation burden of an enterprise can be effectively controlled, and the service level and customer satisfaction of the enterprise are ensured.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a category optimization method, system and storage medium based on shopping basket purchase behavior. Background Art

[0002] With the advent of the big data era, in-depth analysis of consumer shopping basket purchase behavior has become an important topic in enterprise category optimization. Traditional retailers' category management (or product selection management) mainly relies on the experience and intuition of sales staff, or adopts a long-tail strategy, that is, providing all products that can be produced for consumers to choose. In practice, this method has been found to be limited in energy, often ignoring the correlation between products and consumers' combination purchase habits, resulting in unreasonable product combinations and room for profit improvement. In addition, incomplete information or human judgment errors may make the demand forecast of some products more accurate, while the forecast deviation of other products is large, and the overall accuracy fluctuates. Based on this, how to improve the utilization of shopping basket data, improve the accuracy of product demand forecasting, and then optimize the efficiency and accuracy of category management is a problem that needs to be solved at present.

[0003] The patent with publication number CN118735634A discloses a method, device and computing equipment for processing product listings, which specifically discloses a method of building a product resource pool by collecting massive amounts of product data, and screening candidate products from the product selection pool according to the user's key product selection information, so that the user can select products from the candidate products. The patent with publication number CN118735638A discloses a cross-border e-commerce platform product selection method and system based on big data: generating a market trend set; obtaining and recording the sales of merchants' historical product selections; analyzing market trends and evaluating merchants' historical product selections; building a product selection analysis model, analyzing the candidate products provided by merchants based on the model, and generating a product selection recommendation strategy. Although the above patent has realized a certain intelligent product selection decision-making method based on historical data, it does not take into account consumers' shopping basket purchase behavior.

[0004] In addition, existing online convex optimization methods are widely used in the field of machine learning, but are rare in the field of integer optimization such as category optimization. The patent with publication number CN117668712A discloses a category optimization method and related devices based on artificial intelligence and the nine-square screening method: historical business data is obtained and model training is performed to obtain a target category optimization model set; the target category optimization model set is used to perform comprehensive indicator calculations on the multi-dimensional indicators and multiple indicators of the historical business data to obtain an indicator data set; the indicator data set is optimized and calculated by the nine-square screening algorithm, and a list of goods in each square is output, and category optimization recommendation data is generated based on the list of goods in each square; the category optimization recommendation data is optimized and report output is performed interactively based on the screening conditions to obtain multiple target product list reports. The patent with publication number CN115204506A discloses a method, device and computer equipment for optimizing the categories of power equipment, which determine the categories to be evaluated and multiple evaluation items; calculate the evaluation parameters corresponding to each evaluation item based on the statistical data of the categories to be evaluated; determine the optimization parameters of the categories to be evaluated based on each evaluation parameter; and determine that the categories to be evaluated need to be optimized when the optimization parameters meet the optimization conditions. This patented method can reduce the influence of subjective judgment factors in the optimization of production equipment categories and realize objective quantitative evaluation of equipment category optimization. However, the above-mentioned category optimization method does not have the characteristics of the e-commerce environment and does not have convergent results; the method does not take the retailer's maximum profit as the optimization objective function, and cannot solve the category optimization problem faced by e-commerce companies that expect to maximize profits. Summary of the invention

[0005] Based on the technical problems existing in the background technology, the present invention proposes a category optimization method, system and storage medium based on shopping basket purchase behavior, which not only improves the computational efficiency of category optimization, but also effectively controls the operating burden of the enterprise and ensures the service level and customer satisfaction of the enterprise.

[0006] The category optimization method based on shopping basket purchase behavior proposed in the present invention has the following steps: S1: Obtain historical transaction data and estimate consumer preferences; S2: Group the products according to their revenue; S3: Extract historical data and optimize the category of each group of products through the optimization model; S4: Select the optimization result of the product group with the largest expected profit as the final category recommendation; S5: Send the final category recommendation results to the management system for intelligent category display decision-making.

[0007] Preferably, the consumer preferences in S1 include consumer type and quantity purchased by product With preference list Composition and each stage The arrival probability corresponding to each consumer type .

[0008] Preferably, the grouping method in S2 is: sort the product set for pre-selection optimization according to net profit, and group them according to a certain multiple, and the maximum profit difference in each group of products does not exceed the multiple.

[0009] Preferably, the optimization model in S3 is: in, Purchase quantity for the product; is a list of preferences; is the arrival probability; The data for each stage; It is a collection of data from all historical stages; For products; For consumer type; is the total number of all consumer types; is a binary decision variable, i.e., whether to provide the product.

[0010] Preferably, the method steps for performing category optimization in S3 are as follows: S31: Set the starting solution of the optimization model to , on each product group, loop t=1,2,…,T; S32: extract historical transaction data from an independent and identically distributed system without replacement; S33: Calculate the super gradient through the optimization model according to the extracted historical transaction data; S34: Update ; S35: Euclidean projection into the feasible domain, ; S36: By rounding to the interval {0,1}, we get ; S37: For all Take the average and then use the rounding method to get an integer solution as the category optimization result.

[0011] Preferably, the rounding method is a random exchange rounding method, a random pipeline rounding method or a dependent rounding method.

[0012] The category optimization system based on shopping basket purchase behavior proposed by the present invention includes: Acquisition module, used to obtain historical transaction data; Estimation module, used to estimate consumer preferences; The grouping module is used to group products according to revenue; The optimization module is used to extract historical data and optimize the category of each group of products through the optimization model; The push module is used to select the optimization results of the product group with the largest expected profit as the final category recommendation, and send the final category recommendation results to the management system for intelligent category display decision-making.

[0013] The computer-readable storage medium proposed in the present invention stores a computer program, and when the computer program is executed by a processor, the above-mentioned category optimization method based on shopping basket purchase behavior is implemented.

[0014] Beneficial technical effects of the present invention: Unlike the prior art that uses human experience to select products and artificial intelligence to predict, the present invention adopts a more fine-grained customer demand modeling method, further considering consumers' multi-product purchasing behavior and preference modeling methods that conform to rational consumer selection behavior, and obtains product category optimization results based on maximizing retailers' profits, thereby improving consumer satisfaction and reducing retailers' operating costs, ensuring that the product types owned are within a reasonable range, and ensuring the company's operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the category optimization method based on shopping basket purchase behavior proposed by the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further explained below in conjunction with specific embodiments.

[0017] Example 1 Reference Figure 1 , the present invention proposes The category optimization method based on shopping basket purchase behavior proposed in the present invention has the following steps: S1: Obtain historical transaction data and estimate consumer preferences Set condition information and filter out historical sales data that meet the conditions, where the condition information includes the historical time range and the complete set of products that need to be optimized. Based on the above historical data, the estimated consumer preferences include: Consumer type and quantity purchased by product With preference list Composition and each stage The arrival probability corresponding to each consumer type .

[0018] S2: Group products by revenue Specifically: the product collection that will be optimized for product selection According to net profit Sort from largest to smallest, Indicates products net profit; group them according to multiples of 2, making the total number of groups equal to M, and ensure that the maximum profit difference in each group of products does not exceed 2.

[0019] S3: Extract historical data and optimize the category of each group of products through the optimization model The optimization model is: in, Purchase quantity for the product; is a list of preferences; is the arrival probability; Data for each period (day, week, etc.); It is a collection of data from all historical stages; For products; For consumer type; is the total number of all consumer types; It is a binary decision variable, that is, whether to provide the product. It takes 1 when the product is provided and 0 when the product is not provided.

[0020] The optimization steps are as follows: S31: Input the estimated results based on historical data; product grouping results; learning rate of each group m ; S32: Set the starting solution to , on each product group, loop t=1,2,…,T; S33 extracts historical transaction data independently and identically distributed without replacement; S34: Calculate super gradient based on the extracted historical transaction data ; S35: Update ; S36: Euclidean projection into the feasible domain, ; S37: By using the above rounding method, we can round to the interval {0,1} and get ; S37: For all Take the average and then use the rounding method to get an integer solution as the category optimization result.

[0021] The rounding methods used are random exchange rounding, random pipeline rounding or dependent rounding.

[0022] S4: Select the optimization result of the product group with the largest expected profit as the final category recommendation Specifically, the total profit is initialized to 0; for each customer type k, perform the following steps: Find out which products are available (those that meet certain criteria); Select the first few available products (quantity is ); Calculate the profits of these selected products and add them to the total profit; Returns the calculated total profit.

[0023] S5: Send the final category recommendation results to the management system for intelligent category display decision-making.

[0024] The present invention has good performance in the decision-making process of retailers. Taking e-commerce enterprises as an example, after applying the system, business personnel are provided with a quick-response and scientific category optimization tool, which improves the level of category optimization. For enterprises, it can achieve the effects of increasing the number of orders and transaction amounts, reducing personnel costs, and maintaining customer satisfaction. Performance on the data set, specifically: In the simulation case results, this method can obtain 97% of the approximate optimal solution of the commercial solver in 1800 seconds in less than 2 seconds for 100 products.

[0025] The present invention adopts a more fine-grained customer demand modeling method, further considers consumers' multi-product purchasing behavior and preference modeling methods that conform to rational consumer selection behavior, and obtains product category optimization results based on maximizing retailers' profits, thereby improving consumer satisfaction and reducing retailers' operating costs, ensuring that the product types owned are within a reasonable range, and ensuring the company's operating efficiency; the results of the implementation examples show that the method of the present invention has good computing performance and practical application value.

[0026] Example 2 The category optimization system based on shopping basket purchase behavior proposed by the present invention includes: Acquisition module, used to obtain historical transaction data; Estimation module, used to estimate consumer preferences; The grouping module is used to group products according to revenue; The optimization module is used to extract historical data and optimize the category of each group of products through the optimization model; The push module is used to select the optimization results of the product group with the largest expected profit as the final category recommendation, and send the final category recommendation results to the management system for intelligent category display decision-making.

[0027] Example 3 The computer-readable storage medium proposed in the present invention stores a computer program, and when the computer program is executed by a processor, the category optimization method based on shopping basket purchase behavior of Example 1 is implemented.

[0028] The category optimization method of the present invention can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or communicates with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether it is removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or part thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

Claims

1. A category optimization method based on shopping basket purchase behavior, characterized in that: The steps are as follows: S1: Obtain historical transaction data and estimate consumer preferences; S2: Group the products according to their revenue; S3: Extract historical data and optimize the category of each group of products through the optimization model; S4: Select the optimization result of the product group with the largest expected profit as the final category recommendation; S5: Send the final category recommendation results to the management system for intelligent category display decision-making.

2. The category optimization method based on shopping basket purchase behavior according to claim 1, characterized in that: Consumer preferences in S1 include consumer types and quantity purchased by product With preference list Composition and each stage The arrival probability corresponding to each consumer type .

3. The category optimization method based on shopping basket purchase behavior according to claim 1, characterized in that: The grouping method in S2 is: sort the product set for pre-selection optimization according to net profit, and group them according to a certain multiple, and the maximum profit difference in each group of products does not exceed the multiple.

4. The category optimization method based on shopping basket purchase behavior according to claim 1, characterized in that: The optimized model in S3 is: in, Purchase quantity for the product; is a list of preferences; is the arrival probability; The data for each stage; It is a collection of data from all historical stages; For products; For consumer type; is the total number of all consumer types; is a binary decision variable, i.e. whether to provide the product.

5. The category optimization method based on shopping basket purchase behavior according to claim 4 is characterized in that: The steps for category optimization in S3 are as follows: S31: Set the starting solution of the optimization model to , on each product group, loop t=1,2,…,T; S32: extract historical transaction data from an independent and identically distributed system without replacement; S33: Calculate the super gradient through the optimization model according to the extracted historical transaction data; S34: Update ; S35: Euclidean projection into the feasible domain, ; S36: By rounding to the interval {0,1}, we get ; S37: For all Take the average and then use the rounding method to get an integer solution as the category optimization result.

6. The category optimization method based on shopping basket purchase behavior according to claim 5, characterized in that: The rounding method is random swap rounding, random pipeline rounding, or dependent rounding.

7. Category optimization system based on shopping basket purchase behavior, characterized by: include: Acquisition module, used to obtain historical transaction data; Estimation module, used to estimate consumer preferences; The grouping module is used to group products according to revenue; The optimization module is used to extract historical data and optimize the category of each group of products through the optimization model; The push module is used to select the optimization results of the product group with the largest expected profit as the final category recommendation, and send the final category recommendation results to the management system for intelligent category display decision-making.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the category optimization method based on shopping basket purchase behavior as described in any one of claims 1 to 6 is implemented.

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