Price monitoring method and system for preventing price discrimination

Abstract

The invention discloses a price monitoring method and system for preventing price discrimination, and belongs to the technical field of Internet. According to the invention, a shopping demand of a target user is acquired, a price attention group is determined according to the shopping demand, and the price attention group comprises article information of a target article and a target user who purchases the target article; recording price discussion data of the price attention group, and determining a price data set according to the price discussion data and the price monitoring data; predicting a predicted price of the target article according to the price data set and a preset price prediction model; and generating a purchase suggestion of the target article according to the predicted price, the price data set and the price information at the current moment. The method has the beneficial effect of reducing the probability of selection and purchase by the user at an unfair price.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to a price monitoring method and system for preventing price discrimination. Background Technology

[0002] Price discrimination is prevalent in the current e-commerce environment, often manifesting as big data-driven price discrimination—differential pricing based on big data analytics for different customers. Currently, users typically make purchasing decisions by checking historical data changes. However, this historical data is often inaccurate and difficult to predict, leading to information asymmetry that causes users to pay higher prices. This makes it difficult to prevent or avoid price discrimination, resulting in unfair treatment of users during the shopping process.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a price monitoring method and system for preventing price discrimination, aiming to reduce the probability of users making purchases under unfair pricing conditions. To achieve the above objective, this invention provides a price monitoring method for preventing price discrimination, which includes the following steps: Obtain the shopping needs of target users, and determine the price attention group based on the shopping needs. The price attention group includes: the item information of the target item and the target users who have purchased the target item. Record the price discussion data of the price attention group, and determine the price dataset based on the price discussion data and price monitoring data; The predicted price of the target item is predicted based on the price dataset and the preset price prediction model; Purchase recommendations for the target item are generated based on the predicted price, the price dataset, and the current price information.

[0005] Optionally, the step of obtaining the shopping needs of the target user includes: The time the target user spends on the software interface is calculated, and the user's search data is recorded. Identify the target user's item needs based on the dwell time and the search data; The shopping needs are determined based on the item requirements and the items the user has saved.

[0006] Optionally, the price discussion data includes: price query data, discount planning data, and price evaluation data. The step of recording the price discussion data of the price attention group and determining the price dataset based on the price discussion data and price monitoring data includes: Record the price query data, and fit the price change data of the target item based on the price query data; Record the discount planning data, and determine the difficulty data for obtaining the discount based on the discount planning data; Record the price evaluation data, and predict the user's expected price range based on the price evaluation data; The price dataset is determined based on the price change data, the difficulty data, the user's expected price range data, and the price monitoring data.

[0007] Optionally, the step of determining the difficulty data for obtaining discounts based on the discount planning data includes: The number of steps required to obtain the discount price is determined based on the discount planning data. Simulate the execution of the discount implementation steps and calculate the achievement rate corresponding to each discount implementation step; The difficulty data is determined based on the number of steps and the achievement rate.

[0008] Optionally, the step of predicting the user's expected price range based on the price evaluation data includes: Extract the keyword data and price data from the price evaluation data; Based on the dialogue time when the price data appeared, the type and number of corresponding keywords were counted to obtain the statistical results. The user's expected price range data is determined based on the statistical results.

[0009] Optionally, the step of predicting the predicted price of the target item based on the price dataset and the preset price prediction model includes: Extract the feature data from the price dataset; The preset price prediction model is input based on the feature data, and the output result of the preset price prediction model is obtained. The predicted price is determined based on the output results.

[0010] Optionally, the step of generating a purchase suggestion for the target item based on the predicted price, the price dataset, and the current price information includes: Based on the predicted prices and price dataset, a price change curve is determined within a target time range, wherein the target time range includes a first time range before the current moment and a second time range after the current moment. The purchase recommendation is determined based on the price fluctuation curve and the price information.

[0011] Furthermore, to achieve the above objectives, the present invention also provides a price monitoring system for preventing price discrimination, the price monitoring system for preventing price discrimination comprising: The acquisition module is used to acquire the shopping needs of target users and determine the price attention group based on the shopping needs. The price attention group includes: the item information of the target item and the target users who have purchased the target item. The recording module is used to record price discussion data of the price attention group and determine the price dataset based on the price discussion data and price monitoring data; The prediction module is used to predict the price of the target item based on the price dataset and a preset price prediction model. The analysis module is used to generate purchase recommendations for the target item based on the predicted price, the price dataset, and the current price information.

[0012] Furthermore, to achieve the above objectives, the present invention also provides a price monitoring device for preventing price discrimination, the device comprising: a memory, a processor, and a price monitoring program for preventing price discrimination stored in the memory and executable on the processor, the price monitoring program for preventing price discrimination being configured to implement the steps of the price monitoring method for preventing price discrimination described above.

[0013] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a price monitoring program for preventing price discrimination, wherein the price monitoring program for preventing price discrimination, when executed by a processor, implements the steps of the price monitoring method for preventing price discrimination described above.

[0014] This invention proposes a price monitoring method to prevent price discrimination. This method obtains the shopping needs of target users, identifies price-concern groups based on these needs, records price discussion data within these groups, and determines a price dataset based on the price discussion data and price monitoring data. This yields complete user discussion data and price change data. The method then predicts the price of the target item using the price dataset and a preset price prediction model. Compared to simply using price data, the price dataset includes user discussion, resulting in more accurate data predictions. Finally, based on the predicted price, the price dataset, and current price information, a purchase recommendation for the target item is generated, thereby reducing the probability of users making purchases under unfair pricing conditions. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure of a price monitoring device for preventing price discrimination in the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the price monitoring method for preventing price discrimination according to the present invention; Figure 3 This is a flowchart illustrating a second embodiment of the price monitoring method for preventing price discrimination according to the present invention. Figure 4 This is a flowchart illustrating the third embodiment of the price monitoring method for preventing price discrimination according to the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] Reference Figure 1 , Figure 1 This is a schematic diagram of the price monitoring device structure for preventing price discrimination in the hardware operating environment involved in the embodiments of the present invention.

[0019] like Figure 1 As shown, the price monitoring device for preventing price discrimination may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interactive device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interactive device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0020] Those skilled in the art will understand that Figure 1The structure shown does not constitute a limitation on price monitoring equipment for preventing price discrimination, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0021] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a price monitoring program to prevent price discrimination.

[0022] exist Figure 1 In the price monitoring device for preventing price discrimination shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with users; the processor 1001 and memory 1005 in the price monitoring device for preventing price discrimination of the present invention can be set in the price monitoring device for preventing price discrimination, and the price monitoring device for preventing price discrimination calls the price monitoring program for preventing price discrimination stored in the memory 1005 through the processor 1001 and executes the price monitoring method for preventing price discrimination provided in the embodiment of the present invention.

[0023] This invention provides a price monitoring method for preventing price discrimination, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a price monitoring method for preventing price discrimination according to the present invention.

[0024] In this embodiment, the price monitoring method for preventing price discrimination includes: Step S1: Obtain the shopping needs of the target users and determine the price attention group based on the shopping needs. The price attention group includes: the item information of the target item and the target users who have purchased the target item. In this embodiment, the target user's shopping needs can be further supported by corresponding input boxes on the interface, allowing the user to actively input the items and services they wish to purchase. Preferably, the interface can also input the problem the user needs to solve, which could be a question about product selection or a life problem. For example, a user could input a question about how to solve a clogged drain. A price-focused group is generated based on the shopping needs, or the user is added to an existing price-focused group. This group includes users other than the target user, and these groups can exchange interactive information, product links, comments, and personal updates. Preferably, the item information of the target item is added to the group's shared data.

[0025] Step S2: Record the price discussion data of the price attention group, and determine the price dataset based on the price discussion data and price monitoring data; The discussion data here can be discussion data about the target item or discussion data about related items. In this embodiment, the type of discussion data is not limited to any of the aforementioned interactive information, product links, comments, and personal updates. Specifically, each data element of the discussion data is labeled according to data type, data time, and the user profile type of the user sending the data. The user profile can be directly obtained or determined based on the user's shopping history and personal data. The price dataset is obtained by merging the price discussion data and price monitoring data.

[0026] Step S3: Predict the price of the target item based on the price dataset and the preset price prediction model; In this embodiment, price features of the price dataset are extracted and input into the preset price prediction model. The predicted price of the target item is determined based on the output of the preset price prediction model. Step S4: Generate a purchase suggestion for the target item based on the predicted price, the price dataset, and the current price information.

[0027] Optionally, a complete price change curve is determined based on the predicted price and the price dataset. A purchase suggestion for the target item is generated based on the complete price change curve and the price information at the current moment. Specifically, the complete price change curve and the price information at the current moment can be input into a large language model, and the purchase suggestion can be determined based on the output of the large language model.

[0028] In this embodiment, by acquiring the shopping needs of target users and determining price attention groups based on these needs, price discussion data of these price attention groups is recorded, and a price dataset is determined based on the price discussion data and price monitoring data. This yields complete user discussion data and price change data. Based on the price dataset and a preset price prediction model, the predicted price of the target item is predicted. Compared to using only price data, the price dataset includes user discussion, resulting in more accurate data prediction. Based on the predicted price, the price dataset, and the current price information, a purchase suggestion for the target item is generated, thereby reducing the probability of users making purchases under unfair prices.

[0029] Furthermore, based on the first embodiment, a second embodiment of the price monitoring method for preventing price discrimination of the present invention is proposed. In this embodiment, reference is made to... Figure 3 The steps for obtaining the shopping needs of target users include: Step S11: Calculate the time the target user spends on the software interface and record the user's search data; Specifically, corresponding tracking points are deployed at various levels of the client-side interface. These interfaces include: product details page, product list page, and price comparison / historical price page. Specifically, the tracking points record the total time a user spends on a specific product page, the percentage of users actively scrolling to view details, and the time spent viewing user reviews. All search keywords entered by the user are also recorded, including keywords determined after spelling correction. Preferably, the tracking points can also record filter options clicked during the search process.

[0030] Step S12: Identify the target user's item needs based on the dwell time and the search data; In this embodiment, the item needs of the target user are identified based on the dwell time and the search data. The features corresponding to the dwell time and the search data of the target user are extracted to construct a user demand vector. The user demand vector is then input into a pre-trained demand identification model. The pre-trained demand identification model can be obtained by training a machine learning model with historical user data. Here, the machine learning model can be one of logistic regression or random forest.

[0031] Step S13: Determine the shopping needs based on the item requirements and the items the user has saved.

[0032] In this embodiment, the complete shopping needs of a user are determined by combining the item requirements with the items the user has collected.

[0033] In this embodiment, by statistically analyzing the time the target user spends on the software interface and recording the user's search data, the target user's item needs are identified based on the time spent on the software interface and the search data. The shopping needs are then determined based on the item needs and the items the user has saved, thereby improving the accuracy of identifying user needs.

[0034] Furthermore, based on the first or second embodiment, a third embodiment of the price monitoring method for preventing price discrimination of the present invention is proposed. In this embodiment, reference is made to... Figure 4 The price discussion data includes: price query data, discount planning data, and price evaluation data. The step of recording the price discussion data of the price attention group and determining the price dataset based on the price discussion data and price monitoring data includes: Step S21: Record the price query data, and fit the price change data of the target item based on the price query data; Specifically, in this embodiment, when a user needs to query a price, an abnormal price query is performed based on the user's query request to obtain the price query data. When a user publishes price information, the accuracy of the information published by the user is verified. This process typically involves multiple queries using accounts with different purchasing power to determine the price level among different user groups.

[0035] Step S22: Record the discount planning data and determine the difficulty data for obtaining the discount based on the discount planning data; The discount planning data here specifically refers to the steps and methods users take to achieve the corresponding price, commonly including coupon redemption methods and the stacking of coupons. When a user publishes price information, the system monitors the corresponding operational data, such as window activity, coupon redemption status, and platform task completion status. By recording the above discount planning data, corresponding features are extracted and identified to determine the difficulty level of the data.

[0036] Step S23: Record the price evaluation data and predict the user's expected price range based on the price evaluation data; In this embodiment, the price evaluation data is recorded, and by identifying the keywords in user comments under each price range, the user's expected price range data can be identified.

[0037] Step S24: Determine the price dataset based on the price change data, the difficulty data, the user's expected price range data, and the price monitoring data.

[0038] The price change data, the difficulty data, the user's expected price range data, and the price monitoring data are merged and standardized to obtain the price dataset.

[0039] In this embodiment, by recording the price query data, fitting the price change data of the target item based on the price query data, recording the discount planning data, determining the difficulty data of obtaining discounts based on the discount planning data, recording the price evaluation data, and predicting the user's expected price range based on the price evaluation data, a complete and accurate price dataset can be obtained, thereby improving the accuracy of subsequent price predictions.

[0040] Furthermore, the steps for determining the difficulty data of obtaining discounts based on the discount planning data include: The number of steps required to obtain the discount price is determined based on the discount planning data. Simulate the execution of the discount implementation steps and calculate the achievement rate corresponding to each discount implementation step; The difficulty data is determined based on the number of steps and the achievement rate.

[0041] In this embodiment, the discount planning data is quantitatively identified. Specifically, the number and type of steps required to obtain multiple discounted prices are determined, thereby determining the difficulty data for realizing the price posted by the user in the group. Based on the difficulty data, some prices are excluded. That is, prices that are not representative are excluded to avoid abnormal price data affecting the prediction of future price changes of the target item.

[0042] In this embodiment, the number of steps to achieve the discount corresponding to the discount price is determined by the discount planning data, the discount implementation steps are simulated and executed, and the achievement rate corresponding to each discount implementation step is calculated. The difficulty data is determined based on the number of steps and the achievement rate, thereby effectively avoiding the influence of unrepresentative price data on the prediction and improving the accuracy of subsequent price predictions.

[0043] Furthermore, the step of predicting the user's expected price range based on the price evaluation data includes: Extract the keyword data and price data from the price evaluation data; Based on the dialogue time when the price data appeared, the type and number of corresponding keywords were counted to obtain the statistical results. The user's expected price range data is determined based on the statistical results.

[0044] In this embodiment, different keywords are assigned different price ratings. Within a time interval associated with price data, the frequency of occurrence for each keyword type is counted. Statistical results are calculated based on the price ratings and the frequency of occurrence. Positive reviews can correspond to positive price ratings, while negative reviews can correspond to negative price ratings. Positive reviews generally refer to keywords such as "good price," "discount," "good value," "cheap," and "high cost-performance ratio." Other positive reviews typically include keywords like "pull," "NPC," and "not in a hurry." The user's expected price range is then determined based on the statistical results.

[0045] In this embodiment, by extracting keyword data and price data from the price evaluation data, and statistically analyzing the type and frequency of corresponding keywords based on the dialogue time when the price data appears, the statistical results are obtained. Based on the statistical results, the user's expected price range data is determined, thereby improving the completeness of the data.

[0046] Furthermore, based on any of the above embodiments, a fourth embodiment of the price monitoring method for preventing price discrimination of the present invention is proposed. In this embodiment, the step of predicting the predicted price of the target item based on the price dataset and the preset price prediction model includes: Extract the feature data from the price dataset; The preset price prediction model is input based on the feature data, and the output result of the preset price prediction model is obtained. The predicted price is determined based on the output results.

[0047] In this embodiment, the feature data can include price fluctuation frequency, price adjustment range, fluctuation frequency of user reviews, and the difficulty of achieving the lowest price at each moment. The above data is input into the preset price prediction model to obtain its output. Generally, the model outputs the probability corresponding to each price prediction, and the price with the highest probability is selected as the predicted price. In other embodiments, the model further includes predicting the difficulty of achieving each price, and selecting the price with the lowest difficulty as the prediction result.

[0048] Furthermore, based on any of the above embodiments, a fifth embodiment of the price monitoring method for preventing price discrimination of the present invention is proposed. In this embodiment, the step of generating a purchase suggestion for the target item based on the predicted price, the price dataset, and the current price information includes: Based on the predicted prices and price dataset, a price change curve is determined within a target time range, wherein the target time range includes a first time range before the current moment and a second time range after the current moment. The purchase recommendation is determined based on the price fluctuation curve and the price information.

[0049] Furthermore, this invention also proposes a price monitoring system for preventing price discrimination, the price monitoring system for preventing price discrimination comprising: The acquisition module is used to acquire the shopping needs of target users and determine the price attention group based on the shopping needs. The price attention group includes: the item information of the target item and the target users who have purchased the target item. The recording module is used to record price discussion data of the price attention group and determine the price dataset based on the price discussion data and price monitoring data; The prediction module is used to predict the price of the target item based on the price dataset and a preset price prediction model. The analysis module is used to generate purchase recommendations for the target item based on the predicted price, the price dataset, and the current price information.

[0050] Furthermore, this invention also proposes a price monitoring device for preventing price discrimination. The device includes a memory, a processor, and a price monitoring program for preventing price discrimination stored in the memory and executable on the processor. The price monitoring program for preventing price discrimination is configured to implement the steps of the price monitoring method for preventing price discrimination described above.

[0051] Furthermore, embodiments of the present invention also propose a storage medium storing a price monitoring program for preventing price discrimination, wherein the price monitoring program for preventing price discrimination, when executed by a processor, implements the steps of the price monitoring method for preventing price discrimination described above.

[0052] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0053] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0054] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0055] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A price monitoring method for preventing price discrimination, characterized in that, The price monitoring method for preventing price discrimination includes the following steps: Obtain the shopping needs of target users, and determine the price attention group based on the shopping needs. The price attention group includes: the item information of the target item and the target users who have purchased the target item. Record the price discussion data of the price attention group, and determine the price dataset based on the price discussion data and price monitoring data; The predicted price of the target item is predicted based on the price dataset and the preset price prediction model; Purchase recommendations for the target item are generated based on the predicted price, the price dataset, and the current price information.

2. The price monitoring method for preventing price discrimination as described in claim 1, characterized in that, The steps for obtaining the shopping needs of target users include: The time the target user spends on the software interface is calculated, and the user's search data is recorded. Identify the target user's item needs based on the dwell time and the search data; The shopping needs are determined based on the item requirements and the items the user has saved.

3. The price monitoring method for preventing price discrimination as described in claim 1, characterized in that, The price discussion data includes: price query data, discount planning data, and price evaluation data. The step of recording the price discussion data of the price attention group and determining the price dataset based on the price discussion data and price monitoring data includes: Record the price query data, and fit the price change data of the target item based on the price query data; Record the discount planning data, and determine the difficulty data for obtaining the discount based on the discount planning data; Record the price evaluation data, and predict the user's expected price range based on the price evaluation data; The price dataset is determined based on the price change data, the difficulty data, the user's expected price range data, and the price monitoring data.

4. The price monitoring method for preventing price discrimination as described in claim 3, characterized in that, The step of determining the difficulty data for obtaining discounts based on the discount planning data includes: The number of steps required to obtain the discount price is determined based on the discount planning data. Simulate the execution of the discount implementation steps and calculate the achievement rate corresponding to each discount implementation step; The difficulty data is determined based on the number of steps and the achievement rate.

5. The price monitoring method for preventing price discrimination as described in claim 3, characterized in that, The step of predicting the user's expected price range based on the price evaluation data includes: Extract the keyword data and price data from the price evaluation data; Based on the dialogue time when the price data appeared, the type and number of corresponding keywords were counted to obtain the statistical results. The user's expected price range data is determined based on the statistical results.

6. The price monitoring method for preventing price discrimination as described in claim 1, characterized in that, The step of predicting the predicted price of the target item based on the price dataset and the preset price prediction model includes: Extract the feature data from the price dataset; The preset price prediction model is input based on the feature data, and the output result of the preset price prediction model is obtained. The predicted price is determined based on the output results.

7. The price monitoring method for preventing price discrimination as described in any one of claims 1 to 6, characterized in that, The step of generating a purchase recommendation for the target item based on the predicted price, the price dataset, and the current price information includes: Based on the predicted prices and price dataset, a price change curve is determined within a target time range, wherein the target time range includes a first time range before the current moment and a second time range after the current moment. The purchase recommendation is determined based on the price fluctuation curve and the price information.

8. A price monitoring system for preventing price discrimination, characterized in that, The price monitoring system for preventing price discrimination includes: The acquisition module is used to acquire the shopping needs of target users and determine the price attention group based on the shopping needs. The price attention group includes: the item information of the target item and the target users who have purchased the target item. The recording module is used to record price discussion data of the price attention group and determine the price dataset based on the price discussion data and price monitoring data; The prediction module is used to predict the price of the target item based on the price dataset and a preset price prediction model. The analysis module is used to generate purchase recommendations for the target item based on the predicted price, the price dataset, and the current price information.

9. A price monitoring device for preventing price discrimination, characterized in that, The device includes: a memory, a processor, and a price monitoring program for preventing price discrimination stored in the memory and executable on the processor, the price monitoring program for preventing price discrimination being configured to implement the steps of the price monitoring method for preventing price discrimination as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a price monitoring program for preventing price discrimination, which, when executed by a processor, implements the steps of the price monitoring method for preventing price discrimination as described in any one of claims 1 to 7.

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