Idle product transaction platform pricing method based on privacy calculation

Abstract

The invention discloses an idle product transaction platform pricing method based on privacy calculation, an idle product transaction platform comprises a pricing platform and an internet platform, the pricing platform is provided with a plurality of pricing servers, and each user interacts with a commodity server located on the internet platform through the pricing server; the pricing method comprises the steps that S1, a personalized recommendation pool is constructed on an internet platform, and the pricing platform obtains hobbies and interests of users based on the training learning process of federal learning and puts the hobbies and interests into the personalized recommendation pool; s2, constructing a pricing interval for the commodities on the pricing platform; s3, setting a price rule for the commodity on a pricing platform, pricing the commodity according to a preset price rule based on the commodity shelf time and a preset price interval, and uploading a real-time price to an internet platform; and S4, the Internet platform completes personalized commodity recommendation to the user through a pricing platform based on the product information in the recommendation pool and the obtained price information of the implemented commodities.

Description

Technical Field

[0001] The present application belongs to the field of privacy computing technology, and in particular, relates to a pricing method for an idle goods trading platform based on privacy computing. Background Art

[0002] With the development of informatization, platform economy as a new economic form is attracting more and more attention. Internet shopping platforms have become an important part of market economic activities and an indispensable part of people's lives. However, while developing rapidly, the negative factors of Internet shopping platforms have gradually emerged, such as Internet monopoly, infringement of user data privacy, and "big data killing old customers". In this context, it is particularly necessary to conduct more in-depth research on Internet platform companies. Among them, price is one of the core elements of the market economy. Pricing decision-making issues have always been a hot topic for scholars at home and abroad. Currently, supply chain pricing decisions based on different preference factors such as product or channel differences have become the mainstream of research. At present, there are the following problems in the pricing of shopping platform products: (1) Too high or too low pricing: Too high pricing will make it difficult for products to compete and sales will be poor, while too low pricing may affect the profitability of enterprises. (2) Unreasonable pricing strategy: The seller's pricing strategy is unclear, such as frequent price changes and lack of stability, which makes it difficult for consumers to form a sense of trust. (3) Ignoring cost and value: Sellers ignore the production cost and market value of products, resulting in unreasonable pricing. (4) Opaque or unfair pricing: Customers may feel that pricing is opaque and unfair, leading to dissatisfaction and complaints.

[0003] However, as an indispensable part of online shopping platforms, idle goods trading platforms have both the characteristics of ordinary shopping platforms and the characteristics that ordinary shopping platforms do not have. For example: (1) idle goods are usually goods that the seller will not use again after replacing them. Usually, they cannot be priced using traditional pricing methods due to their newness, seller preferences and other characteristics; (2) Sellers have different sales urgency, such as some sellers want to sell as soon as possible, while some sellers can wait for a longer sales cycle; (3) Sellers change their mentality. Usually, idle goods that cannot be sold for a long time will be discarded.

[0004] Therefore, there is an urgent need for a pricing method that can effectively protect user privacy, avoid big data price discrimination, and help sellers sell idle items as soon as possible. Summary of the invention

[0005] The purpose of this application is to overcome the problems of the prior art and disclose a pricing method for an idle item trading platform based on privacy computing. This method can effectively protect user privacy and avoid big data price discrimination, and can also help sellers sell idle items as soon as possible.

[0006] The purpose of this application is achieved through the following technical solutions:

[0007] A pricing method for an idle goods trading platform based on privacy computing, wherein the idle goods trading platform comprises a pricing platform and an Internet platform, wherein the pricing platform is provided with a plurality of pricing servers, and each user interacts with a commodity server located on the Internet platform via the pricing servers;

[0008] The idle goods trading platform pricing method includes:

[0009] S1: Build a personalized recommendation pool on the Internet platform. The pricing platform obtains users' preferences and interests based on the training and learning process of federated learning and puts them into the personalized recommendation pool.

[0010] S2: Construct pricing ranges for products on the pricing platform. Product sellers provide a price range [m, n] for each product, where m <n;

[0011] S3: Set price rules for products on the pricing platform, price products according to preset price rules based on product listing time and preset price range, and upload real-time prices to the Internet platform;

[0012] S4: The Internet platform completes personalized product recommendations to the corresponding server of the pricing platform based on the product information in the recommendation pool and the obtained actual product price information. The user obtains product recommendations from the idle product trading platform based on the pricing server and completes the transaction.

[0013] According to a preferred embodiment, in step S3, the price rules of the commodity include:

[0014] Products are priced from high to low based on the time they are put on the shelves. The price of a product when it is first put on the shelves is the highest price n in the pricing range provided by the seller, and the price is periodically reduced according to the sales time.

[0015] According to a preferred embodiment, in step S3, a heat auction method is introduced in the process of periodic price reduction according to the sales time.

[0016] When the click rate of a product does not reach the preset popularity value within a period, the price of the corresponding product is too high, and the pricing range is activated to reduce the price of the product;

[0017] When the click rate of a product reaches the preset popularity value within a cycle and it is not sold, the product introduction information cannot attract platform users, so the recommendation information in the personalized recommendation pool is activated and the product introduction information is re-listed.

[0018] According to a preferred embodiment, the popularity is obtained by self-learning of the idle goods trading platform.

[0019] According to a preferred embodiment, during the heat statistics process, only one click is recorded when the same user browses the same product of the same price.

[0020] According to a preferred embodiment, clicks of users added to the untrustworthy user pool during the heat statistics process are regarded as invalid clicks; users in the untrustworthy user pool are defined as users who frequently click and browse the same product in a certain period of time.

[0021] According to a preferred embodiment, the commodity price in the next period of the periodic price reduction process is:

[0022] The product price in the next cycle = current product price * (1-1 / product popularity - seller urgency index); product popularity refers to the number of clicks in the corresponding period after the product goes online, and the seller urgency index is determined by the seller's waiting time. If the seller accepts the sale of the item within p days, the seller urgency index is 1 / p.

[0023] The aforementioned main scheme of the present application and its further options can be freely combined to form multiple schemes, all of which are schemes that can be adopted and claimed for protection in the present application. After understanding the scheme of the present application, those skilled in the art can understand that there are multiple combinations based on the prior art and common knowledge, all of which are technical schemes to be protected by the present application, and they are not exhaustively listed here.

[0024] Beneficial effects of this application:

[0025] Users do not interact directly with the product server, which can protect user privacy and avoid big data price discrimination; it can also avoid repeated clicks by a single participant, that is, a user frequently clicks on the same item to increase the popularity of the item. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of the architecture of the idle goods trading platform of this application. DETAILED DESCRIPTION

[0027] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0028] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0029] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the application is usually placed when in use. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0030] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0031] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0032] In addition, the present application would like to point out that, in the present application, unless the specific structure, connection relationship, positional relationship, power source relationship, etc. are specifically written out, the structure, connection relationship, positional relationship, power source relationship, etc. involved in the present application are all known by those skilled in the art on the basis of the prior art without creative work.

[0033] Example 1

[0034] refer to Figure 1 As shown, the present application discloses a pricing method for an idle item trading platform based on privacy computing. The idle item trading platform includes a pricing platform and an Internet platform. The pricing platform is provided with a number of pricing servers, and each user interacts with a commodity server located on the Internet platform via the pricing server.

[0035] The pricing method of the idle goods trading platform includes the following steps:

[0036] Step S1: Build a personalized recommendation pool on the Internet platform. The pricing platform obtains the user's preferences and interests based on the training and learning process of federated learning, and puts them into the personalized recommendation pool. The construction of the personalized recommendation pool is obtained from the parameters of the federated participants.

[0037] The training learning process based on federated learning obtains users' preferences and interests and puts them into the personalized recommendation pool. And users do not directly interact with the commodity server. The web background (pricing platform) is used as the server of federated learning, and platform users are the participants of federated learning. Introducing federated learning can effectively prevent the shopping platform from memorizing user information, effectively protect user data privacy, and avoid the behavior of big data price discrimination on the platform.

[0038] Step S2: Build a pricing range for the commodity on the pricing platform. The commodity seller provides a price range [m, n] for each commodity, where m < n. Different from the deterministic pricing criterion of a traditional platform where a commodity has only one price, the present invention introduces the pricing range theory, that is, the commodity seller needs to provide an acceptable price range for each commodity so as to carry out periodic price reduction for the commodity according to the algorithm.

[0039] Step S3: Set a price rule for the commodity on the pricing platform. Based on the commodity listing time and the preset price range, the commodity is priced according to the preset price rule, and the real-time price is uploaded to the Internet platform.

[0040] Preferably, in step S3, the price rule of the commodity includes: the commodity is priced from high to low based on the listing time. The price when the commodity is just listed is the highest price n in the pricing range provided by the seller, and periodic price reduction is carried out according to the sales time.

[0041] Further, in step S3, a heat auction method is introduced during the process of periodic price reduction according to the sales time.

[0042] When the click-through rate of a commodity does not reach the preset heat value within one cycle, it means that the price of the corresponding commodity is too high, and the pricing range is activated to lower the price of the commodity.

[0043] When the click-through rate of a commodity reaches the preset heat value within one cycle and the commodity is not sold out at the same time, it means that the introduction information of the commodity cannot attract platform users, and then the recommendation information in the personalized recommendation pool is activated to re-list the commodity introduction information.

[0044] Preferably, the heat is obtained by the second-hand trading platform's self-learning.

[0045] Further, during the heat statistics process, only one click is recorded for the same user's browsing of the same commodity at the same price.

[0046] Preferably, the clicks of users added to the dishonest user pool are regarded as invalid clicks during the heat statistics process; users in the dishonest user pool are defined as users who frequently click (for example, click the same commodity more than 5 times on the same day) and browse the same commodity within a certain period of time, thus avoiding the behavior of brushing heat.

[0047] Preferably, during the periodic price reduction process, the commodity price of the next cycle is: commodity price of the next cycle = current commodity price * (1-1 / commodity popularity-seller urgency index); wherein, commodity popularity refers to the number of clicks in the corresponding period after the commodity is launched, and the seller urgency index is determined by the seller's waiting time. If the seller accepts the sale of the item within p days, the seller urgency index is 1 / p.

[0048] Step S4: The Internet platform completes personalized product recommendations to the corresponding server of the pricing platform based on the product information in the recommendation pool and the obtained actual product price information. The user obtains product recommendations from the idle product trading platform based on the pricing server and completes the transaction.

[0049] Therefore, the method of the present invention can not only help sellers quickly match suitable buyers to sell their idle items, but also effectively avoid the phenomenon of big data price discrimination while protecting user data privacy.

[0050] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A pricing method for an idle goods trading platform based on privacy computing, characterized in that: The idle goods trading platform includes a pricing platform and an Internet platform. The pricing platform is provided with a number of pricing servers, and each user interacts with a commodity server located on the Internet platform via the pricing servers; The idle goods trading platform pricing method includes: S1: Build a personalized recommendation pool on the Internet platform. The pricing platform obtains users' preferences and interests based on the training and learning process of federated learning and puts them into the personalized recommendation pool. S2: Construct pricing ranges for products on the pricing platform. Product sellers provide a price range [m, n] for each product, where m <n; S3: Set price rules for products on the pricing platform, price products according to preset price rules based on product listing time and preset price range, and upload real-time prices to the Internet platform; S4: The Internet platform completes personalized product recommendations to the corresponding server of the pricing platform based on the product information in the recommendation pool and the obtained actual product price information. The user obtains product recommendations from the idle product trading platform based on the pricing server and completes the transaction.

2. The idle goods trading platform pricing method according to claim 1, characterized in that: In step S3, the price rules of the commodity include: Products are priced from high to low based on the time they are put on the shelves. The price of a product when it is first put on the shelves is the highest price n in the pricing range provided by the seller, and the price is periodically reduced according to the sales time.

3. The idle goods trading platform pricing method according to claim 2, characterized in that: In step S3, a heat auction method is introduced in the process of periodic price reduction according to the sales time. When the click rate of a product does not reach the preset popularity value within a period, the price of the corresponding product is too high, and the pricing range is activated to reduce the price of the product; When the click rate of a product reaches the preset popularity value within a cycle and it is not sold, the product introduction information cannot attract platform users, so the recommendation information in the personalized recommendation pool is activated and the product introduction information is re-listed.

4. The idle goods trading platform pricing method according to claim 3, characterized in that: The popularity is obtained through self-learning of the idle goods trading platform.

5. The idle goods trading platform pricing method according to claim 4, characterized in that: During the popularity statistics process, only one click is recorded for the same user browsing the same product of the same price.

6. The idle goods trading platform pricing method according to claim 5, characterized in that: During the heat statistics process, the clicks of users who are added to the untrustworthy user pool are regarded as invalid clicks; users in the untrustworthy user pool are defined as users who frequently click and browse the same product within a certain period of time.

7. The idle goods trading platform pricing method according to claim 6, characterized in that: The price of the product in the next cycle during the periodic price reduction process is: The product price of the next cycle = current product price * (1-1 / product popularity - seller urgency index) Among them, the popularity of the product refers to the number of clicks within the corresponding period after the product is launched, and the seller's urgency index is determined by the seller's waiting time. If the seller accepts the sale of the item within p days, the seller's urgency index is 1 / p.

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