Method and apparatus for determining time interval for changing ownership of article

Abstract

The application discloses a time interval determination method and device for changing the ownership of an article. One embodiment of the method comprises: determining a numerical interval of an ownership influence parameter of a target article according to sales data of the target article; determining a parameter update frequency corresponding to the numerical interval; determining an estimated time interval for executing an ownership change process of the target article, the estimated time interval corresponding to the numerical interval; in a process of updating the ownership influence parameter based on the numerical interval, the estimated time interval and the parameter update frequency, adjusting the estimated time interval according to current behavior data of a user associated with the target article to obtain an adjusted time interval, the ownership influence parameter showing a downward trend in the updating process; and updating the ownership influence parameter according to the numerical interval and the parameter update frequency within the adjusted time interval. The personalized parameter update strategy can improve the interactivity and activity of the user in the parameter updating process, and help to improve the efficiency of the ownership change of the target article.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to the field of e-commerce technology, and in particular to a method, apparatus, computer-readable medium, and electronic device for determining the time interval for changes in ownership of goods. Background Technology

[0002] A Dutch auction is a reverse bidding auction involving one seller and multiple buyers. Once a round of bidding begins, the price of the item gradually decreases from the starting price, and the price and duration of the entire price reduction process are simultaneously disclosed to all buyers. There are two conditions for the termination of an auction round: 1) The auction is "sold," meaning one buyer accepts the price and wins the item; 2) The auction "fails to sell," meaning the price is lowered to the reserve price and no one accepts the bid.

[0003] Existing online Dutch auction platforms, while capable of supporting the auction of a massive number of items, employ a consistent bidding strategy for all items, meaning that the price of items decreases uniformly throughout the entire auction process. Summary of the Invention

[0004] This application provides a method, apparatus, computer-readable medium, electronic device, and computer program product for determining the time interval for changes in ownership of articles.

[0005] In a first aspect, embodiments of this application provide a method for determining the time interval for ownership change of an item, comprising: determining a numerical range of ownership impact parameters of the target item based on sales data of the target item; determining the number of parameter updates corresponding to the numerical range; determining an estimated time interval for executing the ownership change process of the target item, wherein the estimated time interval corresponds to the numerical range; during the process of updating the ownership impact parameters based on the numerical range, the estimated time interval, and the number of parameter updates, adjusting the estimated time interval based on the current behavior data of users associated with the target item to obtain an adjusted time interval, wherein the ownership impact parameters show a decreasing trend during the update process; and updating the ownership impact parameters within the adjusted time interval based on the numerical range and the number of parameter updates.

[0006] In some examples, during the process of updating the ownership impact parameter based on a numerical range, an estimated time range, and the number of parameter updates, adjusting the estimated time range according to the user's current behavior data associated with the target item to obtain the adjusted time range includes: determining the update time corresponding to each parameter update operation within the estimated time range; determining the value corresponding to the parameter update operation within the numerical range; and adjusting the estimated time range according to the user's current behavior data during the process of updating the ownership impact parameter based on the update time and the value to obtain the adjusted time range.

[0007] In some examples, determining the update time corresponding to each parameter update operation within the estimated time interval for the number of parameter updates includes: determining the update time corresponding to the parameter update operation based on the estimated time interval, the number of parameter updates, and the constraint range, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the attribute-affecting parameter; and determining the value corresponding to the parameter update operation within the numerical range includes: determining the value corresponding to the parameter update operation based on the numerical range, the number of parameter updates, and the constraint range.

[0008] In some examples, the above-mentioned adjustment of the estimated time interval based on the user's current behavior data to obtain the adjusted time interval includes: adjusting the adjustment ratio for the initial time point based on the user's current behavior data, wherein multiple estimated time intervals are obtained by dividing the time range based on the initial time point, and multiple estimated time intervals correspond one-to-one with multiple numerical intervals; adjusting the initial time point according to the adjustment ratio to obtain the adjusted time point; and dividing the time range into multiple adjusted time intervals based on the adjusted time point.

[0009] In some examples, the above-mentioned adjustment ratio based on the user's current behavior data for the initial time point includes: normalizing the behavior data of multiple dimensions in the user's current behavior data to obtain multiple normalized behavior features; obtaining feature values ​​based on the multiple normalized behavior features; and determining the adjustment ratio based on the comparison results of the feature values ​​and preset values.

[0010] In some examples, determining the update time corresponding to the parameter update operation based on the estimated time interval, the number of parameter updates, and the constraint range includes: determining the sampling time corresponding to the parameter update operation based on uniformly distributed sampling within the constraint range; determining the proportion of the sampling time corresponding to the parameter update operation in the sum of the number of parameter updates and the sampling times corresponding to the parameter update operations; and determining the update time corresponding to the parameter update operation based on the proportion and the estimated time interval.

[0011] In some examples, the above-mentioned determination of the value corresponding to the parameter update operation based on the numerical range, the number of parameter updates, and the constraint range includes: determining the sampling time corresponding to the parameter update operation based on uniformly distributed sampling within the constraint range; determining the proportion of the sampling time corresponding to the parameter update operation in the sum of the number of parameter updates and the sampling times corresponding to the parameter update operations; and determining the value corresponding to the parameter update operation based on the proportion and the numerical range.

[0012] In some examples, the above-mentioned updating of the weight influence parameter within the adjusted time interval, based on the numerical range and the number of parameter updates, includes: determining the adjusted update time corresponding to the parameter update operation that was not executed within the estimated time interval, based on the adjusted time interval, the number of parameter updates, and the constraint range; for the parameter update operation that was not executed, updating the weight influence parameter according to the numerical value corresponding to the parameter update operation that was not executed at the adjusted update time.

[0013] In some examples, the aforementioned sales data is the correspondence data between the historical values ​​of the ownership impact parameter and the historical quantity of the target item after the ownership change, formed based on the user's ownership change behavior, and the determination of the numerical range of the ownership impact parameter of the target item based on the sales data of the target item, including: dividing the numerical range of the ownership impact parameter into multiple numerical ranges based on the correspondence data.

[0014] In some examples, the above method of dividing the numerical range of the ownership influence parameter into multiple numerical intervals based on the correspondence data includes: deleting some correspondence data related to abnormal historical values ​​from the correspondence data to obtain cleaned correspondence data; calculating the cumulative historical number of each historical value up to the ownership influence parameter based on the cleaned correspondence data; and dividing the numerical range into multiple numerical intervals based on the cumulative historical number.

[0015] In some examples, determining the number of parameter updates corresponding to the numerical range mentioned above includes: determining the range of update counts corresponding to the number of parameter updates based on the estimated time interval and the constraint range, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the attribute-affecting parameters; and determining the number of parameter updates based on uniformly distributed sampling within the update count range.

[0016] Secondly, embodiments of this application provide a device for determining the time interval for ownership change of an item, comprising: a numerical interval determination unit configured to determine a numerical interval of an ownership impact parameter of the target item based on sales data of the target item; a frequency determination unit configured to determine the number of parameter updates corresponding to the numerical interval; a time interval determination unit configured to determine an estimated time interval for executing the ownership change process of the target item, wherein the estimated time interval corresponds to the numerical interval; a time interval adjustment unit configured to adjust the estimated time interval based on current user behavior data associated with the target item during the process of updating the ownership impact parameter based on the numerical interval, the estimated time interval, and the number of parameter updates, to obtain an adjusted time interval, wherein the ownership impact parameter shows a decreasing trend during the update process; and a parameter update unit configured to update the ownership impact parameter based on the numerical interval and the number of parameter updates within the adjusted time interval.

[0017] In some examples, the aforementioned time interval adjustment unit is further configured to: determine the update time corresponding to each parameter update operation within the estimated time interval; determine the value corresponding to the parameter update operation within the numerical range; and, during the process of updating the ownership influence parameter based on the update time and the value, adjust the estimated time interval according to the user's current behavior data to obtain the adjusted time interval.

[0018] In some examples, the aforementioned time interval adjustment unit is further configured to: determine the update time corresponding to the parameter update operation based on the estimated time interval, the number of parameter updates, and the constraint range, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership-affecting parameter; and determine the value corresponding to the parameter update operation based on the numerical range, the number of parameter updates, and the constraint range.

[0019] In some examples, the aforementioned time interval adjustment unit is further configured to: adjust the adjustment ratio for the initial time point based on the user's current behavior data, wherein multiple estimated time intervals are obtained by dividing the time range based on the initial time point, and the multiple estimated time intervals correspond one-to-one with multiple numerical intervals; adjust the initial time point according to the adjustment ratio to obtain the adjusted time point; and divide the time range into multiple adjusted time intervals based on the adjusted time point.

[0020] In some examples, the aforementioned time interval adjustment unit is further configured to: normalize the behavioral data of multiple dimensions in the user's current behavioral data to obtain multiple normalized behavioral features; obtain feature values ​​based on the multiple normalized behavioral features; and determine the adjustment ratio based on the comparison results of the feature values ​​and preset values.

[0021] In some examples, the aforementioned time interval adjustment unit is further configured to: determine the sampling time corresponding to the parameter update operation based on uniformly distributed sampling within the constraint range; determine the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the parameter update operations in the number of parameter updates; and determine the update time corresponding to the parameter update operation based on the proportion and the estimated time interval.

[0022] In some examples, the aforementioned time interval adjustment unit is further configured to: determine the sampling time corresponding to the parameter update operation based on uniformly distributed sampling within the constraint range; determine the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the parameter update operations in the number of parameter updates; and determine the value corresponding to the parameter update operation based on the proportion and the value range.

[0023] In some examples, the parameter update unit is further configured to: determine the adjusted update time corresponding to the parameter update operation that was not executed within the estimated time interval based on the adjusted time interval, the number of parameter updates, and the constraint range; and update the weight influence parameter according to the value corresponding to the parameter update operation that was not executed at the adjusted update time.

[0024] In some examples, the aforementioned sales data is the correspondence data between the historical values ​​of the ownership impact parameter and the historical quantity of the target item after the ownership change, formed based on the user's ownership change behavior, and the aforementioned value interval determination unit is further configured to: divide the value range of the ownership impact parameter into multiple value intervals according to the correspondence data.

[0025] In some examples, the aforementioned numerical range determination unit is further configured to: delete part of the corresponding relationship data related to abnormal historical values ​​in the corresponding relationship data to obtain cleaned corresponding relationship data; based on the cleaned corresponding relationship data, count the cumulative historical number of each historical value up to the ownership influence parameter; and divide the numerical range into multiple numerical ranges based on the cumulative historical number.

[0026] In some examples, the aforementioned number determination unit is further configured to determine the update number range corresponding to the parameter update number based on the estimated time interval and the constraint range, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership-affecting parameter; and to determine the parameter update number based on uniformly distributed sampling within the update number range.

[0027] Thirdly, embodiments of this application provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, it implements the method as described in any implementation of the first aspect.

[0028] Fourthly, embodiments of this application provide an electronic device, including: one or more processors; and a storage device storing one or more programs thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect.

[0029] The method and apparatus for determining the time interval for ownership change of an item provided in this application embodiment determine the numerical range of the ownership impact parameter of the target item based on the sales data of the target item; determine the number of parameter updates corresponding to the numerical range; determine the estimated time interval for executing the ownership change process of the target item, wherein the estimated time interval corresponds to the numerical range; during the process of updating the ownership impact parameter based on the numerical range, the estimated time interval, and the number of parameter updates, the estimated time interval is adjusted according to the current user behavior data associated with the target item to obtain an adjusted time interval, and the ownership impact parameter shows a decreasing trend during the update process; within the adjusted time interval, the ownership impact parameter is updated according to the numerical range and the number of parameter updates, thereby providing a method for updating the ownership impact parameter of the target item by combining the sales data related to the target item and the current user behavior data. The personalized parameter update strategy can improve the interactivity and activity of users in the parameter update process, and help improve the efficiency of ownership change of the target item. Attached Figure Description

[0030] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0031] Figure 1 This is an exemplary system architecture diagram in which one embodiment of this application can be applied;

[0032] Figure 2 This is a flowchart of an embodiment of the method for determining the time interval for the change of ownership of articles according to this application;

[0033] Figure 3 This is a schematic diagram illustrating an application scenario of the method for determining the time interval for changes in ownership of goods according to this embodiment;

[0034] Figure 4 This is a flowchart of yet another embodiment of the method for determining the time interval for the change of ownership of articles according to this application;

[0035] Figure 5 This is a structural diagram of one embodiment of the device for determining the time interval of ownership change of articles according to this application;

[0036] Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application. Detailed Implementation

[0037] 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 invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0038] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in this disclosed technical solution all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0040] Figure 1 An exemplary architecture 100 for determining the time interval for changes in ownership of articles, which can be applied according to this application, is shown.

[0041] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. The communication connections between terminal devices 101, 102, and 103 form a network topology. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0042] Terminal devices 101, 102, and 103 can interact with server 105 via network 104 to receive or send data. Terminal devices 101, 102, and 103 can be hardware or software that supports network connectivity for data interaction and processing. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices that support network connectivity, information acquisition, interaction, display, and processing functions, including but not limited to smartphones, in-vehicle computers, tablets, e-book readers, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules, for example, to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0043] Server 105 can be a server that provides various services, such as a background processing server that updates the ownership impact parameters of the target item based on the sales data of the target item and the user's current behavior data provided by terminal devices 101, 102, and 103. As an example, server 105 can be a cloud server.

[0044] It should be noted that a server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (such as software programs or software modules used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0045] It should also be noted that the method for determining the time interval for changes in ownership of articles provided in the embodiments of this application is generally executed by a server, but the possibility of it being executed by a terminal device, or by the server and the terminal device cooperating with each other, is not excluded. Accordingly, the various parts (e.g., various units) included in the device for determining the time interval for changes in ownership of articles can all be set in the server, all in the terminal device, or separately in the server and the terminal device.

[0046] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Any number of terminal devices, networks, and servers can be included depending on implementation needs. When the electronic equipment running on the method for determining the time interval for changing the ownership of goods does not need to transmit data with other electronic equipment, the system architecture may only include the electronic equipment (e.g., servers or terminal devices) running on the method for determining the time interval for changing the ownership of goods.

[0047] Continue to refer to Figure 2 The flowchart 200 illustrates an embodiment of a method for determining the time interval for changes in ownership of goods, including the following steps:

[0048] Step 201: Determine the numerical range of the ownership influence parameters of the target item based on the sales data of the target item.

[0049] In this embodiment, the executing entity for the method of determining the time interval for the change of ownership of goods (e.g., Figure 1 The server can obtain sales data associated with the target item from a remote location or from a local location via a wired or wireless network connection, and determine the value range of the ownership influence parameters of the target item based on the sales data of the target item.

[0050] The ownership impact parameter of a target item refers to the parameter that the owner of the target item can influence the ownership of the target item during the process of allowing a change in ownership.

[0051] As an example, the process that allows for the transfer of ownership of a target item is the auction process for that item. Ownership-influencing parameters include, for example, the price, quantity, and volume of the target item. During the auction, once a bidder accepts the owner's bid for the target item, a process for transferring ownership of the target item can proceed, meaning the rights to the target item are transferred from the original owner to the bidder.

[0052] Sales data associated with a target item can be data generated due to user purchasing behavior during the historical ownership changes of the target item. For example, it could be data such as the user's historical transaction volume during the updating of ownership-related parameters of the target item.

[0053] In this embodiment, based on the sales data of the target item, the executing entity can analyze the numerical range of the ownership influence parameters of the target item that have a high transaction success rate. In this embodiment, the executing entity can determine one or more continuous data ranges.

[0054] In some implementations of this embodiment, the aforementioned sales data is the correspondence data between the historical values ​​of ownership impact parameters and the historical quantity of target items that have undergone ownership changes, formed based on the user's ownership change behavior.

[0055] Taking ownership-related factors as price parameters as an example, the aforementioned implementing entity can extract order data related to the target item over a historical period (e.g., within the past year), cleanse the final price and number of items sold for each order, and sum the number of items sold at the same final price to obtain the historical "price-sales" data of the target item, i.e., the commodity quantity-price relationship model, to characterize the correspondence between the historical values ​​of ownership-related factors and the historical quantity of the target item after ownership changes.

[0056] In this implementation, the aforementioned execution entity can perform step 201 as follows: based on the corresponding relationship data, divide the numerical range of the ownership influence parameter into multiple numerical intervals.

[0057] As an example, the aforementioned executing entity can determine the rate of change of historical quantities based on the corresponding relationship data, and then divide adjacent historical values ​​with similar rates of change into a numerical interval to obtain multiple numerical intervals.

[0058] In this implementation, multiple numerical ranges are determined based on the correspondence between the historical values ​​of the parameters representing the impact of ownership and the historical quantity of target items that have undergone ownership changes, which helps to improve the rationality of the numerical ranges.

[0059] In some optional implementations of this embodiment, the execution entity can determine multiple numerical ranges in the following ways:

[0060] First, delete the portion of the corresponding relationship data that is related to abnormal historical values, and obtain the cleaned corresponding relationship data.

[0061] As an example, the aforementioned executing entity can determine abnormal historical values ​​using the following formula. :

[0062]

[0063]

[0064]

[0065] in, Represents historical values.

[0066] Then, based on the cleaned corresponding data, the cumulative historical number of each historical value up to the ownership influence parameter is calculated.

[0067] As an example, the aforementioned implementing entity can determine the cumulative historical quantity of each historical value up to the ownership impact parameter using the following formula:

[0068]

[0069] in, Historical values The historical number sold at that time.

[0070] The percentage of cumulative historical values ​​corresponding to each historical value is determined using the following formula:

[0071]

[0072] Finally, based on the cumulative historical data, the numerical range is divided into multiple numerical intervals.

[0073] As an example, high sales prices ( ), low sales price ( The numerical range is divided into multiple numerical intervals. Continuing with the example where the price parameter is the parameter influencing ownership, the highest price in the price set where cumulative sales volume accounts for less than 60% is taken as the high-moving-volume price. The highest price in the price set representing less than 90% of cumulative sales volume is taken as the low-moving-volume price. ).

[0074]

[0075]

[0076] Among them, based on high sales price ( ), low sales price ( The logic for dividing the numerical range is as follows: 60% of the historical transaction volume is within the price range of "reservation price to high selling price", 30% of the historical transaction volume is within the price range of "high selling price to low selling price", and 10% of the historical transaction volume is within the price range of "low selling price to starting price".

[0077] This implementation provides a specific method for determining numerical ranges based on corresponding relationship data, which improves the rationality of numerical ranges and helps to enhance user interactivity and activity during parameter update.

[0078] Step 202: Determine the number of parameter updates corresponding to the numerical range.

[0079] In this embodiment, the aforementioned execution entity can determine the number of parameter updates corresponding to the numerical range.

[0080] Within the numerical range of the ownership impact parameter, the ownership impact parameter can be updated through multiple parameter update operations.

[0081] As an example, the aforementioned execution entity can use a random method to determine the number of parameter updates corresponding to the numerical range.

[0082] As another example, the aforementioned execution entity can determine a selectable range of parameter update counts based on the length of the numerical interval, and then, within this selectable range, randomly determine the corresponding parameter update count for the numerical interval. Here, the numerical length is the difference between the start and end values ​​of the numerical interval.

[0083] In some implementations of this embodiment, the execution entity can perform step 202 in the following manner:

[0084] First, determine the range of update counts corresponding to the number of parameter updates based on the estimated time interval and constraint range.

[0085] The constraint range is used to constrain the time interval between adjacent parameter update operations corresponding to the ownership influence parameter.

[0086] In this implementation, the aforementioned execution entity can determine the update frequency range using the following formula: ].

[0087]

[0088]

[0089] in, Indicates rounding up. This indicates rounding down. Indicates the range of constraints.

[0090] Second, the number of parameter updates is determined based on uniformly distributed sampling within the range of update counts.

[0091] As an example, the execution entity mentioned above can determine the number of parameter updates in the following way:

[0092]

[0093]

[0094] This implementation provides a specific method for determining the number of parameter updates, which improves the rationality and accuracy of the number of parameter updates and helps to further enhance user interaction and activity during the parameter update process.

[0095] Step 203: Determine the estimated time range for the execution of the ownership transfer process for the target item.

[0096] In this embodiment, the executing entity can determine the estimated time interval for the ownership transfer process of the target item. The estimated time interval corresponds to the numerical interval.

[0097] Within the estimated time interval, the ownership influence parameters of the target item can be updated within the numerical range. That is, each parameter update operation occurs within the estimated time interval, and the updated ownership influence parameter value is within the numerical range. When step 201 determines multiple numerical ranges, step 203 similarly determines multiple estimated time intervals that correspond one-to-one with the multiple numerical ranges.

[0098] As an example, the aforementioned implementing entity can analyze the sales data of the target item to determine the estimated time interval with a high success rate of the transaction.

[0099] As another example, the aforementioned executing entity can determine the estimated time interval that matches the numerical length based on the numerical length of the numerical interval.

[0100] Step 204: In the process of updating the ownership influence parameters based on the numerical range, the estimated time range, and the number of parameter updates, the estimated time range is adjusted according to the current user behavior data associated with the target item to obtain the adjusted time range.

[0101] In this embodiment, the aforementioned executing entity can adjust the estimated time interval based on the user's current behavior data associated with the target item during the process of updating the ownership influence parameter based on the numerical range, the estimated time interval, and the number of parameter updates, thus obtaining the adjusted time interval. The ownership influence parameter exhibits a decreasing trend during the update process.

[0102] In this embodiment, firstly, for each parameter update operation within the parameter update count, the aforementioned execution entity can randomly determine the updated value of the ownership influence parameter within a numerical range, and randomly determine the update time of the ownership influence parameter within an estimated time range. Then, during the process of updating the ownership influence parameter based on the determined updated value and update time, the aforementioned execution entity can collect current user behavior data associated with the target item, and adjust the estimated time range according to the current user behavior data to obtain the adjusted time range.

[0103] User behavior data refers to the behavioral data exhibited by users during the change of ownership impact parameters within the estimated time interval, including but not limited to the number of auction reservations, the number of real-time buyers watching, the number of buyer interactions (comments, likes, follows, reposts), and the average dwell time of buyers.

[0104] As an example, when the user's current behavior data indicates that the user is highly active and interested in the current explanation process of the target item, the estimated time interval can be appropriately increased to obtain the adjusted time interval.

[0105] The operation of obtaining the adjusted time interval based on the estimated time interval can be performed once or multiple times. In the case of multiple executions, the aforementioned executing entity can collect the user's current behavior data within a preset time interval at each interval, and adjust the estimated time interval based on the user's current behavior data to obtain the adjusted time interval.

[0106] In some optional implementations of this embodiment, the above-described execution principle can perform step 204 in the following manner:

[0107] The first step is to determine the number of parameter updates and the corresponding update time for each parameter update operation within the estimated time interval.

[0108] As an example, for each parameter update operation within the parameter update count, its corresponding update time is randomly determined between the update time corresponding to the previous parameter update operation and the end of the estimated time interval. For the first parameter update operation within the estimated time interval, its update time can be randomly determined within the estimated time interval.

[0109] As another example, the time interval between adjacent parameter update operations is consistent. Based on the estimated time interval length and the number of parameter updates, the time interval between adjacent parameter update operations is determined to determine the update time corresponding to each parameter update operation.

[0110] The second step is to determine the corresponding value within the numerical range for the parameter update operation.

[0111] As an example, for each parameter update operation within a specified number of parameter update operations, the corresponding value is randomly determined between the value corresponding to the previous parameter update operation and the end of the value interval. For the first parameter update operation within the estimated time interval, its value can be randomly determined within the value interval.

[0112] As another example, the numerical difference between adjacent parameter update operations is consistent. Based on the numerical range of the numerical interval and the number of parameter updates, the numerical difference between adjacent parameter update operations is determined to determine the numerical value corresponding to each parameter update operation.

[0113] The third step involves adjusting the estimated time interval based on the user's current behavior data during the process of updating the ownership impact parameters based on the update time and values, thus obtaining the adjusted time interval.

[0114] For each parameter update operation, the current time is the update time of this parameter update operation, and the weighting and affecting parameters are updated according to the value of this parameter update operation.

[0115] In this implementation, the aforementioned executing entity can refer to the determination method in step 204 above to determine the adjusted time interval, which will not be repeated here.

[0116] This implementation clarifies the update process of the ownership impact parameters and the determination process of the adjusted time interval, further improving the flexibility of the update process and the accuracy of the adjusted time interval.

[0117] In some optional implementations of this embodiment, the execution entity can perform the first step as follows: determine the update time corresponding to the parameter update operation within the estimated time interval based on the estimated time interval, the number of parameter updates, and the constraint range. The constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership-affecting parameters.

[0118] As an example, for each non-first parameter update operation within a parameter update operation, the aforementioned execution entity can, under the constraints of the constraint range, randomly determine the update time of the current parameter update operation based on the update time of the previous parameter update operation. Furthermore, it is necessary to ensure that the update times of all parameter update operations are within the estimated time interval.

[0119] The update time of the first parameter update operation within the estimated time interval can be determined based on the start time of the estimated time interval, under the constraints of the constraint range.

[0120] In this implementation, the execution entity can perform the second step as follows: determine the value corresponding to the parameter update operation based on the numerical range, the number of parameter updates, and the constraint range.

[0121] As an example, for the update time corresponding to the parameter update operation determined by the number of parameter updates and the constraint range, the value corresponding to the parameter update operation is randomly determined within the numerical range.

[0122] This embodiment provides a method for determining the value and update time corresponding to the parameter update operation, which improves the rationality of the parameter update operation and helps to further enhance user interaction and activity during the parameter update process.

[0123] In some optional implementations of this embodiment, the execution entity can perform the process of determining the update time in the following manner:

[0124] First, based on the uniformly distributed sampling within the constraints, the sampling time corresponding to the parameter update operation is determined.

[0125] As an example, the aforementioned execution entity can determine the sampling time corresponding to the parameter update operation using the following formula.

[0126]

[0127]

[0128] in, Indicates the range of constraints.

[0129] Then, determine the proportion of the sampling time corresponding to the parameter update operation in the total number of sampling times corresponding to the parameter update operations.

[0130] Finally, based on the ratio and the estimated time interval, the update time corresponding to the parameter update operation is determined.

[0131] As an example, the above-mentioned execution entity can determine the update time corresponding to the parameter update operation using the following formula:

[0132] =

[0133] in, Indicates the first The sampling time corresponding to the next parameter update operation. Indicates the number of times the parameter is updated ( The sum of sampling times corresponding to each parameter update operation. This indicates the estimated time interval.

[0134] This implementation provides a specific method for determining the update time corresponding to the parameter update operation. Based on uniform distribution sampling within the constraint range, it further improves the rationality of the determined update time and helps to further enhance the user's interactivity and activity during the parameter update process.

[0135] In some optional implementations of this embodiment, the execution entity can perform the determination process of the above-mentioned value in the following manner:

[0136] First, based on the uniformly distributed sampling within the constraints, the sampling time corresponding to the parameter update operation is determined.

[0137] As an example, the aforementioned execution entity can determine the sampling time corresponding to the parameter update operation using the following formula.

[0138]

[0139]

[0140] in, Indicates the range of constraints.

[0141] Next, determine the proportion of the sampling time corresponding to the parameter update operation within the total number of sampling times corresponding to the parameter update operations. Finally, based on the proportion and the numerical range, determine the numerical value corresponding to the parameter update operation.

[0142] As an example, the above-mentioned execution entity can determine the value corresponding to the parameter update operation using the following formula:

[0143] =

[0144] in, Indicates the first The sampling time corresponding to the next parameter update operation. Indicates the number of times the parameter is updated ( The sum of sampling times corresponding to each parameter update operation. Indicates the length of a numerical range.

[0145] This implementation provides a specific method for determining the value corresponding to the parameter update operation. Based on uniform distribution sampling within the constraint range, it further improves the rationality of the determined value, which helps to further enhance the user's interactivity and activity during the parameter update process.

[0146] In some optional implementations of this embodiment, the execution entity can perform the third step as follows:

[0147] First, based on the user's current behavior data, adjust the adjustment ratio for the initial time point.

[0148] Among them, multiple estimated time intervals are obtained by dividing the time range based on the initial time point, and multiple estimated time intervals correspond one-to-one with multiple numerical intervals.

[0149] The time frame is a predetermined timeframe within which the ownership transfer process of the target item can be carried out, such as the time frame specified by the owner (auctioneer) of the target item.

[0150] Taking a Dutch auction as an example, the total duration corresponding to the preset time range ( Based on this, and using real-time buyer user behavior data, the time points of high sales volume are calculated. Low sales price time point ( Finally, the auction start time will be added ( =0) and the auction end time ( = Therefore, each adjacent time point forms a key interval, and the four key time points divide the time range into three key time intervals: the popularity gathering interval (auction start time point → low sales time point, [ , ]), Adjusting emotional timeframes (low sales period → high sales period, [ , ]), Breakthrough range (high sales time point → auction end time point, [ , ).

[0151] As an example, the aforementioned executing entity can determine the initial time point using the following initial time point calculation model. The input to the initial time point calculation model is the total duration of the time range. The output is the initial value at the time point of high sales volume ( ), initial value of low sales price time point ( ).

[0152]

[0153] For example:

[0154] This results in the popular viewing segment accounting for 20% of the total duration, which is relatively short.

[0155] This results in the emotional adjustment period accounting for 60% of the total time (60% = 80% - 20%), which is relatively long; while the final breakthrough period accounts for 20% of the total time (20% = 1 - 80%), which is relatively short.

[0156] In the parameter update process of multiple estimated time intervals determined by the initial time point, when the user's current behavior data indicates that the user's activity level is high and they are interested in the explanation process of the target item, the length of the estimated time interval can be increased according to the preset adjustment ratio to obtain the adjusted time interval; when the user's current behavior data indicates that the user's activity level is low and they are not interested in the explanation process of the target item, the length of the estimated time interval can be decreased according to the preset adjustment ratio to obtain the adjusted time interval.

[0157] Then, the initial time point is adjusted according to the adjustment ratio to obtain the adjusted time point.

[0158] Finally, the time range is divided into multiple adjusted time intervals based on the adjusted time points.

[0159] Adjustment ratio as For example, the aforementioned executing entity can adjust the initial time point using the following formula:

[0160]

[0161]

[0162] Once the adjusted time point is obtained, the time range can be divided into multiple adjusted time intervals based on the adjusted time point.

[0163] In this implementation, the length of the time interval is adjusted based on the user's current behavior data during the update process of the ownership impact parameters. This improves the adaptability of the ownership change process with the user's real-time behavior data and helps to further enhance the user's interactivity and activity during the parameter update process.

[0164] In some optional implementations of this embodiment, the aforementioned execution entity can determine the adjustment ratio in the following manner:

[0165] First, the user's current behavioral data from multiple dimensions is normalized to obtain multiple normalized behavioral features. Then, feature values ​​are obtained based on these normalized behavioral features. Finally, the adjustment ratio is determined based on the comparison between the feature values ​​and preset values.

[0166] The preset values ​​can be set according to the actual situation, and no restrictions are imposed here.

[0167] As an example, firstly, the behavioral data across multiple dimensions is normalized using the following formula:

[0168]

[0169] Then, the eigenvalues ​​are obtained using the following formula:

[0170]

[0171] in, , , These are behavioral data from different dimensions. These are the weight parameters corresponding to behavioral data in different dimensions.

[0172] Finally, the adjustment ratio is determined using the following formula:

[0173]

[0174] This embodiment provides a specific implementation method for determining the adjustment ratio based on the user's current behavior data, which improves the accuracy and rationality of the adjustment ratio.

[0175] Step 205: Within the adjusted time interval, update the weighting influence parameters based on the numerical range and the number of parameter updates.

[0176] In this embodiment, the aforementioned executing entity can update the ownership impact parameters within the adjusted time interval based on the numerical range and the number of parameter updates.

[0177] As an example, for parameter update operations that have not yet been executed within the estimated time interval, the aforementioned executing entity can randomly determine the updated value of the ownership-influence parameter for this parameter update operation within the numerical range, and randomly determine the update time of the ownership-influence parameter for this parameter update operation within the adjusted time interval. For each parameter update operation, in response to reaching the update time of this parameter update operation, the value of the ownership-influence parameter is updated to the updated value corresponding to this parameter update operation.

[0178] In some optional implementations of this embodiment, the execution entity can perform step 205 as follows:

[0179] First, based on the adjusted time interval, the number of parameter updates, and the constraint range, determine the adjusted update time corresponding to the parameter update operations that were not executed within the estimated time interval. Then, for the parameter update operations that were not executed, update the weight influence parameters according to the values ​​corresponding to the parameter update operations that were not executed at the adjusted update time.

[0180] As an example, for each parameter update operation that is not executed within the estimated time interval, the adjusted update time corresponding to the parameter update operation can be determined by referring to the method for determining the update time described above. Specifically, based on uniformly distributed sampling within the constraint range, the sampling time corresponding to the parameter update operation is determined; the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the number of parameter updates is determined; and the adjusted update time corresponding to the parameter update operation is determined based on the proportion and the estimated time interval.

[0181] In this implementation, for parameter update operations that have not been executed within the estimated time interval, the ownership impact parameters are updated according to the values ​​corresponding to the current parameter update operation at the time of the adjusted update corresponding to the current parameter update operation. This provides users with a parameter update process that is more suitable for the current user and helps to further improve the efficiency of ownership change of target items.

[0182] See also Figure 3 , Figure 3 This is a schematic diagram 300 illustrating an application scenario of the method for determining the time interval for changes in ownership of goods according to this embodiment. Figure 3 In this application scenario, server 301 needs to retrieve sales data associated with target item 303 from database 302. After retrieving the sales data, it first determines the numerical range of the ownership impact parameter of the target item based on the sales data; then, it determines the number of parameter updates corresponding to the numerical range; then, it determines the estimated time range for executing the ownership change process of the target item, where the estimated time range corresponds to the numerical range; then, during the process of updating the ownership impact parameter based on the numerical range, the estimated time range, and the number of parameter updates, it collects the current behavior data of users associated with the target item in real time, and adjusts the estimated time range based on the current behavior data of users associated with the target item to obtain the adjusted time range; finally, within the adjusted time range, it updates the ownership impact parameter based on the numerical range and the number of parameter updates.

[0183] The method provided in the above embodiments of this application determines the numerical range of the ownership impact parameters of the target item based on the sales data of the target item; determines the number of parameter updates corresponding to the numerical range; determines the estimated time range for executing the ownership change process of the target item, wherein the estimated time range corresponds to the numerical range; during the process of updating the ownership impact parameters based on the numerical range, the estimated time range, and the number of parameter updates, the estimated time range is adjusted according to the current user behavior data associated with the target item to obtain the adjusted time range; within the adjusted time range, the ownership impact parameters are updated according to the numerical range and the number of parameter updates, thereby providing a method for updating the ownership impact parameters of the target item by combining the sales data related to the target item and the current user behavior data. The personalized parameter update strategy can improve the interactivity and activity of users in the parameter update process, and help improve the efficiency of ownership change of the target item.

[0184] Continue to refer to Figure 4 The illustration shows a schematic flow 400 of another embodiment of the method for determining the time interval for the change of ownership of articles according to this application, including the following steps:

[0185] Step 401: Delete the part of the corresponding relationship data that is related to the abnormal historical values ​​in the corresponding relationship data to obtain the cleaned corresponding relationship data.

[0186] Correspondence data characterizes the correspondence between the historical values ​​of the ownership influence parameters of the target item and the historical number of the target item after ownership changes.

[0187] Step 402: Based on the cleaned corresponding relationship data, calculate the cumulative historical number of each historical value up to the ownership influence parameter.

[0188] Step 403: Divide the numerical range into multiple numerical intervals based on the cumulative historical count.

[0189] Step 404: Based on the initial time point, the time range is divided to obtain multiple estimated time intervals for the ownership change process of the target item.

[0190] Among them, multiple estimated time intervals correspond one-to-one with multiple numerical intervals.

[0191] Step 405: Determine the range of update counts corresponding to the number of parameter updates based on the estimated time interval and constraint range.

[0192] The constraint range is used to constrain the time interval between adjacent parameter update operations corresponding to the ownership influence parameter.

[0193] Step 406: Determine the number of parameter updates based on uniformly distributed sampling within the range of update counts.

[0194] Step 407: Based on the uniformly distributed sampling within the constraint range, determine the sampling time corresponding to the parameter update operation.

[0195] Step 408: Determine the proportion of the sampling time corresponding to the parameter update operation in the total number of sampling times corresponding to the parameter update operations.

[0196] Step 409: Determine the update time corresponding to the parameter update operation based on the ratio and the estimated time interval.

[0197] Step 410: Determine the value corresponding to the parameter update operation based on the ratio and the numerical range.

[0198] Step 411: In response to the current time being the update time, update the ownership influence parameters according to the values.

[0199] Step 412: During the update process of the ownership impact parameters, obtain the user's current behavior data.

[0200] Step 413: Adjust the adjustment ratio for the initial time point based on the user's current behavior data.

[0201] Step 414: Adjust the initial time point according to the adjustment ratio to obtain the adjusted time point.

[0202] Step 415: Divide the time range into multiple adjusted time intervals based on the adjusted time points.

[0203] Step 416: Based on the adjusted time interval, the number of parameter updates, and the constraint range, determine the adjusted update time corresponding to the parameter update operation that was not executed within the estimated time interval.

[0204] Step 417: For parameter update operations that have not been executed, update the ownership influence parameters according to the values ​​corresponding to the parameter update operations that have not been executed at the post-adjustment update time.

[0205] As can be seen from this embodiment, with Figure 2 Compared with the corresponding embodiments, the process 400 of the method for determining the time interval for item ownership change in this embodiment specifically illustrates the process of determining the estimated time interval, the process of updating the number of parameter updates, the process of determining the adjusted time interval based on the user's current behavior data, and the process of determining the parameter update operation. The personalized parameter update strategy can improve the user's interactivity and activity in the parameter update process, which helps to improve the efficiency of ownership change of the target item.

[0206] Continue to refer to Figure 5 As an implementation of the methods shown in the above figures, this application provides an embodiment of a device for determining the time interval of an article's ownership change, which is similar to... Figure 2Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0207] like Figure 5 As shown, the device 500 for determining the time interval for ownership change of an item includes: a numerical interval determination unit 501, configured to determine the numerical interval of the ownership impact parameter of the target item based on the sales data of the target item; a frequency determination unit 502, configured to determine the number of parameter updates corresponding to the numerical interval; a time interval determination unit 503, configured to determine the estimated time interval for executing the ownership change process of the target item, wherein the estimated time interval corresponds to the numerical interval; a time interval adjustment unit 504, configured to adjust the estimated time interval based on the current behavior data of the user associated with the target item during the process of updating the ownership impact parameter based on the numerical interval, the estimated time interval, and the number of parameter updates, to obtain an adjusted time interval, wherein the ownership impact parameter shows a decreasing trend during the update process; and a parameter update unit 505, configured to update the ownership impact parameter based on the numerical interval and the number of parameter updates within the adjusted time interval.

[0208] In some implementations of this embodiment, the time interval adjustment unit 504 is further configured to: determine the update time corresponding to each parameter update operation within the estimated time interval; determine the value corresponding to the parameter update operation within the numerical range; and, during the process of updating the ownership influence parameter based on the update time and the value, adjust the estimated time interval according to the user's current behavior data to obtain the adjusted time interval.

[0209] In some implementations of this embodiment, the time interval adjustment unit 504 is further configured to: determine the update time corresponding to the parameter update operation based on the estimated time interval, the number of parameter updates, and the constraint range, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership-affecting parameter; and determine the value corresponding to the parameter update operation based on the value interval, the number of parameter updates, and the constraint range.

[0210] In some implementations of this embodiment, the time interval adjustment unit 504 is further configured to: adjust the adjustment ratio for the initial time point according to the user's current behavior data, wherein multiple estimated time intervals are obtained by dividing the time range based on the initial time point, and the multiple estimated time intervals correspond one-to-one with multiple numerical intervals; adjust the initial time point according to the adjustment ratio to obtain the adjusted time point; and divide the time range into multiple adjusted time intervals according to the adjusted time point.

[0211] In some implementations of this embodiment, the time interval adjustment unit 504 is further configured to: normalize the behavioral data of multiple dimensions in the user's current behavioral data to obtain multiple normalized behavioral features; obtain feature values ​​based on the multiple normalized behavioral features; and determine the adjustment ratio based on the comparison result between the feature values ​​and preset values.

[0212] In some implementations of this embodiment, the time interval adjustment unit 504 is further configured to: determine the sampling time corresponding to the parameter update operation based on the uniformly distributed sampling within the constraint range; determine the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the parameter update operations in the number of parameter updates; and determine the update time corresponding to the parameter update operation according to the proportion and the estimated time interval.

[0213] In some implementations of this embodiment, the time interval adjustment unit 504 is further configured to: determine the sampling time corresponding to the parameter update operation based on the uniformly distributed sampling within the constraint range; determine the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the parameter update operations in the number of parameter updates; and determine the value corresponding to the parameter update operation according to the proportion and the value range.

[0214] In some implementations of this embodiment, the parameter update unit 505 is further configured to: determine the adjusted update time corresponding to the parameter update operation that was not executed within the estimated time interval based on the adjusted time interval, the number of parameter updates, and the constraint range; and update the ownership influence parameter according to the value corresponding to the parameter update operation that was not executed at the adjusted update time for the parameter update operation that was not executed.

[0215] In some implementations of this embodiment, the sales data mentioned above is the correspondence data between the historical values ​​of the ownership influence parameter and the historical quantity of the target item after the ownership change, formed based on the user's ownership change behavior. The numerical range determination unit 501 mentioned above is further configured to divide the numerical range of the ownership influence parameter into multiple numerical ranges according to the correspondence data.

[0216] In some implementations of this embodiment, the numerical range determination unit 501 is further configured to: delete part of the corresponding relationship data related to abnormal historical values ​​in the corresponding relationship data to obtain cleaned corresponding relationship data; based on the cleaned corresponding relationship data, count the cumulative historical number of each historical value up to the ownership influence parameter; and divide the numerical range into multiple numerical ranges based on the cumulative historical number.

[0217] In some implementations of this embodiment, the above-mentioned number determination unit 502 is further configured to determine the update number range corresponding to the parameter update number based on the estimated time interval and the constraint range, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership influence parameter; and to determine the parameter update number based on uniformly distributed sampling within the update number range.

[0218] In this embodiment, the numerical range determination unit in the device for determining the time range of ownership change of an item determines the numerical range of the ownership impact parameter of the target item based on the sales data of the target item; the frequency determination unit determines the number of parameter updates corresponding to the numerical range; the time range determination unit determines the estimated time range for executing the ownership change process of the target item, wherein the estimated time range corresponds to the numerical range; the time range adjustment unit adjusts the estimated time range based on the current user behavior data associated with the target item during the process of updating the ownership impact parameter based on the numerical range, the estimated time range, and the number of parameter updates, to obtain the adjusted time range; the parameter update unit updates the ownership impact parameter based on the numerical range and the number of parameter updates within the adjusted time range, thereby providing a device for updating the ownership impact parameter of a target item by combining the user's historical behavior data and the user's current behavior data related to the target item. The personalized parameter update strategy can improve user interactivity and activity during the parameter update process, helping to improve the efficiency of ownership change of the target item.

[0219] The following is for reference. Figure 6 It illustrates a device suitable for implementing embodiments of this application (e.g., Figure 1 The diagram shows the structure of the computer system 600 of the devices 101, 102, 103, and 105. Figure 6 The device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0220] like Figure 6 As shown, the computer system 600 includes a processor (e.g., CPU, Central Processing Unit) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0221] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0222] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by processor 601, it performs the functions defined in the methods of this application.

[0223] It should be noted that the computer-readable medium of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0224] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the client computer, partially on the client computer, as a standalone software package, partially on the client computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the client computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0225] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0226] The units described in the embodiments of this application can be implemented in software or hardware. The described units can also be housed in a processor; for example, it can be described as: a processor including a numerical range determination unit, a count determination unit, a time range determination unit, a time range adjustment unit, and a parameter update unit. The names of these units do not necessarily limit the specific unit itself. For example, the time range adjustment unit can also be described as "a unit that, during the process of updating the ownership influence parameter based on the numerical range, the estimated time range, and the number of parameter updates, adjusts the estimated time range according to the user's current behavior data associated with the target item to obtain the adjusted time range."

[0227] In another aspect, this application also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the computer device to: determine the numerical range of the ownership impact parameter of the target item based on sales data of the target item; determine the number of parameter updates corresponding to the numerical range; determine the estimated time interval for executing the ownership change process of the target item, wherein the estimated time interval corresponds to the numerical range; during the process of updating the ownership impact parameter based on the numerical range, the estimated time interval, and the number of parameter updates, adjust the estimated time interval according to the current user behavior data associated with the target item to obtain an adjusted time interval, wherein the ownership impact parameter shows a decreasing trend during the update process; and within the adjusted time interval, update the ownership impact parameter according to the numerical range and the number of parameter updates.

[0228] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for determining the time interval for changes in ownership of goods, comprising: Based on the sales data of the target item, determine the numerical range of the ownership influence parameter of the target item; Determine the number of parameter updates corresponding to the given numerical range; Determine the estimated time interval for the ownership transfer process of the target item, wherein the estimated time interval corresponds to the numerical interval; During the process of updating the ownership influence parameter based on the numerical range, the estimated time range, and the number of parameter updates, at preset intervals, the estimated time range is adjusted according to the current user behavior data associated with the target item within the preset time range to obtain the adjusted time range, including: Determine the update time corresponding to each parameter update operation within the estimated time interval, including the number of parameter updates. Determine the value corresponding to the parameter update operation within the specified range; During the process of updating the ownership influence parameter based on the update time and the value, the adjustment ratio for the initial time point is adjusted according to the user's current behavior data. The multiple estimated time intervals are obtained by dividing the time range based on the initial time point, and the multiple estimated time intervals correspond one-to-one with the multiple value intervals. The initial time point is adjusted according to the adjustment ratio to obtain the adjusted time point. In response to the user's current behavior data indicating that the user is interested in the explanation process of the target item, the length of the estimated time interval is increased according to the adjustment ratio. In response to the user's current behavior data indicating that the user is not interested in the explanation process of the target item, the length of the estimated time interval is decreased according to the adjustment ratio. The time range is divided into multiple adjusted time intervals based on the adjusted time points; The ownership impact parameter shows a downward trend during the update process, and the current user behavior data refers to the user's behavior data during the change of the ownership impact parameter within the estimated time interval, including at least one of the following: number of users making reservations, number of real-time users watching, number of user interactions, and average user dwell time.

2. The method according to claim 1, wherein, Determining the update time corresponding to each parameter update operation within the estimated time interval for the number of parameter updates includes: Based on the estimated time interval, the number of parameter updates, and the constraint range, the update time corresponding to the parameter update operation is determined, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership influence parameter; and Determining the value corresponding to the parameter update operation within the numerical range includes: The numerical value corresponding to the parameter update operation is determined based on the numerical range, the number of parameter updates, and the constraint range.

3. The method according to claim 1, wherein, The step of adjusting the adjustment ratio for the initial time point based on the user's current behavior data includes: The user's current behavior data is normalized across multiple dimensions to obtain multiple normalized behavior features. Based on the multiple normalized behavioral features, feature values ​​are obtained; The adjustment ratio is determined based on the comparison between the feature value and the preset value.

4. The method according to claim 2, wherein, The step of determining the update time corresponding to the parameter update operation based on the estimated time interval, the number of parameter updates, and the constraint range includes: Based on the uniformly distributed sampling within the constraints, the sampling time corresponding to the parameter update operation is determined; Determine the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the number of parameter update operations; Based on the ratio and the estimated time interval, the update time corresponding to the parameter update operation is determined.

5. The method according to claim 2, wherein, Determining the value corresponding to the parameter update operation based on the numerical range, the number of parameter updates, and the constraint range includes: Based on the uniformly distributed sampling within the constraints, the sampling time corresponding to the parameter update operation is determined; Determine the proportion of the sampling time corresponding to the parameter update operation in the sum of the sampling times corresponding to the number of parameter update operations; Based on the ratio and the numerical range, the value corresponding to the parameter update operation is determined.

6. The method according to any one of claims 1-5, wherein, Also includes: Within the adjusted time interval, the ownership influence parameter is updated based on the numerical range and the number of parameter updates, including: Based on the adjusted time interval, the number of parameter updates, and the constraint range, determine the adjusted update time corresponding to the parameter update operation that was not executed within the estimated time interval; For any parameter update operations that were not executed, at the time of the post-adjustment update, the ownership impact parameter is updated according to the value corresponding to the parameter update operation that was not executed.

7. The method according to claim 1, wherein, The sales data is based on the historical values ​​of the ownership impact parameters and the historical quantities of the target items after ownership changes, formed by user ownership change behavior. The step of determining the numerical range of the ownership influence parameter of the target item based on the sales data of the target item includes: Based on the corresponding relationship data, the numerical range of the ownership influence parameter is divided into multiple numerical intervals.

8. The method according to claim 7, wherein, The step of dividing the numerical range of the ownership influence parameter into multiple numerical intervals based on the corresponding relationship data includes: Delete the portion of the correspondence data related to abnormal historical values ​​from the correspondence data to obtain the cleaned correspondence data; Based on the cleaned corresponding relationship data, the cumulative historical number of each historical value up to the ownership influence parameter is calculated; Based on the cumulative historical data, the numerical range is divided into multiple numerical intervals.

9. The method according to claim 1, wherein, Determining the number of parameter updates corresponding to the numerical range includes: Based on the estimated time interval and the constraint range, the update number range corresponding to the parameter update number is determined, wherein the constraint range is used to constrain the time interval length between adjacent parameter update operations corresponding to the ownership influence parameter; The number of parameter updates is determined based on uniformly distributed sampling within the range of the number of updates.

10. A device for determining the time interval for changes in ownership of articles, comprising: The numerical range determination unit is configured to determine the numerical range of the ownership influence parameter of the target item based on the sales data of the target item. The number determination unit is configured to determine the number of parameter updates corresponding to the numerical range; The time interval determination unit is configured to determine the estimated time interval for executing the ownership change process of the target item, wherein the estimated time interval corresponds to the numerical interval; The time interval adjustment unit is configured to, during the process of updating the ownership influence parameter based on the numerical interval, the estimated time interval, and the number of parameter updates, adjust the estimated time interval according to the current user behavior data associated with the target item to obtain the adjusted time interval, including: The update times corresponding to each parameter update operation within the estimated time interval are determined; the corresponding values ​​of the parameter update operations within the numerical range are determined; during the process of updating the ownership influence parameters based on the update times and the values, the adjustment ratio for the initial time point is adjusted according to the user's current behavior data, wherein multiple estimated time intervals are obtained by dividing the time range based on the initial time point, and multiple estimated time intervals correspond one-to-one with multiple numerical ranges; the initial time point is adjusted according to the adjustment ratio to obtain the adjusted time point, wherein, in response to the user's current behavior data indicating that the user is interested in the explanation process of the target item, the length of the estimated time interval is increased according to the adjustment ratio, and in response to the user's current behavior data indicating that the user is not interested in the explanation process of the target item, the length of the estimated time interval is decreased according to the adjustment ratio; the time range is divided into multiple adjusted time intervals according to the adjusted time point. The ownership impact parameter shows a downward trend during the update process, and the current user behavior data refers to the user's behavior data during the change of the ownership impact parameter within the estimated time interval, including at least one of the following: number of users making reservations, number of real-time users watching, number of user interactions, and average user dwell time.

11. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-9.

12. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-9.

13. A computer program product, comprising: A computer program that, when executed by a processor, implements the method according to any one of claims 1-9.

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