House resource flow regulation and control method and device

Abstract

The invention provides a housing resource flow regulation and control method and device, and the method comprises the steps: determining a reference housing resource flow effect according to the unit cost effect and the unit duration customer obtaining cost of a target housing resource, and enabling the target housing resource to be a published value-added housing resource which purchases a value-added product; the unit cost effect is determined based on the ratio of the unit time length housing resource flow effect corresponding to the value-added housing resource set in the target historical period to the housing resource unit time length average unit price; the unit time length housing resource flow effect is determined based on a housing resource average exposure effect, a housing resource average detail page browsing effect and a housing resource average effective connection effect of the value-added housing resource set in the unit time length; and in response to the condition that the relationship between the first house resource flow effect of the target house resource in the first time period and the reference house resource flow effect meets the supporting condition, exposure supporting and / or house resource flow pushing supporting are / is performed on the target house resource in the supporting period according to the determined first supporting parameter. According to the method, the housing resource flow can be dynamically adjusted in multiple dimensions, and the user satisfaction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and particularly relates to a house source flow regulation method and device. BACKGROUND

[0002] In a house source flow operation scheme, a house source lessor / seller increases house exposure by purchasing value-added products (a series of paid services launched by a platform to improve house exposure, attract potential customers and ensure transaction safety), so that the lessor / seller can obtain more user flow and realize the increase of customer acquisition. However, with the expansion of the scale of value-added products, more and more lessors / sellers choose to purchase value-added products, and the orders generated by value-added products gradually increase, and the number of house sources that need to be supported to increase exposure also increases accordingly.

[0003] At present, when allocating house source flow based on the purchase of value-added products, the following problems exist:

[0004] 1. Since the types of value-added products are various, the unit costs are different, and the exposure corresponding to the value-added product with high unit cost needs to be increased accordingly. However, when allocating house source flow, the exposure of house sources in the corresponding time range is usually ensured according to the corresponding time length of value-added products, and the differentiated flow allocation of house sources that purchase different value-added products based on unit cost is not considered.

[0005] 2. For different house sources that purchase the same value-added product, the flow allocation is not balanced, and the house source with less allocated flow is easy to cause the complaint of the lessor / seller.

[0006] 3. The heat of the house source that purchases value-added products is different according to the house source attributes such as house source category and house source area, and the regulation is easy to be unreasonable only from the single exposure dimension. SUMMARY

[0007] In view of the above problems, the embodiments of the present application provide a house source flow regulation method and device which overcome the above problems or at least partially solve the above problems.

[0008] In a first aspect, the embodiments of the present application provide a house source flow regulation method, comprising:

[0009] Based on the unit cost effectiveness and unit time customer acquisition cost of the target property, the benchmark property traffic effectiveness is determined. The target property is a value-added property that has been purchased and published. The unit cost effectiveness is determined based on the ratio of the unit time property traffic effectiveness of the value-added property set in the target historical period to the average unit price of the property in the unit time. The unit time property traffic effectiveness is determined based on the average exposure effect, average detail page view effect, and average effective link effect of the value-added property set in the unit time.

[0010] In response to the fact that the relationship between the first property traffic effect of the target property in the first time period and the benchmark property traffic effect meets the support conditions, the first support parameter corresponding to the target property in the support period is determined, where the first time period corresponds to the unit duration and the support period is the time period following the first time period;

[0011] Based on the first support parameters, the target property will receive exposure support and / or property promotion support during the support period to improve the property's traffic effect.

[0012] Secondly, embodiments of this application provide a housing flow control device, comprising:

[0013] The first determining module is used to determine the baseline listing traffic effect of the target listing based on the unit cost effect and the unit time customer acquisition cost of the target listing. The target listing is a value-added listing that has been purchased and published. The unit cost effect is determined based on the ratio of the unit time listing traffic effect of the value-added listing set in the target historical period to the average unit price of the listings in the unit time period. The unit time listing traffic effect is determined based on the average exposure effect, average detail page browsing effect, and average effective link effect of the value-added listing set in the unit time period.

[0014] The second determining module is used to determine the first support parameter corresponding to the target property within the support period in response to the relationship between the first property traffic effect and the benchmark property traffic effect in the first time period satisfying the support conditions. The first time period corresponds to the unit duration, and the support period is the time period following the first time period.

[0015] The processing module is used to provide exposure support and / or property promotion support to the target property during the support period according to the first support parameters, so as to improve the property traffic effect of the target property.

[0016] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the housing traffic control method described in the first aspect above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the housing traffic control method described in the first aspect above.

[0018] The technical solution of this application collects data on property exposure, property details page, and effective property links to determine the property traffic effect per unit time. It calculates the ratio of the property traffic effect per unit time to the average unit price per unit time, allowing for a comprehensive consideration of multi-dimensional information to determine the unit cost effect. Based on the unit cost effect and the unit time customer acquisition cost of the target property, it determines the benchmark property traffic effect, establishing a benchmark value specific to the target property for measuring its traffic effect. When the target property meets the support conditions based on the relationship between the first property traffic effect and the benchmark property traffic effect, it determines the first support parameter for the target property. Based on the first support parameter, it provides exposure support and / or property promotion support to the target property during the support period to improve its traffic effect. This allows for dynamic adjustment of property traffic across multiple dimensions, increasing the target property's access volume and improving the satisfaction of paying users with value-added products. Attached Figure Description

[0019] Figure 1 A schematic diagram illustrating the housing traffic control method provided in the embodiments of this application;

[0020] Figure 2 This is a schematic diagram illustrating the display of a property list on a client-side page provided in this embodiment of the application.

[0021] Figure 3 This is a diagram illustrating how the property details page of the client provided in this application embodiment displays the target property in the form of a pop-up window;

[0022] Figure 4 This is a schematic diagram illustrating how the server, based on information identification, determines properties with reduced ranking according to an embodiment of this application.

[0023] Figure 5 This is a schematic diagram of the housing traffic control device provided in the embodiments of this application;

[0024] Figure 6 This is a schematic diagram of the electronic device structure provided in the embodiments of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Multiple embodiments in this application may include two or more.

[0027] In the various embodiments of this application, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0028] This application provides a method for regulating housing supply flow, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step 101: Based on the unit cost effectiveness and unit time customer acquisition cost of the target property, determine the benchmark property traffic effectiveness. The target property is the value-added property that has been purchased and published. The unit cost effectiveness is determined by the ratio of the unit time property traffic effectiveness of the value-added property set in the target historical period to the average unit price of the property in the unit time. The unit time property traffic effectiveness is determined by the average exposure effect, average detail page view effect, and average effective link effect of the value-added property set in the unit time.

[0030] The property listing traffic control method provided in this embodiment is applied to a server, which is the backend platform of the target application. The target application is either a dedicated application supporting property rental and sales functions or a comprehensive application supporting multiple business functions (including property rental and sales functions). The target properties are value-added properties that have been purchased and published. Value-added properties refer to properties that have gained higher exposure, better display effects, and more protection measures by paying for value-added products provided by the platform. By launching value-added products, the platform can help real estate agents and landlords promote properties more effectively, while providing homebuyers and renters with a better and more reliable property search experience.

[0031] For example, value-added properties include shops, factories, and residences that have been upgraded with value-added products. By purchasing these value-added products, property owners and landlords can increase the exposure of their properties, thereby increasing the probability of renting or selling them.

[0032] The server obtains the unit cost-effectiveness and unit time customer acquisition cost for the target property. The unit cost-effectiveness represents how much property traffic can be obtained for every yuan, and the unit time customer acquisition cost is the cost of value-added products purchased for the target property per unit time.

[0033] The unit cost effectiveness is determined by the ratio of the unit-time traffic effect of the value-added property collection to the average unit price per unit time of the property within the target historical period. The value-added property collection includes multiple value-added properties, and the target property belongs to one of these multiple value-added properties. It can also be a newly published property that does not belong to the value-added property collection but is similar to the value-added properties in the collection. The target historical period is a pre-defined period, such as the past 30 days. The unit-time property traffic effectiveness is determined by the average property exposure, average property detail page views, and average effective connections of the value-added property collection within a unit time (e.g., 1 day). That is, data is collected to determine the unit-time property traffic effectiveness in terms of property exposure, property detail page views, and effective connections. The effective connections refer to the effective connections between users using the target application and property agents / landlords, as counted by the server. For example, effective connections include chat within the target application and call transactions established through the target application. By collecting data from the above three dimensions, we can obtain the effect of property traffic based on multi-dimensional data statistics, and then regulate the traffic, avoiding the need to regulate the effect of property traffic from only a single dimension.

[0034] Cost per unit of time (CPM) represents the cost incurred per unit of time for purchasing value-added products and services. When calculating CPM, the ratio of the selling price of the value-added products associated with the target property to the number of effective unit time periods supported by those products is used to determine the CPM for that property. The unit time period is a pre-set duration, such as 1 day, 12 hours, or 36 hours. For example, if the homeowner of the value-added property purchases a premium package for 2580 yuan, with a service period (valid days of the value-added product) of 92 days, then the daily amortized cost is 28.04 yuan / day (2580 / 92); or, if the homeowner of the value-added property purchases a premium package for 2580 yuan, and then purchases a 7-day package (costing 400 yuan) on the 80th day, then the daily amortized cost for the first 80 days is 28.04 yuan / day (2580 / 92), and the daily amortized cost for the last 19 days is (2580*(12 / 92)+400) / (12+7)=38.7 / day.

[0035] Since the unit cost effect represents how much property traffic effect can be obtained for every yuan, the unit time customer acquisition cost of the target property is the cost consumed by the value-added products purchased for the target property in a unit time. After obtaining the unit cost effect and the unit time customer acquisition cost corresponding to the target property, the benchmark property traffic effect corresponding to the target property in a unit time is determined based on the product of the unit cost effect and the unit time customer acquisition cost of the target property.

[0036] It should be noted that since the cost per unit of time (CST) represents the cost incurred per unit of time for purchasing value-added products and services for a target property, the CST per unit of time will differ for properties offering different value-added products. The CST per unit of time effectiveness is determined by the ratio of the CST per unit of time effectiveness to the average unit price per unit of time for the set of value-added properties during the target historical period. Both the CST per unit of time effectiveness and the average unit price per unit of time are related to multiple value-added properties in the set; therefore, multiple value-added properties in the set can share the CST per unit of time effectiveness. If the target property belongs to the set of value-added properties, the CST per unit of time effectiveness can be used directly. If the target property does not belong to the set of value-added properties but is related to properties in the set (e.g., it is a similar property to another value-added property), the CST per unit of time effectiveness can also be used. Since the baseline property traffic effectiveness is determined by the product of the CST per unit of time effectiveness and the CST per unit of time effectiveness, the baseline property traffic effectiveness will differ for different properties.

[0037] Step 102: In response to the fact that the relationship between the first property traffic effect and the benchmark property traffic effect in the first time period meets the support conditions, determine the first support parameter corresponding to the target property in the support period. The first time period corresponds to the unit duration, and the support period is the subsequent time period of the first time period.

[0038] The server obtains the traffic performance of the target property within a specific time period. The duration of the first time period is a unit of time, such as one day, and the first time period is a period following the target historical time period; for example, the first day after the target historical time period is the first time period in this embodiment. After obtaining the traffic performance of the target property within the first time period, the server compares this traffic performance with the baseline traffic performance of the target property within the unit of time. Based on the relationship between the first and baseline traffic performance, the server determines whether the target property meets the support conditions. If the target property meets the support conditions, the server determines the first support parameter for the target property within the support period, and provides traffic support to the target property based on the first support parameter within the support period.

[0039] The support period is the period following the first period. Preferably, the support period is adjacent to the first period, meaning it is continuous in time with the first period. The duration of the support period is a preset duration, such as 7 days, to provide traffic support to the target properties within the preset duration following the first period.

[0040] Step 103: Based on the first support parameter, provide exposure support and / or property promotion support to the target property during the support period to improve the property traffic effect.

[0041] After determining the primary support parameters for the target property, traffic support is provided to the target property during the support period based on these parameters to improve its traffic performance. This traffic support includes exposure support and / or property promotion support. Exposure support focuses on increasing the property's visibility and display frequency on the platform, allowing more users to see it. Promotion support focuses on pushing property information to more targeted users through precise recommendations, improving click-through rates and conversion rates. By employing exposure support and / or promotion support to support the target property's traffic, the effectiveness of its traffic performance during the support period can be guaranteed.

[0042] It is important to note that when providing traffic support to target properties during the support period, the support can be provided for the entire support period, or it can be provided only for a portion of the support period, depending on the effectiveness of the support.

[0043] The implementation process described in this application involves collecting data on property exposure, property details page, and effective property links to determine the property traffic effect per unit time. It calculates the ratio of the property traffic effect per unit time to the average unit price per unit time, allowing for a comprehensive consideration of multiple dimensions to determine the unit cost effect. Based on the unit cost effect and the unit time customer acquisition cost of the target property, a benchmark property traffic effect is determined, establishing a specific benchmark value for measuring the property traffic effect. When the target property meets the support conditions based on the relationship between the first property traffic effect and the benchmark property traffic effect, a first support parameter is determined for the target property. Based on this first support parameter, exposure support and / or property promotion support are provided to the target property during the support period to improve its property traffic effect. This allows for dynamic adjustment of property traffic across multiple dimensions, increasing the target property's access volume and improving the satisfaction of paying users with value-added products.

[0044] In an optional embodiment of this application, when providing traffic support to the target property, the method further includes:

[0045] If the traffic effect of the second property corresponding to the target property is greater than or equal to the first threshold at a certain moment in the support period, it is determined that the conditions for terminating support are met, and the first support parameter corresponding to the target property is adjusted to the default support parameter.

[0046] or

[0047] If the traffic effect of the second property corresponding to the target property is less than the first threshold at any time during the support period, it is determined that the conditions for terminating the support are not met. At the end of the support period, the first support parameter corresponding to the target property is adjusted to the default support parameter.

[0048] Among them, the traffic effect of the second property corresponding to the target property at any time during the support period is determined based on the property exposure, property details page views, and property effective links corresponding to the target property at that time; the first threshold is determined based on the traffic effect of the benchmark property and is greater than the traffic effect of the benchmark property; the first support parameter is greater than the default support parameter, and the support intensity is positively correlated with the support parameter.

[0049] The server calculates the traffic performance of the second listing for the target property within the support period. The server can perform traffic performance statistics at multiple preset statistical times. Within the support period, the target property corresponds to multiple second listing traffic performances at multiple statistical times. The second listing traffic performance of the target property is determined based on the property's exposure, property detail page views, and effective connections at the corresponding statistical time. After obtaining the property's exposure, property detail page views, and effective connections at a specific statistical time up to the support period, the server determines the second listing traffic performance at that statistical time based on the sum of the product of a first conversion coefficient and the effective connections, the product of a second conversion coefficient and the property detail page views, and the property exposure. The obtained property exposure, property detail page views, and effective connections use the start time of the support period as the start time and the statistical time as the end time.

[0050] When the server detects that the relationship between the traffic performance of the second property and the traffic performance of the benchmark property at a certain statistical moment during the support period meets the termination conditions, it stops providing traffic support to the target property based on the first support parameter and adjusts the first support parameter for the target property to the default support parameter. The default support parameter is, for example, the support parameter for non-value-added properties, and it is less than the first support parameter. For example, the default support parameter is 300, and the first support parameter is 500, which is determined based on the default support parameter.

[0051] If the traffic effect of the second property corresponding to the target property at a certain statistical moment is greater than or equal to the first threshold, the relationship between the traffic effect of the second property and the traffic effect of the benchmark property meets the termination support condition. The first threshold is a threshold determined based on the product of the traffic effect of the benchmark property and the first coefficient. The value of the first coefficient is greater than 1. For example, if the first coefficient is 1.2, when the traffic effect of the second property corresponding to the target property is greater than or equal to 1.2 times the traffic effect of the benchmark property, the property traffic support based on the first support parameter is terminated.

[0052] If the relationship between the traffic effect of the second property and the first threshold at any statistical moment during the support period does not meet the termination conditions for support, such as if the traffic effect of the second property at any statistical moment during the support period is less than the first threshold (1.2 times the traffic effect of the benchmark property), it indicates that the target property needs to continue to receive traffic support using the first support parameter during the support period. At the end of the support period, the strategy of using the first support parameter is terminated, and the first support parameter is adjusted to the default support parameter so that the default support strategy (such as the support strategy for non-value-added properties) can be restored after the support period ends.

[0053] In the above implementation process, while providing traffic support to the target property based on the first support parameter, the traffic effect of the corresponding second property within the support period is monitored in real time. When the traffic effect of the second property meets the termination conditions, the default support parameter is restored. This allows for flexible adjustment of the support status based on the actual support situation, avoiding uneven support caused by excessive support for some properties. If the traffic effect of the second property fails to reach the first threshold within the support period, traffic support for the target property continues with the first support parameter within the support period. After the support period ends, the default support parameter is restored, which can prevent the target property from dominating the client's search results.

[0054] The following describes the process of determining the effectiveness of property traffic per unit time and the average unit price per unit time. Since the effectiveness of property traffic per unit time is determined based on the average exposure, average detail page views, and average effective links of the value-added property collection within the unit time, this information needs to be obtained when determining the effectiveness of property traffic per unit time. Optionally, obtaining the average exposure, average detail page views, and average effective links of the value-added property collection within the unit time includes:

[0055] Obtain the total number of valid connections, total number of exposures, and total number of page views for the value-added property collection within the target historical period.

[0056] Based on the ratios of the total number of valid connections, total number of exposures, and total number of page views for each property listing to the unit duration corresponding to the target historical period and the number of value-added properties corresponding to the value-added property set, the average effective connection effect, average exposure effect, and average page view effect of the value-added property set within the unit duration are determined.

[0057] For a collection of value-added properties including multiple listings, the total number of valid connections, total property exposures, and total number of property detail page views are calculated within a target historical period. For example, for a collection of 100 value-added properties, the total number of valid connections, total property exposures, and total number of property detail page views are calculated over the past 30 days. The total number of valid connections includes the total number of valid connections for multiple value-added properties within the target historical period, and valid connections for a property include at least one of valid chats and valid calls. The total number of property exposures includes the total number of exposures for multiple value-added properties within the target historical period; if a property is displayed on a property listing page, it is considered to have been exposed. The total number of property detail page views includes the total number of property detail page views for multiple value-added properties within the target historical period; if a property detail page is opened, it is considered to have been viewed.

[0058] When calculating the total number of valid connections, total number of exposures, and total number of views on property details pages, ROI logs can be used to collect data and determine the required parameters. This method differs from traditional event tracking and ensures data validity.

[0059] After statistically analyzing the total number of valid connections, total number of exposures, and total number of page views for the value-added property collection within the target historical period, the following calculations are performed: For the total number of valid connections, the ratio is calculated to the number of units of time (e.g., days) corresponding to the target historical period and the number of value-added properties in the value-added property collection. This determines the average effective connection effect of the value-added property collection within a unit of time. For the total number of exposures, the ratio is calculated to the number of units of time corresponding to the target historical period and the number of value-added properties in the value-added property collection. This determines the average exposure effect of the value-added property collection within a unit of time. For the total number of page views, the ratio is calculated to the number of units of time corresponding to the target historical period and the number of value-added properties in the value-added property collection. This determines the average page view effect of the value-added property collection within a unit of time.

[0060] For example, if the unit duration is 1 day, and the number of days (unit duration) corresponding to the target historical period is 30, the average effective connection effect of a property listing = the total effective connections of the value-added property collection in the past 30 days / 30 / the number of value-added properties in the value-added property collection; the average exposure effect of a property listing = the total exposure of the value-added property collection in the past 30 days / 30 / the number of value-added properties in the value-added property collection; the average detail page view effect of a property listing = the total number of detail page views of the value-added property collection in the past 30 days / 30 / the number of value-added properties in the value-added property collection.

[0061] After determining the average exposure, average detail page views, and average effective links of the value-added property collection within a unit of time, the unit-time property traffic effect is determined based on these factors. In this embodiment, the unit-time property traffic effect is the traffic effect determined by combining the average detail page views and average effective links with the average exposure. Specifically, the unit-time property traffic effect is determined by summing the product of a first conversion coefficient and the average effective links, the product of a second conversion coefficient and the average detail page views, and the average exposure. The first conversion coefficient is used to convert the average effective links to the average exposure, and the second conversion coefficient is used to convert the average detail page views to the average exposure. These two conversion coefficients allow for effect conversion, enabling the determination of the unit-time property traffic effect based on the converted property exposure.

[0062] When determining the average unit price per unit time for a property, the amortization revenue of value-added products corresponding to the set of value-added properties in the target historical period is calculated. Based on the calculated amortization revenue of value-added products, the ratio of this amortization revenue to the number of unit times corresponding to the target historical period and the number of value-added properties corresponding to the set of value-added properties is calculated to determine the average unit price per unit time for the property. For example, if the target historical period corresponds to 30 days, the average unit price per unit time for the property = amortization revenue of value-added products corresponding to the set of value-added properties in the past 30 days / 30 / number of value-added properties.

[0063] It should be noted that the amortization revenue of value-added products over the target historical period is determined based on the value-added product revenue, the effective days of the value-added product, and the number of days the effective days occupy within the target historical period. For example, the amortization revenue over the target historical period is determined by the value-added product revenue / the effective days of the value-added product * (the number of days the effective days occupy within the target historical period). In special cases, the amortization cost over the target historical period is simply the cost of purchasing the value-added product. For example, if the effective days of the value-added product are 15 days, the amortization cost over the target historical period (e.g., 30 days) is the same as the cost of purchasing the value-added product; if the effective days of the value-added product are 45 days, but only 20 days are effective within the target historical period (nearly 30 days), then the amortization cost over the target historical period is the amortization cost for those 20 days.

[0064] Each property in the value-added property collection corresponds to one order during the target historical period. This order is for purchasing value-added products. For the case of purchasing multiple value-added products (such as multiple gold shop products) for a single property, they can be merged into one order through product upgrade. That is, in the above calculation method, there is a one-to-one correspondence between properties and orders.

[0065] Since the unit time property traffic effect represents the average traffic effect of multiple value-added properties within a unit time, after determining the unit time property traffic effect and the average unit price of properties within a unit time, the unit cost effect (representing the property traffic effect that can be obtained for each yuan) can be determined based on the ratio of the unit time property traffic effect to the average unit price of properties within a unit time. Then, the benchmark property traffic effect of the target property within a unit time can be determined by multiplying the unit cost effect by the unit time customer acquisition cost corresponding to the target property.

[0066] The above implementation process involves statistically analyzing the total number of effective connections, total exposures, and total page views of value-added housing listings within the target historical period. Based on the statistical data, the average effective connection effect, average exposure effect, and average page view effect of housing listings are determined. Then, based on data transformation, the unit-time housing traffic effect, which represents the average traffic effect of multiple value-added housing listings within a unit time period, is determined. Thus, the unit cost effect can be obtained by comparing the unit-time housing traffic effect with the average unit price of housing listings within a unit time period.

[0067] The process of determining the first support parameter is described below. When the relationship between the target property's traffic performance in the first time period and the benchmark property's traffic performance meets the support conditions, the first support parameter for the target property within the support period is determined, including:

[0068] If the traffic effect of the first property is less than the second threshold, the target property is determined to meet the support conditions. The second threshold is determined based on the traffic effect of the benchmark property and is less than the traffic effect of the benchmark property.

[0069] Based on the relationship between the traffic effect of the first property and the second threshold, the first support parameter corresponding to the target property is determined. The first support parameter is positively correlated with the difference between the second threshold and the traffic effect of the first property.

[0070] The traffic effect of the first listing corresponding to the target listing is determined based on the listing exposure, listing details page views, and number of valid links for the target listing in the first time period.

[0071] The server calculates the traffic performance of the target property within a specific time period. This performance is determined based on the property's exposure, detail page views, and effective connections within that time period. The exposure, detail page views, and effective connections are calculated as the total exposure, detail page views, and effective connections within a given time period. After obtaining these metrics, the server determines the traffic performance of the target property based on the product of a first conversion coefficient and the effective connections, the product of a second conversion coefficient and the detail page views, and the sum of the exposures.

[0072] After obtaining the traffic effect of the first property listing, it is compared with a second threshold. The second threshold is the product of the traffic effect of the benchmark property listing corresponding to the target property and a second coefficient, where the second coefficient is less than 1 (e.g., 0.9). If the traffic effect of the first property listing is less than the second threshold, the target property listing is deemed to meet the support conditions, and the first support parameter corresponding to the target property listing is determined. When determining the first support parameter, the relationship between the traffic effect of the first property listing and the second threshold is analyzed, and the matching first support parameter is determined based on the range of the difference between the second threshold and the traffic effect of the first property listing. Specifically, the difference between the second threshold and the traffic effect of the first property listing is positively correlated with the first support parameter; that is, the larger the difference, the larger the first support parameter, and the greater the corresponding support. This ensures that when the traffic effect of the first property listing is low, a greater support is provided to the target property listing to improve its traffic effect.

[0073] For example, when the difference between the second threshold and the effect of the first property traffic is in the first difference range, the first support parameter is 400; when the difference between the second threshold and the effect of the first property traffic is in the second difference range, the first support parameter is 500; when the difference between the second threshold and the effect of the first property traffic is in the third difference range, the first support parameter is 600, and the first difference range, the second difference range, and the third difference range increase sequentially.

[0074] The above implementation process involves statistically analyzing the traffic performance of the first property corresponding to the target property in the first time period. Then, the traffic performance of the first property is compared with a second threshold determined based on the benchmark property traffic performance and a second coefficient. When the comparison results indicate that the support conditions are met, a matching first support parameter is determined based on the difference range between the second threshold and the traffic performance of the first property. This enables the provision of support parameters that are suitable for the target property based on the relationship between the actual property traffic performance and the threshold, thereby providing traffic support to the target property with an appropriate level of support.

[0075] The following describes the process of providing traffic support to target properties based on the first support parameter. When providing exposure support and / or property promotion support to target properties during the support period according to the first support parameter, it includes:

[0076] The exposure level of the target property is determined based on the first support parameter, and the number of times the target property is displayed is increased based on the determined exposure level.

[0077] and / or

[0078] Based on the property attributes of the target property and the property needs of the users searching for a house, the target users whose property meets the requirements for matching the target property are identified. Furthermore, based on the support level corresponding to the first support parameter and the matching degree between the target property and the property needs of the target users, the recommended position of the target property is determined, and the target property is pushed to the target users' clients based on the recommended position.

[0079] When a server provides traffic support to a target property, it includes providing exposure support and / or pushing the property's traffic. Exposure support increases the number of times the property is shown, thus improving traffic effectiveness. Pushing the property's traffic allows for more precise targeting, further enhancing traffic performance.

[0080] For support measures focused on exposure, after determining the primary support parameter for the target property, the exposure level for the target property is determined based on this parameter. A higher primary support parameter results in a higher exposure level, which can be reflected in the number of exposures. After determining the exposure level for the target property, the number of times the target property is displayed is increased based on the corresponding number of exposures, thereby improving the property's traffic performance from an exposure perspective.

[0081] Regarding support for housing listings in terms of promotion, the approach considers both the property attributes of the target property and the housing needs of users searching for a home. Based on the compatibility between these two factors, the target users whose suitability for the target property is met are determined. Specifically, the compatibility between the property attributes of the target property and the housing needs of users can be seen as the matching between the target property's profile and the user's housing needs profile. The user's housing needs profile can represent preferred property attributes such as apartment type, size, lighting, and ventilation. By considering the matching between the target property's profile and the user's housing needs profile, suitable properties can be recommended to users, accurately pushing the target property to those with specific needs. The compatibility between the target property's attributes and the housing needs of users can also be seen as the matching between the target property and the properties users are interested in. Properties users are interested in can be understood as properties that match the user's housing needs. By considering the matching between the target property and the properties users are interested in, properties that match the user's interests can be recommended to users, accurately pushing the target property to those with specific needs. When considering the fit between target properties and the housing needs of users looking for housing, it is also possible to consider both the user's housing needs profile and the properties that the user is interested in.

[0082] Once the target users are identified based on the property attributes of the target property and the matching of the property needs of the users searching for a house, the recommended position of the target property is determined based on the support level corresponding to the first support parameter and the matching of the target property with the property needs of the target users. Based on the determined recommended position, the target property is pushed to the client of the target users, so as to accurately push the target property to the target users.

[0083] Specifically, when determining the recommended location of the target property based on the support level corresponding to the first support parameter, the matching between the target property and the target user's property needs, and pushing the target property to the target user's client based on the recommended location, the process includes:

[0084] Based on the level of support corresponding to the first support parameter and the match between the target property and the housing needs of the target users, the recommendation score of the target property is determined.

[0085] In the context of list push, the recommended position of the target property in the property recommendation list is determined based on the recommendation rating of the target property, and the property recommendation list is pushed to the target user's client.

[0086] In the scenario of direct property recommendation, the recommendation priority of the target property is determined based on the recommendation rating of the target property, and the target property is pushed to the target user's client in a pop-up manner based on the push priority.

[0087] When determining the recommendation score for a target property, a first score is determined based on the weight and support level of the first support parameter. A second score is determined based on the match between the target property and the target user's housing needs and the corresponding weight. The recommendation score is determined by the sum of the first and second scores. In other words, the recommendation score for a target property is related to two factors: the greater the support level of the first support parameter and the higher the match between the target property and the housing needs of the user, the higher the recommendation score. This ensures that properties with strong support and high match with user needs are prioritized for user awareness.

[0088] After determining the recommendation rating of the target property, the recommendation position of the target property is determined based on the recommendation rating. The higher the recommendation rating, the higher the corresponding recommendation position. The target property is then pushed to the target user's client according to the determined recommendation position, so as to provide traffic support to the target property through property push.

[0089] Different recommendation methods can be used to push property listings to the target user's client for different push scenarios. In the property list push scenario, the target property's recommendation position in the property recommendation list is determined based on its recommendation rating, and the generated property recommendation list is pushed to the target user's client. In the property direct push scenario, the target property's push priority is determined based on its recommendation rating, and the target property is pushed to the target user's client via a pop-up window based on the push priority, and the target property is displayed on the client's property details page.

[0090] In a property listing push scenario, the server determines the target property's recommended position in the list based on its recommendation rating; the higher the rating, the higher the property appears. After generating the property recommendation list including the target property, the list is pushed to the target user's client. The recommendation ratings of other properties in the list are determined using the same or similar method as the target property. Figure 2 The image shows a schematic diagram of a server-pushed property recommendation list displayed on a client-side application. Multiple properties in the list are arranged in descending order of their recommendation ratings. By pushing this property recommendation list to the target user's client (target application), the target user (a user browsing properties on the client-side) can learn about different properties based on their ratings. Since the recommendation rating is determined based on the property's support level and the match between the property and the user's needs, it can provide users with suitable properties while also ensuring sufficient support, guaranteeing that the target user can see properties that require support and match their needs.

[0091] In a direct property listing push scenario, the server pushes property listings to the target user's client's property details page via pop-ups. The push priority is determined by the property's recommendation rating; higher priority means earlier listings are pushed. After determining the target property's push priority based on its recommendation rating, the property is pushed to the target user's client via pop-ups based on this priority. For example, if there are four properties to be recommended, their push priorities are determined based on their corresponding recommendation ratings, and the properties are pushed sequentially to the client's property details page via pop-ups. Different properties to be recommended can correspond to different property details pages on the client's client. Figure 3 The image shows a diagram illustrating how the pushed property is displayed as a pop-up on the property details page of the client.

[0092] The above implementation process involves scoring properties based on the first support parameter, the degree of matching between the target property and user needs, determining the recommended position of the property based on the determined score, and pushing the property to the client. This property push can ensure traffic support for the property while taking into account user needs, and can make properties with strong support and high matching degree with user needs known to users first.

[0093] As an optional embodiment of this application, the method further includes:

[0094] Among multiple properties awaiting recommendation for purchasing value-added products, properties with reduced rankings are identified as those with false information. The support parameters corresponding to the reduced-ranked properties are adjusted to the second support parameter to reduce the support intensity for these properties. The second support parameter is less than the default support parameter.

[0095] In addition to providing traffic support for value-added properties, the server also needs to identify properties that will be demoted from the list of properties to be recommended for purchasing value-added products. Demoted properties are usually fake properties, such as those with fake prices, fake locations, or fake statuses. After identifying demoted properties, the support parameters for them are adjusted to the second support parameter, which is lower than the default support parameter for regular properties. By adjusting the support parameters, the support for demoted properties is reduced, thus lowering their traffic and directing high-quality traffic to other properties, indirectly increasing the traffic for value-added properties.

[0096] For example, the default support parameter is 300. After identifying a property that has been downgraded, the support parameter for that property can be adjusted to 100 or even 0. This reduces or eliminates support for downgraded properties and directs the support towards other reliable value-added properties. This approach can provide better properties to client users while cracking down on downgraded properties and protecting the legitimate rights and interests of property publishers who purchase value-added products.

[0097] When determining which properties will be demoted in search rankings, the server can collect data on user interactions with the properties, the identity information of the property publisher, verify the property's location, status, and price, in order to identify false information about the properties. For example, Figure 4 This diagram illustrates how a server identifies fake listings through information collection and verification. In this process, the server identifies the collected information, determines abnormal user interactions related to the listing (such as abnormal call content or chat content), identifies abnormal identities of the listing publisher, identifies abnormal behavior of the listing publisher, identifies listings that attract followers, etc. Based on the identified information, the server analyzes and identifies listings with corresponding fake information, and then determines listings that are downgraded in search ranking.

[0098] The above implementation process identifies downgraded properties and adjusts their support parameters to a second downgrade parameter to reduce the support for downgraded properties. This allows high-quality traffic to be directed towards reliable value-added properties, thereby improving the traffic effect of value-added properties.

[0099] In another optional embodiment of this application, the method further includes:

[0100] If, based on the details of regulation of multiple properties, it is determined that the first property needs to be re-supported, the support parameters of the first property will be adjusted to improve the property traffic effect of the first property.

[0101] If, based on the control details of multiple properties, it is determined that the category of a second property needs to be adjusted, a prompt message corresponding to the second property is generated and provided to the property publisher of the second property. The prompt message is used to indicate that the property category of the second property should be adjusted to the target category. The target category is the category that can improve the property traffic effect of the second property, which is determined based on the property attributes of the second property.

[0102] The server can store property management logs and task execution logs. The property management logs primarily record detailed property traffic management information, while the task execution logs mainly record task execution information and the number of properties managed. Based on the property management logs, the server determines the details of property management and generates a property management effect diagram. Based on the task execution logs, the server determines the execution status of property traffic management, and can also, upon receiving feedback from property publishers that the property support effect is unsatisfactory, determine the execution status of property traffic support based on the task execution logs and provide this information to the property publishers.

[0103] After analyzing the effect graphs of housing control measures on multiple housing listings and identifying the first listing requiring renewed support, the server adjusts the support parameters for that listing to improve its traffic performance, thus re-providing traffic support. When adjusting the support parameters for the first listing, the original parameters can be used, or the parameters can be redefined based on the actual support situation.

[0104] When the server analyzes the effect graph of property management adjustments representing multiple property listings and identifies a second property whose category needs adjustment, it generates a corresponding prompt message for the second property and provides it to the property listing publisher. The prompt message indicates that the property category of the second property should be adjusted to the target category. The target category is the category that the server analyzes based on the property attributes of the second property, which is believed to improve the property traffic effect. By providing the prompt message to the property listing publisher, the publisher is prompted to adjust the property category. Specifically, the server analyzes the property management effect chart to identify a second property with poor traffic performance that requires category adjustment. Based on the property attributes of the second property, the server analyzes its target category and generates a prompt message instructing the second property to adjust its category to the target category. This prompt message can be directly provided to the property publisher of the second property, who can then change the property category accordingly. Alternatively, after the server provides the prompt message to the property publisher and the publisher confirms the change, the server can modify the category of the second property based on the publisher's instructions.

[0105] The above implementation process involves adjusting the support parameters of the first property after identifying the one that needs renewed support, in order to provide traffic support to the first property again and improve its traffic effect. After identifying the second property that needs to have its category adjusted, the property attributes of the second property are analyzed to determine its target category, and a prompt message is generated instructing the second property to adjust its category to the target category. By modifying the property category, the page views of the second property can be increased, thereby improving the property traffic effect.

[0106] This application provides a housing flow control device, such as... Figure 5 As shown, it includes:

[0107] The first determining module 501 is used to determine the baseline listing traffic effect of the target listing based on the unit cost effect and the unit time customer acquisition cost of the target listing. The target listing is a value-added listing that has been published after purchasing value-added products. The unit cost effect is determined based on the ratio of the unit time listing traffic effect of the value-added listing set in the target historical period to the average unit price of the listings in the unit time period. The unit time listing traffic effect is determined based on the average exposure effect, average detail page browsing effect, and average effective connection effect of the value-added listing set in the unit time period.

[0108] The second determining module 502 is used to determine the first support parameter corresponding to the target property within the support period in response to the relationship between the first property traffic effect and the benchmark property traffic effect in the first time period satisfying the support conditions. The first time period corresponds to the unit duration, and the support period is the time period following the first time period.

[0109] The processing module 503 is used to provide exposure support and / or property promotion support to the target property during the support period according to the first support parameters, so as to improve the property traffic effect of the target property.

[0110] Optionally, the device further includes:

[0111] The first adjustment module is used to respond to the fact that the second housing traffic effect corresponding to the target housing at a certain moment in the support period is greater than or equal to the first threshold, determine that the termination support condition is met, and adjust the first support parameter corresponding to the target housing to the default support parameter.

[0112] or

[0113] The second adjustment module is used to respond to the fact that the second property traffic effect corresponding to the target property is less than the first threshold at any time during the support period, and to determine that the termination of support conditions are not met, and to adjust the first support parameter corresponding to the target property to the default support parameter at the end of the support period.

[0114] Wherein, the second property traffic effect corresponding to the target property at any time during the support period is determined based on the property exposure, property details page views, and property effective connections corresponding to the target property at that time; the first threshold is determined based on the benchmark property traffic effect and is greater than the benchmark property traffic effect; the first support parameter is greater than the default support parameter, and the support intensity is positively correlated with the support parameter.

[0115] Optionally, the device further includes:

[0116] The acquisition module is used to acquire the average exposure effect, average detail page browsing effect, and average effective connection effect of the value-added housing collection within a unit of time.

[0117] The third determining module is used to determine the unit time property traffic effect based on the product of the first conversion coefficient and the average effective connection effect of the property, the product of the second conversion coefficient and the average detail page browsing effect of the property, and the sum of the average exposure effect of the property.

[0118] The fourth determining module is used to determine the average unit price per unit time of the value-added housing set in the target historical period based on the ratio of the amortization revenue of the value-added products corresponding to the target historical period to the unit duration of the target historical period and the number of value-added housing sets corresponding to the value-added housing set.

[0119] Optionally, the acquisition module includes:

[0120] The acquisition submodule is used to acquire the total number of valid connections, total number of exposures, and total number of page views for the value-added housing resources collection during the target historical period.

[0121] The first determining submodule is used to determine the average effective connection effect, average exposure effect, and average detail page view effect of the value-added property set within a unit time period based on the ratios of the total effective connection of the property, the total exposure of the property, and the total number of page views of the property details page to the unit time period corresponding to the target historical period and the number of value-added properties corresponding to the value-added property set.

[0122] Optionally, the first determining module is further configured to:

[0123] The unit time customer acquisition cost of the target property is determined based on the selling price of the value-added products corresponding to the target property and the number of valid unit time periods supported by the value-added products.

[0124] The baseline traffic effect of the target property is determined by multiplying the unit cost effect and the unit time customer acquisition cost of the target property.

[0125] Optionally, the second determining module includes:

[0126] The second determining submodule is used to determine that the target property meets the support conditions in response to the first property's traffic effect being less than a second threshold, wherein the second threshold is determined based on the benchmark property's traffic effect and is less than the benchmark property's traffic effect.

[0127] The third determining submodule is used to determine the first support parameter corresponding to the target property based on the relationship between the first property traffic effect and the second threshold. The first support parameter is positively correlated with the difference between the second threshold and the first property traffic effect.

[0128] The traffic effect of the first property corresponding to the target property is determined based on the property exposure, property details page views, and property effective links in the first time period.

[0129] Optionally, the processing module includes:

[0130] The first processing submodule is used to determine the exposure intensity of the target property based on the first support parameter, and increase the number of times the target property is displayed based on the determined exposure intensity.

[0131] and / or

[0132] The second processing submodule is used to determine the target users whose suitability for the target property meets the requirements based on the property attributes of the target property and the property needs of the users looking for a house; and to determine the recommended location of the target property based on the support level corresponding to the first support parameter and the suitability of the target property with the property needs of the target users, and to push the target property to the client of the target users based on the recommended location.

[0133] Optionally, the second processing submodule includes:

[0134] The determining unit is used to determine the recommended rating of the target property based on the support level corresponding to the first support parameter and the matching between the target property and the property needs of the target user;

[0135] The first push unit is used to determine the recommended position of the target property in the property recommendation list based on the recommendation rating of the target property in the list push scenario, and push the property recommendation list to the target user's client.

[0136] The second push unit is used to determine the push priority of the target property based on the recommendation rating of the target property in the direct push scenario, and push the target property to the client of the target user in the form of a pop-up window based on the push priority.

[0137] Optionally, the device further includes:

[0138] The fourth adjustment module is used to adjust the support parameters of the first housing unit when the first housing unit that needs to be re-supported is determined based on the control details of multiple housing units, so as to improve the housing unit's housing traffic effect;

[0139] The generation and sending module is used to generate a prompt message for the second property and provide it to the property publisher of the second property when the second property needs to have its property category adjusted based on the control details of multiple properties. The prompt message is used to indicate that the property category of the second property should be adjusted to the target category. The target category is a category that can improve the property traffic effect of the second property based on the property attributes of the second property.

[0140] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0141] This application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described housing traffic control method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0142] For example, Figure 6 A schematic diagram of the physical structure of an electronic device is shown. (For example...) Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions stored in the memory 630. The processor 610 is used to execute various processes of the housing traffic control method according to the embodiments of this application, which will not be described in detail here.

[0143] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0144] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described housing traffic control method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0145] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0147] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0148] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0149] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0150] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0153] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for regulating housing supply flow, characterized in that, include: Based on the unit cost effectiveness and unit time customer acquisition cost of the target property, the benchmark property traffic effectiveness is determined. The target property is a value-added property that has been purchased and published. The unit cost effectiveness is determined based on the ratio of the unit time property traffic effectiveness of the value-added property set in the target historical period to the average unit price of the property in the unit time. The unit time property traffic effectiveness is determined based on the average exposure effect, average detail page view effect, and average effective link effect of the value-added property set in the unit time. In response to the fact that the relationship between the first property traffic effect of the target property in the first time period and the benchmark property traffic effect meets the support conditions, the first support parameter corresponding to the target property in the support period is determined, where the first time period corresponds to the unit duration and the support period is the time period following the first time period; Based on the first support parameters, the target property will receive exposure support and / or property promotion support during the support period to improve the property's traffic effect.

2. The method according to claim 1, characterized in that, The method further includes: In response to the second property traffic effect corresponding to the target property at a certain moment in the support period being greater than or equal to the first threshold, it is determined that the termination of support conditions are met, and the first support parameter corresponding to the target property is adjusted to the default support parameter; or If the traffic effect of the second property corresponding to the target property is less than the first threshold at any time during the support period, it is determined that the conditions for terminating the support are not met. At the time of termination of the support period, the first support parameter corresponding to the target property is adjusted to the default support parameter. Wherein, the second property traffic effect corresponding to the target property at any time during the support period is determined based on the property exposure, property details page views, and property effective connections corresponding to the target property at that time; the first threshold is determined based on the benchmark property traffic effect and is greater than the benchmark property traffic effect; the first support parameter is greater than the default support parameter, and the support intensity is positively correlated with the support parameter.

3. The method according to claim 1, characterized in that, The method further includes: Obtain the average exposure effect, average detail page browsing effect, and average effective link effect of the value-added housing collection within a unit of time. The property traffic effect per unit time is determined by summing the product of the first conversion coefficient and the average effective connection effect of the property, the product of the second conversion coefficient and the average detail page browsing effect of the property, and the average exposure effect of the property. The average unit price per unit time of the value-added housing collection in the target historical period is determined based on the ratio of the amortization revenue of the value-added products of the value-added housing collection in the target historical period to the unit time length of the target historical period and the number of value-added housing units in the value-added housing collection.

4. The method according to claim 3, characterized in that, The process of obtaining the average exposure effect, average detail page view effect, and average effective link effect of the value-added housing set within a unit of time includes: Obtain the total number of valid connections, total number of exposures, and total number of page views for the value-added housing collection during the target historical period; Based on the ratios of the total number of valid connections, the total number of exposures, and the total number of page views for each property listing to the unit duration corresponding to the target historical period and the number of value-added properties corresponding to the value-added property listing set, the average number of valid connections, the average number of exposures, and the average number of page views for each value-added property listing set within the unit duration are determined.

5. The method according to claim 1, 3, or 4, characterized in that, The step of determining the baseline listing traffic effect of the target listing based on the unit cost effectiveness and unit time customer acquisition cost of the target listing includes: The unit time customer acquisition cost of the target property is determined based on the selling price of the value-added products corresponding to the target property and the number of valid unit time periods supported by the value-added products. The baseline traffic effect of the target property is determined by multiplying the unit cost effect and the unit time customer acquisition cost of the target property.

6. The method according to claim 1, characterized in that, The first support parameter for the target property during the support period is determined based on the relationship between the first property traffic effect and the benchmark property traffic effect in the first time period, which satisfies the support conditions. This includes: In response to the first property's traffic effect being less than a second threshold, it is determined that the target property meets the support conditions, wherein the second threshold is determined based on the benchmark property's traffic effect and is less than the benchmark property's traffic effect; Based on the relationship between the traffic effect of the first property and the second threshold, a first support parameter corresponding to the target property is determined. The first support parameter is positively correlated with the difference between the second threshold and the traffic effect of the first property. The traffic effect of the first property corresponding to the target property is determined based on the property exposure, property details page views, and property effective links in the first time period.

7. The method according to claim 1, characterized in that, The step of providing exposure support and / or promotion support for the target property during the support period according to the first support parameter includes: The exposure level of the target property is determined based on the first support parameter, and the number of times the target property is displayed is increased based on the determined exposure level. and / or Based on the property attributes of the target property and the property needs of the users searching for a house, target users whose suitability for the target property meets the requirements are identified. Furthermore, based on the support level corresponding to the first support parameter and the suitability between the target property and the property needs of the target users, the recommended location of the target property is determined, and the target property is pushed to the client of the target user based on the recommended location.

8. The method according to claim 7, characterized in that, The step of determining the recommended location of the target property based on the support level corresponding to the first support parameter and the matching between the target property and the target user's property needs, and pushing the target property to the target user's client based on the recommended location, includes: Based on the level of support corresponding to the first support parameter and the matching between the target property and the property needs of the target user, the recommendation score of the target property is determined; In the context of list push, based on the recommendation rating of the target property, the recommended position of the target property in the property recommendation list is determined, and the property recommendation list is pushed to the target user's client. In the scenario of direct promotion of property listings, the push priority of the target property listing is determined based on the recommendation rating of the target property listing, and the target property listing is pushed to the target user's client in a pop-up manner based on the push priority.

9. The method according to claim 1, characterized in that, The method further includes: If, based on the details of regulation of multiple housing units, it is determined that the first housing unit needs to be re-supported, the support parameters of the first housing unit are adjusted to improve the housing unit's traffic effect. If, based on the control details of multiple properties, it is determined that the category of a second property needs to be adjusted, a prompt message corresponding to the second property is generated and provided to the property publisher of the second property. The prompt message is used to indicate that the property category of the second property should be adjusted to the target category. The target category is a category that can improve the property traffic effect of the second property, which is determined based on the property attributes of the second property.

10. A housing flow control device, characterized in that, include: The first determining module is used to determine the baseline listing traffic effect of the target listing based on the unit cost effect and the unit time customer acquisition cost of the target listing. The target listing is a value-added listing that has been purchased and published. The unit cost effect is determined based on the ratio of the unit time listing traffic effect of the value-added listing set in the target historical period to the average unit price of the listings in the unit time period. The unit time listing traffic effect is determined based on the average exposure effect, average detail page browsing effect, and average effective link effect of the value-added listing set in the unit time period. The second determining module is used to determine the first support parameter corresponding to the target property within the support period in response to the relationship between the first property traffic effect and the benchmark property traffic effect in the first time period satisfying the support conditions. The first time period corresponds to the unit duration, and the support period is the time period following the first time period. The processing module is used to provide exposure support and / or property promotion support to the target property during the support period according to the first support parameters, so as to improve the property traffic effect of the target property.

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