CN110264250B - Method and device for determining distributed data of peer-to-peer resource quantity of product in multiple regions - Google Patents
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Abstract
The embodiment of the specification provides a method and a device for determining distribution data of peer-to-peer resource quantity of products in a plurality of regions. The method comprises the following steps: acquiring data sets respectively corresponding to a plurality of regions to be released of a preset product; determining a conversion rate corresponding to the first region under each peer-to-peer resource amount based on a corresponding exchange probability of each potential user in the first data set corresponding to the first region under each peer-to-peer resource amount in the plurality of peer-to-peer resource amounts; constructing a broken line based on the conversion rates of the first region under a plurality of equivalent resource amounts respectively; determining an optimal slope from slopes between the coordinate point of the minimum peer-to-peer resource amount on the broken line and other peer-to-peer resource amount coordinate points, and taking a larger peer-to-peer resource amount corresponding to the optimal slope as a candidate peer-to-peer resource amount of the first region; determining an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts for the plurality of regions; and outputting candidate peer-to-peer resource amounts of a plurality of regions when the average peer-to-peer resource amount is larger than a preset threshold value.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of computer information processing, and in particular, to a method and apparatus for determining distribution data of peer-to-peer resource amounts of products in multiple regions.
Background
In many scenarios, product demanders may exchange certain products with some kind of resource, such as "energy" in an ant forest for tree seedlings, and money for merchandise in a merchandise sales. When selling commodities in a plurality of regions in combination with a commodity sales scenario, regional pricing is generally required, i.e. pricing strategies of different prices in different regions are implemented. Although commodity prices are different in different regions, the average price of the whole is required to meet certain constraint conditions. In general, the higher the commodity price in a certain region, the lower the probability of purchase by the user in that region, i.e., the lower the commodity conversion rate in that region. Therefore, pricing strategies require balancing commodity prices with conversion rates, which is sought to be highest when the overall average price is meeting constraints.
At present, the pricing strategy is formulated through manual experience, which consumes a great deal of manpower and material resources, and the efficacy of the formulated pricing strategy is poor due to factors such as subjectivity of people, limited market cognition level, experience deviation and the like. Therefore, a method for determining the distribution data of commodity prices in a plurality of regions by using a computer information processing technology is needed to improve the efficiency and effect of the pricing strategy and reduce the manpower and material resources consumed for formulating the pricing strategy.
Disclosure of Invention
One or more embodiments of the present disclosure describe a method and an apparatus for determining distribution data of product peer-to-peer resource amounts in multiple regions, which may determine product peer-to-peer resource amounts in each region based on a computer information processing technology, so that a higher product conversion rate may be achieved when an overall average peer-to-peer resource amount satisfies a constraint condition.
According to a first aspect, there is provided a method of determining distribution data of an amount of peer-to-peer resources of a product over a plurality of territories, comprising:
acquiring data sets respectively corresponding to a plurality of regions to be put in of a preset product, wherein the data sets comprise first data sets corresponding to a first region, the first data sets comprise user information of a plurality of potential users in the first region, and the user information of each potential user comprises exchange probability that each potential user exchanges with the preset product under a plurality of equivalent resource amounts;
determining the conversion rate corresponding to the first region under the first peer-to-peer resource amount based on the corresponding exchange probability of each potential user under the first peer-to-peer resource amount in the first data set; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts;
Constructing a first broken line based on the plurality of peer-to-peer resource amounts and the conversion rates of the first region corresponding to the plurality of peer-to-peer resource amounts respectively;
determining an optimal slope from slopes between the coordinate point of the minimum equivalent resource amount on the first fold line and other coordinate points of the larger equivalent resource amount, and taking the larger equivalent resource amount corresponding to the optimal slope as a candidate equivalent resource amount of the first region;
determining an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts for the plurality of regions;
and when the average peer-to-peer resource amount is larger than a preset threshold value, outputting the candidate peer-to-peer resource amounts of the regions as the distribution data.
In some embodiments, the exchange probability that each potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts is obtained by respectively inputting the user characteristics of the potential user and the peer-to-peer resource amounts into an exchange probability prediction model for prediction;
the exchange probability prediction model is obtained based on training historical behavior information of a plurality of sample users; the historical behavior information of the sample user comprises user characteristics, historical peer-to-peer resource quantity and exchange labels.
In some embodiments, the user characteristics include user attribute characteristics and user scene characteristics related to a scene in which the user uses the preset product.
In some embodiments, the abscissa of the first broken line corresponds to the plurality of peer resource amounts, the ordinate corresponds to the conversion rates of the first zone respectively corresponding to the plurality of peer resource amounts, and the optimal slope is the smallest slope among slopes between the smallest peer resource amount coordinate point and other larger peer resource amount coordinate points on the first broken line.
In some embodiments, an abscissa of the first broken line corresponds to a conversion rate of the first zone corresponding to the plurality of peer resource amounts, respectively, an ordinate corresponds to the plurality of peer resource amounts, and the optimal slope is a maximum slope among slopes between a minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first broken line.
In some embodiments, the determining an average amount of peer-to-peer resources based on the candidate amounts of peer-to-peer resources for the plurality of zones comprises:
an average peer-to-peer resource amount is determined based on the candidate peer-to-peer resource amounts for each of the plurality of regions and the population amounts for each of the regions.
In some embodiments, the determining an average amount of peer-to-peer resources based on the candidate amounts of peer-to-peer resources for the plurality of zones comprises:
an average amount of peer-to-peer resources is determined based on the amount of candidate peer-to-peer resources for each of the plurality of territories and the number of potential users.
In some embodiments, the method further comprises:
when the average peer-to-peer resource amount is smaller than or equal to the preset threshold value, determining a global optimal slope from a plurality of optimal slopes corresponding to the plurality of regions, taking the region corresponding to the global optimal slope as a region to be optimized, and taking a candidate peer-to-peer resource amount corresponding to the global optimal slope in the region as a peer-to-peer resource amount to be optimized;
on the broken line corresponding to the region to be optimized, determining a new optimal slope from the slope between the coordinate point of the peer-to-peer resource quantity to be optimized and the coordinate point of the peer-to-peer resource quantity higher than the peer-to-peer resource quantity to be optimized, and taking the larger peer-to-peer resource quantity corresponding to the new optimal slope as a new candidate peer-to-peer resource quantity of the region to be optimized;
updating the average peer-to-peer resource amount based on the new candidate peer-to-peer resource amount and the candidate peer-to-peer resource amount of the region outside the region to be optimized;
Repeating the steps until the updated average peer-to-peer resource amount is greater than the preset threshold.
According to a second aspect, there is provided an apparatus for determining distribution data of an amount of peer-to-peer resources of a product over a plurality of territories, comprising:
the acquisition unit is configured to acquire data sets respectively corresponding to a plurality of regions to be released of a preset product, wherein the data sets comprise first data sets corresponding to a first region, the first data sets comprise user information of a plurality of potential users in the first region, and the user information of each potential user comprises exchange probability that the potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts;
the first determining unit is configured to determine the conversion rate corresponding to the first region under the first peer-to-peer resource amount based on the corresponding exchange probability of each potential user in the first data set under the first peer-to-peer resource amount; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts;
a construction unit configured to construct a first polyline based on the plurality of peer-to-peer resource amounts and conversion rates of the first territory corresponding to the plurality of peer-to-peer resource amounts, respectively;
A second determining unit, configured to determine an optimal slope from slopes between the minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first folding line, and use a larger peer resource amount corresponding to the optimal slope as a candidate peer resource amount of the first region;
a third determining unit configured to determine an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts of the plurality of regions;
and the output unit is configured to output the distribution data of the candidate peer-to-peer resource amounts of the regions when the average peer-to-peer resource amount is larger than a preset threshold value.
According to a third aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of the first aspect.
According to a fourth aspect, there is provided a computing device comprising a memory having executable code stored therein and a processor which when executing the executable code implements the method of the first aspect.
According to the method and the device provided by the embodiment of the specification, the computing equipment can be used for distributing corresponding user information through a plurality of regions, determining candidate peer-to-peer resource amounts of each region under the conditions that the conversion rate is slow and the peer-to-peer resource amount is fast, and judging whether the average peer-to-peer resource amount of the candidate peer-to-peer resource amounts of each region meets constraint conditions or not by combining with a preset threshold value, so that the product peer-to-peer resource amounts of each region meeting the constraint conditions and having high conversion rate can be determined; moreover, the scheme provided by the embodiment of the description is a computer information processing scheme, so that the consumption of a large amount of manpower and material resources can be avoided, and the efficiency is high.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a flow chart of a method of determining distribution data of product peer-to-peer resource amounts across multiple zones, in accordance with some embodiments;
FIG. 2 illustrates a schematic diagram of predicting exchange probabilities, in accordance with some embodiments;
FIG. 3 illustrates a schematic diagram of candidate peer resource amounts, according to some embodiments;
FIG. 4 illustrates a flow diagram for optimizing average peer-to-peer resource amounts, according to some embodiments;
fig. 5 illustrates a schematic block diagram of an apparatus for determining distribution data of product peer-to-peer resource amounts across multiple zones, in accordance with some embodiments.
Detailed Description
The following describes the scheme provided in the present specification with reference to the drawings.
It will be readily appreciated that in the event of a non-gift or the like, the product demander needs or is willing to exchange a particular product with the product provider using its available resources of particular value. The resource of particular value may be referred to as a peer-to-peer resource of the product. The product may be a physical product, such as a food, a garment, etc.; the product may also be a virtual product, such as a point of play card, bus cycle card, or the like. The resources may be currency, labor time, items, virtual resources (e.g., "energy" in an ant forest), etc. It is readily understood that when the resource is in currency, the amount of peer-to-peer resource appears as the price of the product.
Generally, a product provider wants to deliver a product in a plurality of regions. In this case, the product provider may set different amounts of peer-to-peer resources for different areas where the product is to be delivered, in order to pursue a higher conversion rate of the product in each area.
Taking products as marketable commodities as an example, in a commodity sales scenario, commodities, such as clothing, food, electronic commodities, etc., may be sold in physical stores under physical lines in different regions. The commodity can be sold for users in different regions through an e-commerce platform (such as a payment bank and the like), such as a public transportation period card, a subway period card and the like.
Considering consumption habits, income levels, commodity types and other factors of different regions, different equivalent resource amounts, such as prices, can be set for different regions, so that the commodity has higher conversion rate in a plurality of regions.
In the embodiments of the present specification, the conversion rate refers to the ratio of the number of users using the existing resource exchange product to the number of reachable users of the product. In a commodity selling scene, the conversion rate is the ratio of the number of users purchasing the commodity to the number of reachable users of the commodity, and can be used for measuring the user acceptance of the commodity.
It is readily understood that the lower the amount of peer-to-peer resources, the higher the conversion will be. Considering cost and other factors, the overall average equivalent resource quantity of the product in different regions needs to meet certain constraint conditions.
Therefore, setting different amounts of peer-to-peer resources for different territories is a systematic and cumbersome project considering the above-mentioned various factors. In particular, in practical applications, dynamic pricing needs to be performed for each region, that is, the amount of peer resources in products of each region can be adjusted according to a specific pricing cycle (for example, 1 day, 1 week, 1 month, etc.), so as to pursue higher conversion rate. Dynamic pricing still further increases the effort of peer-to-peer resource volume setting.
For this reason, the embodiments of the present disclosure provide a method for determining distribution data of a product peer-to-peer resource amount in a plurality of regions based on a computer information processing technology. The computing device executes the method provided by the embodiment of the specification, and can determine the candidate peer-to-peer resource quantity of each region under the conditions that the conversion rate is slow and the peer-to-peer resource quantity is fast based on the user information corresponding to the distribution of a plurality of regions, and then judge whether the average peer-to-peer resource quantity of the candidate peer-to-peer resource quantity of each region meets the constraint condition by combining with the preset threshold value, so that the product peer-to-peer resource quantity of each region meeting the constraint condition and having higher conversion rate can be determined.
Next, referring to fig. 1, a method for determining distribution data of peer-to-peer resource amounts of products in multiple regions according to an embodiment of the present disclosure will be specifically described. The method may be performed by any apparatus, device, platform, cluster of devices having computing, processing capabilities. As shown in fig. 1, the method comprises the steps of: step 100, acquiring data sets respectively corresponding to a plurality of regions to be released of a preset product, wherein the data sets comprise first data sets corresponding to a first region, the first data sets comprise user information of a plurality of potential users in the first region, and the user information of each potential user comprises exchange probability that each potential user exchanges with the preset product respectively under a plurality of peer-to-peer resource amounts; step 102, determining a conversion rate corresponding to the first region under the first peer-to-peer resource amount based on the corresponding exchange probability of each potential user under the first peer-to-peer resource amount in the first data set; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts; 104, constructing a first broken line based on the plurality of peer-to-peer resource amounts and the conversion rates of the first region corresponding to the plurality of peer-to-peer resource amounts respectively; step 106, determining an optimal slope from slopes between the coordinate point of the minimum equivalent resource amount on the first folding line and other coordinate points of the larger equivalent resource amount, and taking the larger equivalent resource amount corresponding to the optimal slope as a candidate equivalent resource amount of the first region; step 108, determining an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts of the plurality of regions; and 110, outputting the candidate peer-to-peer resource amounts of the regions as the distribution data when the average peer-to-peer resource amount is larger than a preset threshold value.
Next, the steps described above are specifically described with reference to specific examples.
Firstly, in step 100, a data set corresponding to a plurality of regions to be delivered of a preset product is obtained, wherein the data set comprises a first data set corresponding to a first region, the first data set comprises user information of a plurality of potential users in the first region, and the user information of each potential user comprises exchange probability that each potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts.
Potential users refer to users who potentially have a need for a product. In the case where the product is a commodity, the resource is money, and the equivalent amount of the resource is the commodity price, the exchange probability may also be referred to as the purchase probability.
In some embodiments, the product may specifically be a public transportation cycle card sold through an e-commerce platform (e.g., payment treasures, etc.) or physical store, such as a public transportation cycle card, subway cycle card, etc.; the bicycle cycle card can also be a shared bicycle cycle card; etc., and are not listed here. Taking subway period cards as an example, the subway period cards can be divided into month cards, week cards and sub-cards. Specifically, taking a subway moon card as an example, after a user purchases the subway moon card, the user can enjoy a specific discount or preference by virtue of the subway moon card when riding the ground body within one month from the effective moment of the subway moon card.
In some embodiments, the territory may be an administrative area. In one example, the city or area of a certain administrative level may be specified, for example, a provincial city, province, autonomous region, etc. In another example, the territory may be a city, specifically, a city satisfying a specific condition, for example, a city whose population number exceeds a preset value (for example, 100 ten thousand), a city in which subways are built, or the like.
In some embodiments, the zone may be a geographic area. Taking the geographic area of China as an example, the regions can be North China, northeast China, northwest China, east China, south China and southwest China.
In some embodiments, the potential user may be a registered user of an e-commerce platform (e.g., a payment instrument). The potential user can be attributed to the corresponding region through the address information in the registration information of the potential user; the potential users can be attributed to the corresponding regions according to the offline transaction sites of the potential users; and the potential users can be attributed to the corresponding regions according to the receiving addresses of the online shopping.
In some embodiments, the potential users may be specifically residents of each territory.
In some embodiments, for any region of the multiple regions, the user characteristics of the potential users corresponding to the region and the peer-to-peer resource amounts may be input into the exchange probability prediction model to perform prediction, so as to obtain the exchange probability of the potential users under the peer-to-peer resource amounts.
The user characteristics may include attribute characteristics of the user and user scene characteristics. User attribute characteristics may include region, age, gender, educational level, occupation, and the like. The user scene feature is a feature related to a scene in which the user uses the product to be put, for example, in a case where the product to be put is a public transportation cycle card, the user scene feature may be a frequency in which the user is in a subway taking scene for a specific period of time (for example, one month), a frequency in which the user is in a public transportation taking scene, or the like.
For any potential user, each peer resource in the plurality of peer resource amounts can be spliced with the user attribute characteristics and the scene characteristics of the user to form a plurality of prediction samples, and the prediction samples are input into the exchange probability prediction model for prediction to obtain the exchange probability corresponding to each sample. In one example, referring to fig. 2, 4 peer-to-peer resource amounts may be set, which are respectively peer-to-peer resource amount 1, peer-to-peer resource amount 2, peer-to-peer resource amount 3, and peer-to-peer resource amount 4, and user attribute features and scene features of any user are spliced into 4 samples sequentially with peer-to-peer resource amount 1, peer-to-peer resource amount 2, peer-to-peer resource amount 3, and peer-to-peer resource amount 4, and then these 4 samples are input into the exchange probability prediction model, so that exchange probabilities corresponding to each of peer-to-peer resource amount 1, peer-to-peer resource amount 2, peer-to-peer resource amount 3, and peer-to-peer resource amount 4 can be obtained.
It should be noted that, in the embodiment of the present disclosure, the plurality of peer-to-peer resource amounts may be peer-to-peer resource amounts to be exchanged for products, and specifically, taking a commodity price as an example, the commodity price may be a price to be sold, for example, 10 yuan, 11 yuan, 12 yuan, and so on. In some cases, the peer-to-peer resource amount may be represented as the original peer-to-peer resource amount multiplied by a particular discount, including, for example, 6-fold, 6.5-fold, 7-fold, 7.5-fold, and so on.
Information about whether a plurality of sample users exchanged products at each of a plurality of historical peer-to-peer resource amounts may be collected, and may specifically be represented by exchange tags. And splicing the exchange labels, the user characteristics and the historical peer-to-peer resource quantities corresponding to the historical peer-to-peer resource quantities of the samples into training samples, and performing model training to obtain an exchange probability prediction model. The exchange probability prediction model may be implemented by a variety of model structures, such as a deep neural network (deep neural network, DNN) model, a GBDT model, and so forth.
In some embodiments, the number of times a sample user browses a product and the number of times a product is swapped under each peer resource amount may be counted to obtain a probability of swapping of the sample user under each peer resource amount. Then, based on the user characteristics, clustering the sample users and the potential users; and taking the exchange probability of the sample user under each peer-to-peer resource amount as the exchange probability of the user in the same class cluster with the sample user under each peer-to-peer resource amount. The scheme of clustering users based on the user features may refer to the description of the prior art, and will not be described herein.
In the above embodiments, when the potential user is a registered user of the e-commerce platform, the user characteristics of the potential user may be, but are not limited to, obtained through registration information, social records, online consumption records, offline consumption records, and the like. When the potential user is a resident of the corresponding territory, the user characteristics of the potential user may be, but are not limited to, obtained through feedback information of an entity store of the territory, local resident information, and the like.
After the exchange probability of each potential user is obtained, the potential users can be classified based on the region where each potential user is located, so that a data set corresponding to each region is obtained.
Next, in step 102, based on the exchange probability corresponding to each potential user in the first data set under the first peer-to-peer resource amount, determining a conversion rate corresponding to the first region under the first peer-to-peer resource amount; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts.
As described above, conversion refers to the ratio of the number of users exchanging products using existing resources to the number of reachable users of the products. Therefore, with respect to the first peer-to-peer resource amount corresponding to the first zone, the conversion rate may be calculated based on the exchange probability corresponding to each potential user of the first zone under the first peer-to-peer resource amount. Specifically, an average value of the exchange probabilities corresponding to each potential user in the first region under the first peer-to-peer resource amount is used as the conversion rate corresponding to the first region under the first peer-to-peer resource amount.
By referring to the mode, the conversion rate of each region corresponding to each equivalent resource amount can be obtained.
Next, in step 104, a first polyline is constructed based on the plurality of peer-to-peer resource amounts and the conversion rates of the first zone corresponding to the plurality of peer-to-peer resource amounts, respectively.
In some embodiments, for any region, each peer resource amount in the plurality of peer resource amounts may be taken as an abscissa, and a conversion rate corresponding to the region under each peer resource amount may be taken as an ordinate, so as to construct a polyline corresponding to the region.
In other embodiments, the corresponding broken line may be constructed by taking the conversion rate of the region corresponding to each of the plurality of peer resource amounts as an abscissa and each of the plurality of peer resource amounts as an abscissa.
In some embodiments, the polylines corresponding to the regions are polylines on different polylines, that is, the polylines corresponding to the regions are constructed according to the peer-to-peer resource amounts and the conversion rates corresponding to the regions under the peer-to-peer resource amounts. In these implementations, each region corresponds to a line graph, with only one line graph on each line graph.
In some embodiments, the polylines corresponding to the regions may be respectively constructed on the same polyline. Namely, each region in the plurality of regions corresponds to different folding lines on the same folding line diagram.
And in step 106, determining an optimal slope from the slopes between the minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first folding line, and taking the larger peer resource amount corresponding to the optimal slope as the candidate peer resource amount of the first region.
And for any region, calculating the slope between the coordinate point of the minimum equivalent resource amount and the coordinate points of other larger equivalent resource amounts in the plurality of equivalent resource amounts in sequence based on the broken line corresponding to the region, then determining the optimal slope from the calculated plurality of slopes, and taking the larger equivalent resource amount corresponding to the optimal slope as the candidate equivalent resource amount of the region.
In some embodiments, the abscissa of the polyline may correspond to a plurality of peer resource amounts and the ordinate may correspond to a conversion rate, and then the smallest slope of the calculated plurality of slopes may be taken as the optimal slope. In one example, referring to fig. 3, the conversion rate of a zone and the corresponding relationship record of the respective peer resource amounts for each behavior. Taking the first line corresponding to the first region as an example, the line graph in fig. 3 is a line graph corresponding to the first region. Based on the line diagram in fig. 3, the slopes between R11 and R12, R11 and R13, R11 and R14, R11 and R15, and R11 and R16 are sequentially calculated, and if the slope between R11 and R14 is the smallest among the obtained slopes, the peer-to-peer resource amount corresponding to R14 may be used as the candidate peer-to-peer resource amount in the first region.
In this embodiment, when the peer-to-peer resource amount is the larger peer-to-peer resource amount corresponding to the minimum slope, the conversion rate decreases the slowest and the peer-to-peer resource amount increases the fastest.
In some embodiments, the abscissa of the polyline may correspond to the conversion rate, the ordinate may correspond to the amount of peer resources, and the largest slope of the calculated plurality of slopes may be taken as the optimal slope. In this embodiment, when the peer-to-peer resource amount is the larger peer-to-peer resource amount corresponding to the maximum slope, the peer-to-peer resource amount increases fastest and the conversion rate decreases slowest.
Then, in step 108, an average amount of peer-to-peer resources is determined based on the candidate amounts of peer-to-peer resources for the plurality of zones.
In some embodiments, a simple average of candidate peer-to-peer resource amounts for multiple regions may be calculated, resulting in an average peer-to-peer resource amount
I.e. the average peer-to-peer resource amount can be calculated by the following formula:
wherein n represents the number of regions, P i Representing the amount of candidate peer resources for the i-th zone.
In some embodiments, an average amount of peer-to-peer resources may be determined based on the candidate amounts of peer-to-peer resources for each of the plurality of regions and the population amounts for each of the regions 
In one example, average peer-to-peer resource amount +.>
Can be obtained by the followingThe formula is calculated:
wherein n represents the number of regions, P i Representing the candidate peer-to-peer resource quantity, a, for the ith zone i Representing the population number of the i-th territory.
In some embodiments, an average amount of peer-to-peer resources may be determined based on the amounts of candidate peer-to-peer resources for each of the plurality of regions and the number of potential users
In one example, average peer-to-peer resource amount +.>
The method can be calculated by the following formula:
wherein n represents the number of regions, P i Representing the candidate peer-to-peer resource quantity, a, for the ith zone i ' indicates the number of potential users in the ith zone.
Then, in step 110, when the average peer-to-peer resource amount is greater than a preset threshold, the candidate peer-to-peer resource amounts of the plurality of regions are output as distribution data.
The preset threshold may be determined based on constraints (e.g., cost of product, etc.). When the average peer-to-peer resource amount is obtained, it may be determined whether the average peer-to-peer resource amount is greater than a preset threshold. If the average peer-to-peer resource amount is greater than the preset threshold value, the average peer-to-peer resource amount is indicated to meet the constraint condition. The candidate peer-to-peer resource amount of each region can be output to serve as the peer-to-peer resource amount of the preset product in each region.
In some embodiments, the method provided in the embodiments of the present specification further includes: step 112, optimizing the average peer-to-peer resource amount when the average peer-to-peer resource amount is less than or equal to a preset threshold. Referring to fig. 4, step 112 specifically includes the sub-steps of:
and 1120, determining a global optimal slope from a plurality of optimal slopes corresponding to the plurality of regions, taking the region corresponding to the global optimal slope as a region to be optimized, and taking the candidate peer-to-peer resource quantity corresponding to the global optimal slope in the region as the peer-to-peer resource quantity to be optimized.
When the average peer-to-peer resource amount is not greater than the preset threshold value, the average peer-to-peer resource amount is not satisfied with the constraint condition, and the average peer-to-peer resource amount needs to be improved. At this time, the candidate peer-to-peer resource quantity of the region corresponding to the global optimal slope is used as the peer-to-peer resource quantity to be optimized, and optimization is performed preferentially, so that the average peer-to-peer resource quantity is improved, and the overall average conversion rate is reduced more slowly.
And 1122, determining a new optimal slope from the slopes between the coordinate points of the peer-to-peer resource amount to be optimized and the coordinate points of the peer-to-peer resource amount higher than the peer-to-peer resource amount to be optimized on the broken line corresponding to the region to be optimized, and taking the larger peer-to-peer resource amount corresponding to the new optimal slope as a new candidate peer-to-peer resource amount of the region to be optimized.
Reference is made specifically to the description of step 106 hereinabove, and no further description is given here.
Reference is made specifically to the description of step 108 hereinabove, and no further description is given here.
And when the updated average peer-to-peer resource amount is still smaller than or equal to the preset threshold, repeating the steps 1120-1124 until the updated average peer-to-peer resource amount is larger than the preset threshold.
And when the updated average peer-to-peer resource amount is greater than the preset threshold, executing step 110, and outputting the candidate peer-to-peer resource amounts of the regions as the distribution data.
According to the scheme provided by the embodiment of the specification, the computing equipment can be used for determining the candidate peer-to-peer resource quantity of each region under the conditions that the conversion rate is slow and the peer-to-peer resource quantity is fast through the user information corresponding to the regions respectively, and judging whether the average peer-to-peer resource quantity of the candidate peer-to-peer resource quantity of each region meets the constraint condition or not by combining the preset threshold value, so that the product peer-to-peer resource quantity of each region meeting the constraint condition and having high conversion rate can be determined; moreover, the scheme provided by the embodiment of the description is a computer information processing scheme, so that the consumption of a large amount of manpower and material resources can be avoided, and the efficiency is high.
In another aspect, embodiments of the present disclosure provide an apparatus 500 for determining distribution data of an amount of peer-to-peer resources of a product over a plurality of regions. Referring to fig. 5, an apparatus 500 includes:
the acquiring unit 510 is configured to acquire a data set corresponding to a plurality of regions to be released of a preset product, where the data set includes a first data set corresponding to a first region, where the first data set includes user information of a plurality of potential users in the first region, and the user information of each potential user includes exchange probabilities that each potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts;
a first determining unit 520, configured to determine a conversion rate corresponding to the first region under the first peer-to-peer resource amount based on a corresponding exchange probability of each potential user in the first data set under the first peer-to-peer resource amount; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts;
a construction unit 530 configured to construct a first polyline based on the plurality of peer-to-peer resource amounts and the conversion rates of the first territory corresponding to the plurality of peer-to-peer resource amounts, respectively;
a second determining unit 540, configured to determine an optimal slope from slopes between the minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first folding line, and use a larger peer resource amount corresponding to the optimal slope as a candidate peer resource amount of the first region;
A third determining unit 550 configured to determine an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts of the plurality of territories;
and an output unit 560 configured to output the candidate peer-to-peer resource amounts of the plurality of regions as the distribution data when the average peer-to-peer resource amount is greater than a preset threshold.
In some embodiments, the exchange probability that each potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts is obtained by respectively inputting the user characteristics of the potential user and the peer-to-peer resource amounts into an exchange probability prediction model for prediction;
the exchange probability prediction model is obtained based on training historical behavior information of a plurality of sample users; the historical behavior information of the sample user comprises user characteristics, historical peer-to-peer resource quantity and exchange labels.
In some embodiments, the user characteristics include user attribute characteristics and user scene characteristics related to a scene in which the user uses the preset product.
In some embodiments, the abscissa of the first broken line corresponds to the plurality of peer resource amounts, the ordinate corresponds to the conversion rates of the first zone respectively corresponding to the plurality of peer resource amounts, and the optimal slope is the smallest slope among slopes between the smallest peer resource amount coordinate point and other larger peer resource amount coordinate points on the first broken line.
In some embodiments, an abscissa of the first broken line corresponds to a conversion rate of the first zone corresponding to the plurality of peer resource amounts, respectively, an ordinate corresponds to the plurality of peer resource amounts, and the optimal slope is a maximum slope among slopes between a minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first broken line.
In some embodiments, the third determining unit 550 is further configured to: an average peer-to-peer resource amount is determined based on the candidate peer-to-peer resource amounts for each of the plurality of regions and the population amounts for each of the regions.
In some embodiments, the third determining unit 550 is further configured to: an average amount of peer-to-peer resources is determined based on the amount of candidate peer-to-peer resources for each of the plurality of territories and the number of potential users.
In some embodiments, the apparatus further comprises an optimization unit (not shown) configured to: when the average peer-to-peer resource amount is smaller than or equal to the preset threshold value, determining a global optimal slope from a plurality of optimal slopes corresponding to the plurality of regions, taking the region corresponding to the global optimal slope as a region to be optimized, and taking a candidate peer-to-peer resource amount corresponding to the global optimal slope in the region as the peer-to-peer resource amount to be optimized;
On the broken line corresponding to the region to be optimized, determining a new optimal slope from the slope between the coordinate point of the peer-to-peer resource quantity to be optimized and the coordinate point of the peer-to-peer resource quantity higher than the peer-to-peer resource quantity to be optimized, and taking the larger peer-to-peer resource quantity corresponding to the new optimal slope as a new candidate peer-to-peer resource quantity of the region to be optimized;
updating the average peer-to-peer resource amount based on the new candidate peer-to-peer resource amount and the candidate peer-to-peer resource amount of the region outside the region to be optimized;
repeating the steps until the updated average peer-to-peer resource amount is greater than a preset threshold.
The functional units of the apparatus 500 may be implemented with reference to the method embodiment shown in fig. 1, and are not described herein.
In another aspect, embodiments of the present description provide a computer-readable storage medium having a computer program stored thereon, which when executed in a computer, causes the computer to perform the method shown in fig. 1.
In another aspect, embodiments of the present description provide a computing device including a memory having executable code stored therein and a processor that, when executing the executable code, implements the method shown in fig. 1.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention in further detail, and are not to be construed as limiting the scope of the invention, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the invention.
Claims (18)
1. A method of determining distribution data of product peer-to-peer resource amounts across multiple territories, comprising:
acquiring data sets respectively corresponding to a plurality of regions to be put in of a preset product, wherein the data sets comprise first data sets corresponding to a first region, the first data sets comprise user information of a plurality of potential users in the first region, and the user information of each potential user comprises exchange probability that each potential user exchanges with the preset product under a plurality of equivalent resource amounts;
Determining the conversion rate corresponding to the first region under the first peer-to-peer resource amount based on the corresponding exchange probability of each potential user under the first peer-to-peer resource amount in the first data set; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts;
constructing a first broken line based on the plurality of peer-to-peer resource amounts and the conversion rates of the first region corresponding to the plurality of peer-to-peer resource amounts respectively;
determining an optimal slope from slopes between the coordinate point of the minimum equivalent resource amount on the first fold line and other coordinate points of the larger equivalent resource amount, and taking the larger equivalent resource amount corresponding to the optimal slope as a candidate equivalent resource amount of the first region;
determining an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts for the plurality of regions;
when the average peer-to-peer resource amount is larger than a preset threshold value, outputting candidate peer-to-peer resource amounts of the regions as the distribution data;
wherein the peer-to-peer resource amount is an amount of valuable resource for a product consumer to exchange a product with a product provider;
the optimal slope is the slope corresponding to the peer-to-peer resource amount when the peer-to-peer resource amount increases fastest and the conversion rate decreases slowest.
2. The method of claim 1, wherein the exchange probability that each potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts is obtained by respectively inputting the user characteristics of the potential user and the peer-to-peer resource amounts into an exchange probability prediction model for prediction;
the exchange probability prediction model is obtained by training based on historical behavior information of a plurality of sample users; the historical behavior information of the sample user comprises user characteristics, historical peer-to-peer resource quantity and exchange labels.
3. The method of claim 2, wherein the user characteristics include user attribute characteristics and user scene characteristics related to a scene in which a user uses the preset product.
4. The method of claim 1, wherein an abscissa of the first polyline corresponds to the plurality of peer resource amounts and an ordinate corresponds to conversions of the first zone at the plurality of peer resource amounts, respectively, the optimal slope being a minimum slope of slopes between a minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first polyline.
5. The method of claim 1, wherein an abscissa of the first polyline corresponds to a conversion rate of the first zone at the plurality of peer resource amounts, respectively, and an ordinate corresponds to the plurality of peer resource amounts, the optimal slope being a largest slope of slopes between a minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first polyline.
6. The method of claim 1, wherein the determining an average peer-to-peer resource amount based on candidate peer-to-peer resource amounts for the plurality of zones comprises:
an average peer-to-peer resource amount is determined based on the candidate peer-to-peer resource amounts for each of the plurality of regions and the population amounts for each of the regions.
7. The method of claim 1, wherein the determining an average peer-to-peer resource amount based on candidate peer-to-peer resource amounts for the plurality of zones comprises:
an average amount of peer-to-peer resources is determined based on the amount of candidate peer-to-peer resources for each of the plurality of territories and the number of potential users.
8. The method of claim 1, wherein the method further comprises:
when the average peer-to-peer resource amount is smaller than or equal to the preset threshold value, determining a global optimal slope from a plurality of optimal slopes corresponding to the plurality of regions, taking the region corresponding to the global optimal slope as a region to be optimized, and taking a candidate peer-to-peer resource amount corresponding to the global optimal slope in the region as a peer-to-peer resource amount to be optimized;
on the broken line corresponding to the region to be optimized, determining a new optimal slope from the slope between the coordinate point of the peer-to-peer resource quantity to be optimized and the coordinate point of the peer-to-peer resource quantity higher than the peer-to-peer resource quantity to be optimized, and taking the larger peer-to-peer resource quantity corresponding to the new optimal slope as a new candidate peer-to-peer resource quantity of the region to be optimized;
Updating the average peer-to-peer resource amount based on the new candidate peer-to-peer resource amount and the candidate peer-to-peer resource amount of the region outside the region to be optimized;
repeating the steps until the updated average peer-to-peer resource amount is greater than the preset threshold.
9. An apparatus for determining distribution data of product peer-to-peer resource amounts in a plurality of regions, comprising:
the acquisition unit is configured to acquire data sets respectively corresponding to a plurality of regions to be released of a preset product, wherein the data sets comprise first data sets corresponding to a first region, the first data sets comprise user information of a plurality of potential users in the first region, and the user information of each potential user comprises exchange probability that each potential user exchanges with the preset product under a plurality of peer-to-peer resource amounts;
the first determining unit is configured to determine the conversion rate corresponding to the first region under the first peer-to-peer resource amount based on the corresponding exchange probability of each potential user in the first data set under the first peer-to-peer resource amount; the first peer-to-peer resource amount is any one of the plurality of peer-to-peer resource amounts;
a construction unit configured to construct a first polyline based on the plurality of peer-to-peer resource amounts and conversion rates of the first territory corresponding to the plurality of peer-to-peer resource amounts, respectively;
A second determining unit, configured to determine an optimal slope from slopes between the minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first folding line, and use a larger peer resource amount corresponding to the optimal slope as a candidate peer resource amount of the first region;
a third determining unit configured to determine an average peer-to-peer resource amount based on the candidate peer-to-peer resource amounts of the plurality of regions;
an output unit configured to output, as the distribution data, the candidate peer-to-peer resource amounts of the plurality of regions when the average peer-to-peer resource amount is greater than a preset threshold;
wherein the peer-to-peer resource amount is an amount of valuable resource for a product consumer to exchange a product with a product provider;
the optimal slope is the slope corresponding to the peer-to-peer resource amount when the peer-to-peer resource amount increases fastest and the conversion rate decreases slowest.
10. The device according to claim 9, wherein the exchange probability of each potential user for exchanging with the preset product under the condition of a plurality of peer resource amounts is obtained by respectively inputting the user characteristics of the potential user and the peer resource amounts into an exchange probability prediction model for prediction;
The exchange probability prediction model is obtained based on training historical behavior information of a plurality of sample users; the historical behavior information of the sample user comprises user characteristics, historical peer-to-peer resource quantity and exchange labels.
11. The apparatus of claim 10, wherein the user characteristics include user attribute characteristics and user scene characteristics related to a scene in which a user uses the preset product.
12. The apparatus of claim 9, wherein an abscissa of the first polyline corresponds to the plurality of peer resource amounts and an ordinate corresponds to conversions of the first zone at the plurality of peer resource amounts, respectively, the optimal slope being a minimum slope of slopes between a minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first polyline.
13. The apparatus of claim 9, wherein an abscissa of the first polyline corresponds to a conversion rate of the first zone at the plurality of peer resource amounts, respectively, and an ordinate corresponds to the plurality of peer resource amounts, the optimal slope being a largest slope of slopes between a minimum peer resource amount coordinate point and other larger peer resource amount coordinate points on the first polyline.
14. The apparatus of claim 9, wherein the third determination unit is further configured to: an average peer-to-peer resource amount is determined based on the candidate peer-to-peer resource amounts for each of the plurality of regions and the population amounts for each of the regions.
15. The apparatus of claim 9, wherein the third determination unit is further configured to: an average amount of peer-to-peer resources is determined based on the amount of candidate peer-to-peer resources for each of the plurality of territories and the number of potential users.
16. The apparatus of claim 9, wherein the apparatus further comprises an optimization unit configured to:
when the average peer-to-peer resource amount is smaller than or equal to the preset threshold value, determining a global optimal slope from a plurality of optimal slopes corresponding to the plurality of regions, taking the region corresponding to the global optimal slope as a region to be optimized, and taking a candidate peer-to-peer resource amount corresponding to the global optimal slope in the region as a peer-to-peer resource amount to be optimized;
on the broken line corresponding to the region to be optimized, determining a new optimal slope from the slope between the coordinate point of the peer-to-peer resource quantity to be optimized and the coordinate point of the peer-to-peer resource quantity higher than the peer-to-peer resource quantity to be optimized, and taking the larger peer-to-peer resource quantity corresponding to the new optimal slope as a new candidate peer-to-peer resource quantity of the region to be optimized;
Updating the average peer-to-peer resource amount based on the new candidate peer-to-peer resource amount and the candidate peer-to-peer resource amount of the region outside the region to be optimized;
repeating the steps until the updated average peer-to-peer resource amount is greater than the preset threshold.
17. A computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of claims 1-8.
18. A computing device comprising a memory and a processor, wherein the memory has executable code stored therein, which when executed by the processor, implements the method of any of claims 1-8.
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| CN106127531A (en) * | 2016-07-14 | 2016-11-16 | 北京物思创想科技有限公司 | The method and system of differentiation price are performed based on machine learning |
| CN106875211A (en) * | 2017-01-06 | 2017-06-20 | 华东师范大学 | Optimal service pricing method based on user's perceived value in a kind of cloud computing environment |
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