CN111598677A - Resource quota determining method and device and electronic equipment - Google Patents
Resource quota determining method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a resource quota determining method and device based on posterior data optimization and electronic equipment. The method comprises the following steps: constructing a sequencing model, and training the sequencing model by using training data, wherein the training data comprises user characteristic data and resource quota data of historical users; calculating user scores by using the trained sequencing model; bias calibration is carried out on the user scores; and calculating the resource quota limit of the user according to a mapping function between the user score and the user quota. The method of the invention uses the sequencing model to calculate the user score, and calibrates the user score through the CTR estimation algorithm, so that the distance between each sample can be more accurately ensured, the distance can be ensured on the basis of ensuring the model output to be ordered, and the model accuracy is further improved; for different users, the resource quota limit of the user can be determined more accurately, and therefore the resource allocation process is further optimized.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a resource quota determining method and device based on posterior data optimization and electronic equipment.
Background
In internet-based application technology, there is often a need to exchange resources between different parties. Resources, as referred to herein, refer to any available material, information, money, time, etc. Information resources include computing resources and various types of data resources. The data resources include various private data in various domains. In the process of allocating resources, it is often necessary to authenticate a resource allocation right of a user and allocate different resource quotas to different users, where the resource allocation right refers to authentication of whether the user has the right to acquire resources, and may be authenticated by a specific resource management mechanism or by all parties of the resources. By resource quota is meant the highest amount of resources that the user can obtain within a particular time.
Money-related resources are also commonly referred to as financial resources, which refers to the sum or aggregate of a series of objects related to the structure, quantity, scale, distribution, effect and interaction relationship between subjects and objects of financial services in the financial field, and only when the financial resources are configured efficiently in production and life, the financial and economic sustainable development can be realized. For companies that provide internet financial services, the financial resource may be the total amount of funds, or the amount of assets equivalent to funds, or the like. For companies that provide internet financial services, some of the financial assets may be used to provide financial services to individual users, some may be used to provide financial services to other enterprise users, and others may be used to invest in the development of the company or perform other financial-related transactions.
For companies with internet financial services, it is important to allocate financial resources reasonably among different businesses because the total financial resources are limited in a relatively fixed time. For enterprise users or other financial related businesses served by the internet financial service company, the time and period for occupying financial resources can be generally approved through plan approval in advance, which is favorable for overall arrangement of allocation of the financial resources. For individual users, due to individual differences of the individual users, an internet financial service company can hardly predict plans and time of financial resource demands of the individual users in advance, how to better predict the financial service demands of the individual users, and how to more reasonably distribute the financial resources of the individual users are difficult problems faced by the internet financial service company at present.
Therefore, there is a need to provide a more optimized resource quota determination method.
Disclosure of Invention
In order to solve the above problem, the present invention provides a resource quota determining method based on posterior data optimization, which includes: constructing a sequencing model, and training the sequencing model by using training data, wherein the training data comprises user characteristic data and resource quota data of historical users; calculating user scores by using the trained sequencing model; bias calibration is carried out on the user scores; and calculating the resource quota limit of the user according to a mapping function between the user score and the user quota.
Preferably, the method further comprises the following steps: setting a first threshold value and a second threshold value, and acquiring guest groups distributed with different quota through the first threshold value and the second threshold value; and screening historical user data, and acquiring quota allocation values which are greater than or equal to the first threshold and less than or equal to the second threshold and corresponding user characteristic data as training data.
Preferably, the method further comprises the following steps: the ranking model is a logistic regression model, and the user score calculated by the logistic regression model is a numerical value between 0 and 1.
Preferably, the bias calibrating the user score comprises: using the CTR estimation algorithm, the user scores will be calibrated.
Preferably, the calculation formula of the CTR prediction algorithm is as follows:
Preferably, the calculating the resource quota limit of the user according to the mapping function between the user score and the user quota further includes: setting a total quota interval for a specific guest group, and setting a plurality of quota distribution value intervals which are equidistant and continuous in the total quota interval; dividing the user scores of 0-1 into a plurality of continuous intervals corresponding to the plurality of quota allocation value intervals at equal intervals; and forming a mapping relation between the multiple quota allocation value intervals and multiple intervals scored by the user so as to determine mapping functions of different customer groups.
Preferably, the mapping relation formed by the multiple quota allocation value intervals and the multiple intervals scored by the user is a linear relation.
Preferably, the logistic regression model calculates the user score using a sigmoid function.
In addition, the invention also provides a resource quota determining device based on posterior data optimization, which comprises: the construction module is used for constructing a sequencing model and training the sequencing model by using training data, wherein the training data comprises user characteristic data and resource quota data of historical users; the first calculation module is used for calculating the user score by using the trained sequencing model; the calibration module is used for carrying out bias calibration on the user scores; and the second calculation module calculates the resource quota limit of the user according to a mapping function between the user score and the user limit.
Preferably, the method further comprises the following steps: the setting module is used for setting a first threshold value and a second threshold value and obtaining the guest groups distributed with different quota through the first threshold value and the second threshold value; and the screening module is used for screening historical user data, acquiring quota distribution values which are greater than or equal to the first threshold and less than or equal to the second threshold and corresponding user characteristic data thereof, and taking the quota distribution values as training data.
Preferably, the method further comprises the following steps: the ranking model is a logistic regression model, and the user score calculated by the logistic regression model is a numerical value between 0 and 1.
Preferably, the calibration module further comprises: using the CTR estimation algorithm, the user scores will be calibrated.
Preferably, the calculation formula of the CTR prediction algorithm is as follows:
Preferably, the system further comprises a processing module, wherein the processing module sets a total quota interval for a specific guest group, and sets a plurality of quota allocation value intervals which are equidistant and continuous in the total quota interval; dividing the user scores of 0-1 into a plurality of continuous intervals corresponding to the plurality of quota allocation value intervals at equal intervals; and forming a mapping relation between the multiple quota allocation value intervals and multiple intervals scored by the user so as to determine mapping functions of different customer groups.
Preferably, the mapping relation formed by the multiple quota allocation value intervals and the multiple intervals scored by the user is a linear relation.
Preferably, the logistic regression model calculates the user score using a sigmoid function.
In addition, the present invention also provides an electronic device, wherein the electronic device includes: a processor; and a memory storing computer-executable instructions that, when executed, cause the processor to perform the a posteriori data optimization based resource quota determining method of the present invention.
Furthermore, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the resource quota determining method based on posterior data optimization according to the present invention.
Advantageous effects
Compared with the prior art, the resource quota determining method disclosed by the invention has the advantages that the user score is calculated by using the sequencing model, and the user score is calibrated by using the CTR (China Mobile radio) estimation algorithm, so that the distance between samples can be more accurately ensured, the distance can be ensured on the basis of orderly output of the model, and the accuracy of the model is further improved; for different users, the resource quota limit of the user can be determined more accurately, and therefore the resource allocation process is further optimized.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive faculty.
FIG. 1 is a flowchart of an example of a resource quota determining method based on posterior data optimization according to the present invention.
FIG. 2 is a flowchart of another example of a resource quota determining method based on a posteriori data optimization according to the present invention.
FIG. 3 is a flowchart of another example of a resource quota determining method based on a posteriori data optimization according to the present invention.
Fig. 4 is a schematic block diagram of an example of the resource quota determining apparatus based on posterior data optimization according to the present invention.
Fig. 5 is a schematic block diagram illustrating another example of the resource quota determining apparatus based on posterior data optimization according to the present invention.
Fig. 6 is a schematic block diagram illustrating still another example of the resource quota determining apparatus based on posterior data optimization according to the present invention.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention.
Fig. 8 is a block diagram of an exemplary embodiment of a computer-readable medium according to the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
In order to further optimize resource allocation, the invention provides a resource quota determining method based on posterior data optimization, which uses a sorting model to calculate user scores and calibrates the user scores through a CTR (China Mobile radio) estimation algorithm, so that the distance between samples can be more accurately ensured, the model output can be ensured to be in a distance guarantee on the basis of ordering, and the model accuracy is further improved; for different users, the resource quota limit of the user can be determined more accurately, and therefore the resource allocation process is further optimized.
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments. It should be noted that, in the present invention, a resource refers to any available substance, information, time, and information resources include computing resources and various types of data resources. The data resources include various private data in various domains. In essence, the present invention can be applied to the distribution of various types of resources, including physical goods, water, electricity, meaningful data, and the like. However, for convenience, the resource quota determination method is described in the present invention by taking financial data resources as an example, but those skilled in the art should understand that the present invention can also be used for resource quota determination of other resources.
Example 1
An embodiment of the resource quota determining method based on posterior data optimization according to the present invention will be described below with reference to fig. 1 to 3.
FIG. 1 is a flowchart of an example of a resource quota determining method based on posterior data optimization according to the present invention.
As shown in fig. 1, a resource quota determining method based on posterior data optimization includes the following steps.
Step S101, a ranking model is built, training data is used for training the ranking model, and the training data comprises user characteristic data and resource quota data of historical users.
And step S102, calculating user scores by using the trained sequencing model.
And step S103, carrying out bias calibration on the user scores.
And step S104, calculating the resource quota limit of the user according to a mapping function between the user score and the user limit.
First, in step S101, a ranking model is constructed, and training data including user feature data and resource quota data of historical users is used to train the ranking model.
Preferably, the ranking model is a logistic regression model, and the user score calculated by using the logistic regression model is a numerical value between 0 and 1.
Specifically, for the construction of the ranking model, the definition of good and bad samples is also included. As a specific example, a user characteristic of "whether the resource return is overdue or violated" may be used to define a good-bad sample, i.e., a label is "whether the resource return is overdue or violated", and a label value is defined as 0 or 1, i.e., a user score is a numerical value between 0 and 1, where 1 represents that the user has the resource return overdue, and 0 represents that the user has no overdue. Generally, the smaller the user score is, the lower the resource return overdue probability or default probability of the user is, the better the loan recovery principal is, the better the use efficiency of the fund is, the lower the risk degree of resource recovery is, and vice versa.
It should be noted that the user score refers to a return risk assessment value of the user for the allocated resource. In this example, the user score of the user is directly used as the resource return overdue probability or default probability of the user. But not limited thereto, in other examples, it may also represent only a probability of breach or other risk indicator.
As shown in fig. 2, a step S201 of filtering the historical user data to construct a training data set is further included.
In step S201, the historical user data is filtered to construct a training data set.
Specifically, historical user data is screened, a first threshold and a second threshold are set, and guest groups and related data distributed in different limits are obtained through the first threshold and the second threshold to construct a training data set, so that sample selection is optimized, and a model learning effect is improved.
Further, quota distribution values which are greater than or equal to the first threshold value and less than or equal to the second threshold value and corresponding user characteristic data are obtained from historical user data (namely posterior data) and are used as training data.
For example, the first threshold value is 20000 yuan credit allocation value, the second threshold value is 3300 yuan credit allocation value, the users with high-order credit and the users with low-order credit are screened out, and a training data set is constructed to be used for training the ranking model.
Next, in step S102, a user score is calculated using the trained ranking model.
Specifically, user characteristic data is input, and a trained ranking model is used for calculating the user score of the current user.
Preferably, the logistic regression model calculates the user score using a sigmoid function.
And further, ranking the user scores calculated in all the training data sets, ranking the historical user quota allocation values, and further performing data alignment processing to obtain data of the user scores and the user quota allocation values aligned on two sides.
For example, the user scores calculated for user a, user B, and user C are 0.2, 0.6, and 0.9, with the user scores ordered as user C > user B > user a. Correspondingly, the sequence of the user quota allocation values allocated to each user is user C, user B and user A.
It should be noted that, when the user scores calculated by using the ranking model are ranked, the order among different users, that is, the ranking of positive and negative samples, can be ensured. However, in order to further improve the model accuracy, the invention carries out offset calibration on the user score to more accurately ensure the distance between each sample, thereby realizing more accurate determination of the user quota allocation value.
Next, in step S103, bias calibration is performed on the user score.
In this example, the user scores will be calibrated using the CTR prediction algorithm.
Specifically, the calculation formula of the CTR prediction algorithm is as follows:
Therefore, the credit allocation value interval corresponding to each user score is adjusted according to the historical sample through the CTR pre-estimation algorithm so as to ensure that the model output can also keep distance on the basis of order.
Next, in step S104, a resource quota limit of the user is calculated according to a mapping function between the user score and the user quota.
As shown in fig. 3, the method further includes a step S301 of setting a mapping function between the user score and the user amount.
In step S301, a mapping function between the user score and the user amount is set.
Specifically, a total credit line section is set for a specific guest group, and a plurality of credit line allocation value sections which are equidistant and continuous are set in the total credit line section.
In this example, the guest groups are classified into four classes according to a preset guest group classification policy, wherein the guest group classification policy includes whether an authentication report exists, whether a record of movement is available within a specific time period, and the like. However, the present invention is not limited thereto, and the above description is only by way of example and is not to be construed as limiting the present invention.
Specifically, the user scores of 0-1 are equally divided into a plurality of continuous intervals corresponding to the plurality of quota allocation value intervals. For example, the partition walls are divided into (0-0.1), (0.1-0.2), (0.2-0.3) and (0.3-0.4) … (0.9-1) at equal intervals.
For example, the total credit range is (1000 yuan, 20000 yuan), and further divided into (1000 yuan to 2000 yuan), (2000 yuan to 3000 yuan), and … (18000 yuan to 20000 yuan).
Further, a mapping relation is formed between the plurality of quota allocation value intervals and a plurality of intervals scored by the user, preferably, the mapping relation is a linear relation so as to determine mapping functions of different customer groups.
Further, the method also comprises the step of establishing corresponding mapping functions for different customer groups according to parameter indexes, wherein the parameter indexes comprise risk parameters, multi-projection parameters, user scholastic calendars and the like.
Therefore, the resource quota limit (namely the user quota allocation value) of the user is calculated according to the established mapping function.
The above-mentioned process of the resource quota determining method based on posterior data optimization is only used for explaining the present invention, and the order and number of the steps are not particularly limited. In addition, the steps in the method can be split into two or three steps, or some steps can be combined into one step, and the steps are adjusted according to practical examples.
Compared with the prior art, the resource quota determining method disclosed by the invention has the advantages that the user score is calculated by using the sequencing model, and the user score is calibrated by using the CTR (China Mobile radio) estimation algorithm, so that the distance between samples can be more accurately ensured, the distance can be ensured on the basis of orderly output of the model, and the accuracy of the model is further improved; for different users, the resource quota limit of the user can be determined more accurately, and therefore the resource allocation process is further optimized.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
Example 2
Referring to fig. 4, 5 and 6, the present invention further provides a resource quota determining apparatus 400 based on posterior data optimization, including: a building module 401, configured to build a ranking model, and train the ranking model using training data, where the training data includes user feature data and resource quota data of a historical user; a first calculating module 402, configured to calculate a user score using the trained ranking model; a calibration module 403, configured to perform bias calibration on the user score; the second calculating module 404 calculates the resource quota limit of the user according to a mapping function between the user score and the user quota.
As shown in fig. 5, the method further includes: a setting module 501, configured to set a first threshold and a second threshold, and obtain guest groups allocated with different quota through the first threshold and the second threshold; the screening module 502 is configured to screen historical user data, and obtain quota allocation values that are greater than or equal to the first threshold and less than or equal to the second threshold and user characteristic data corresponding to the quota allocation values as training data.
Preferably, the ranking model is a logistic regression model, and the user score calculated by the logistic regression model is a numerical value between 0 and 1.
Preferably, the calibration module further comprises: using the CTR estimation algorithm, the user scores will be calibrated.
Preferably, the calculation formula of the CTR prediction algorithm is as follows:
As shown in fig. 6, the system further comprises a processing module 601, wherein the processing module 601 sets a total quota interval for a specific guest group, and sets a plurality of quota allocation value intervals which are equidistant and continuous in the total quota interval; dividing the user scores of 0-1 into a plurality of continuous intervals corresponding to the plurality of quota allocation value intervals at equal intervals; and forming a mapping relation between the multiple quota allocation value intervals and multiple intervals scored by the user so as to determine mapping functions of different customer groups.
Preferably, the mapping relation formed by the multiple quota allocation value intervals and the multiple intervals scored by the user is a linear relation.
Preferably, the logistic regression model calculates the user score using a sigmoid function.
In embodiment 2, the same portions as those in embodiment 1 are not described.
Compared with the prior art, the resource quota determining device disclosed by the invention has the advantages that the user score is calculated by using the sequencing model, and the user score is calibrated by using the CTR (China traffic report) estimation algorithm, so that the distance between samples can be more accurately ensured, the model output can be ensured to be in a distance guarantee on the basis of ordering, and the model accuracy is further improved; for different users, the resource quota limit of the user can be determined more accurately, and therefore the resource allocation process is further optimized.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Example 3
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. An electronic device 200 according to the invention will be described below with reference to fig. 7. The electronic device 200 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic device processing method section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 1.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to carry out the above-described methods of the invention.
As shown in fig. 8, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (10)
1. A resource quota determining method based on posterior data optimization is characterized by comprising the following steps:
constructing a sequencing model, and training the sequencing model by using training data, wherein the training data comprises user characteristic data and resource quota data of historical users;
calculating user scores by using the trained sequencing model;
bias calibration is carried out on the user scores;
and calculating the resource quota limit of the user according to a mapping function between the user score and the user quota.
2. The method of claim 1, further comprising:
setting a first threshold value and a second threshold value, and acquiring guest groups distributed with different quota through the first threshold value and the second threshold value;
and screening historical user data, and acquiring quota allocation values which are greater than or equal to the first threshold and less than or equal to the second threshold and corresponding user characteristic data as training data.
3. The method according to any one of claims 1 to 2, further comprising:
the ranking model is a logistic regression model, and the user score calculated by the logistic regression model is a numerical value between 0 and 1.
4. The method of claim 3, wherein the bias calibrating the user score comprises:
using the CTR estimation algorithm, the user scores will be calibrated.
5. The method according to claim 4, wherein the CTR pre-estimation algorithm has a calculation formula:
6. The method of claim 1, wherein calculating the resource quota limit of the user according to a mapping function between the user rating and the user limit further comprises:
setting a total quota interval for a specific guest group, and setting a plurality of quota distribution value intervals which are equidistant and continuous in the total quota interval;
dividing the user scores of 0-1 into a plurality of continuous intervals corresponding to the plurality of quota allocation value intervals at equal intervals;
and forming a mapping relation between the multiple quota allocation value intervals and multiple intervals scored by the user so as to determine mapping functions of different customer groups.
7. The method of claim 6, wherein the mapping relationship between the plurality of quota allocation value intervals and the plurality of intervals scored by the user is linear.
8. A resource quota determining apparatus based on a posteriori data optimization, comprising:
the construction module is used for constructing a sequencing model and training the sequencing model by using training data, wherein the training data comprises user characteristic data and resource quota data of historical users;
the first calculation module is used for calculating the user score by using the trained sequencing model;
the calibration module is used for carrying out bias calibration on the user scores;
and the second calculation module calculates the resource quota limit of the user according to a mapping function between the user score and the user limit.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the a posteriori data optimization-based resource quota determining method according to any of claims 1 to 7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the a posteriori data optimization based resource quota determining method of any of claims 1 to 7.
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