CN108734460A - A kind of means of payment recommends method, apparatus and equipment - Google Patents
A kind of means of payment recommends method, apparatus and equipment Download PDFInfo
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Abstract
This specification embodiment discloses a kind of means of payment and recommends method, apparatus and equipment.The method includes：Obtain the multidimensional characteristic information in real time such as user information, information to be paid and means of payment information, it is based on above- mentioned information using machine learning model is recommended, marking sequence is carried out to the available means of payment of user, according to ranking results, recommend the means of payment needed for it for designated user, so that user can quickly finish delivery operation.
Description
Technical field
This specification is related to field of computer technology more particularly to a kind of means of payment recommends method, apparatus and equipment.
Background technology
It is more and more when people's shopping on the web or solid shop/brick and mortar store are done shopping with the development of noncash currency payment technology
Ground selects payment software to complete payment.
In the prior art, payment software has provided a user a variety of means of payment, for example, bank card pays, pays out, account
Family remaining sum payment etc..In order to meet the rapid payment demand of user, when user pays, payment software would generally be user
Recommend the means of payment.The existing recommendation means of payment is normally based on the realization of the simple rules such as user's use habit.
Based on the prior art, it is desirable to be able to accurately carry out the scheme of means of payment recommendation for designated user.
Invention content
This specification embodiment provides the means of payment and recommends method, apparatus and equipment, for solving following technical problem：
It is required to accurately carry out the scheme of means of payment recommendation for designated user.
In order to solve the above technical problems, what this specification embodiment was realized in：
A kind of means of payment that this specification embodiment provides recommends method, including：
For paying each time, user information and information to be paid are obtained；
According to the user information and the information to be paid, using the recommendation machine learning model of pre-training, to having
The means of payment sorts；
Recommend the means of payment to user according to ranking results.
A kind of means of payment recommendation apparatus that this specification embodiment provides, including：
First acquisition module obtains user information and information to be paid for paying each time；
Sorting module utilizes the recommendation machine learning mould of pre-training according to the user information and the information to be paid
Type, to having means of payment sequence；
Recommending module recommends the means of payment according to ranking results to user.
The a kind of electronic equipment that this specification embodiment provides, including：
At least one processor；And
The memory being connect at least one processor communication；Wherein,
The memory is stored with the instruction that can be executed by least one processor, and described instruction is by described at least one
A processor executes, so that at least one processor can：
For paying each time, user information and information to be paid are obtained；
According to the user information and the information to be paid, using the recommendation machine learning model of pre-training, to having
The means of payment sorts；
Recommend the means of payment to user according to ranking results.
Above-mentioned at least one technical solution that this specification embodiment uses can reach following advantageous effect：
It is available according to userspersonal information, user's history payment information, user by using recommendation machine learning model
Means of payment information, scene information to be paid and merchandise news to be paid etc., various features consider simultaneously, according to actual delivery feelings
Condition carries out the recommendation of the means of payment for designated user, can effectively promote the accuracy rate of means of payment recommendation.
Description of the drawings
In order to illustrate more clearly of this specification embodiment or technical solution in the prior art, below will to embodiment or
Attached drawing needed to be used in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is only
Some embodiments described in this specification, for those of ordinary skill in the art, in not making the creative labor property
Under the premise of, other drawings may also be obtained based on these drawings.
Fig. 1 is the schematic diagram for the means of payment commending system that the scheme of this specification is related under practical application scene；
Fig. 2 is the flow diagram that a kind of means of payment that this specification embodiment provides recommends method；
Fig. 3 is the flow diagram of the training method for the recommendation machine learning model that this specification embodiment provides；
Fig. 4 is the LTR model training method schematic diagrames that this specification embodiment provides；
Fig. 5 is the model training datagram that this specification embodiment provides；
Fig. 6 is a kind of structural schematic diagram for means of payment recommendation apparatus that this specification embodiment provides；
Fig. 7 is the means of payment commending system structural schematic diagram that this specification embodiment provides；
Fig. 8 is the schematic diagram for the platform architecture that this specification embodiment provides.
Specific implementation mode
In order to make those skilled in the art more fully understand the technical solution in this specification, below in conjunction with this explanation
Attached drawing in book embodiment is clearly and completely described the technical solution in this specification embodiment, it is clear that described
Embodiment be merely a part but not all of the embodiments of the present application.Based on this specification embodiment, this field
The every other embodiment that those of ordinary skill is obtained without creative efforts, should all belong to the application
The range of protection.
Fig. 1 is a kind of signal for the means of payment commending system that the scheme of this specification is related under practical application scene
Figure.Based on user information, scene information to be paid, merchandise news to be paid, means of payment information etc., using trained in advance
Recommend machine learning model, for the means of payment needed for the accurate recommended user of designated user；For example, the recommendation machine learning
Model is LTR (learning to rank, sequence study) model, and the LTR models are according between user information and the means of payment
The degree of correlation the optionally each means of payment of user is given a mark and is sorted, recommend the earlier payer of sorting for user
Formula.Wherein, it is based on user information characteristic parameter, scene information characteristic parameter to be paid, to be paid to recommend machine learning model
What the training such as merchandise news characteristic parameter, means of payment characteristic parameter obtained.
It should be noted that the means of payment mentioned here, can be the mode paid based on payment software, it is specific to wrap
It includes：Based on payment software credit payment included in it, pay out pay, account balance payment etc. is by way of payments；Also may be used
To be to be based on payment software in such a way that various bank cards (for example, credit card, debit card etc.) complete payment.It is readily appreciated that,
It realizes that means of payment needs are realized by user terminal, can be supported for example, the user terminal can be user mobile phone, computer etc.
The equipment for paying software.
Based on above-mentioned scene, the scheme of this specification is described in detail below.
Fig. 2 is the flow diagram that a kind of means of payment that this specification embodiment provides recommends method, and this method is specific
It may comprise steps of：
Step S202：For paying each time, user information and information to be paid are obtained.
For example, it is assumed that user is equipped with certain payment software in its mobile phone terminal, user by payment software login user account,
And the related means of payment is bound or enabled based on the user account.It is readily appreciated that, user can be related in user account
Personal information；Binding or the means of payment enabled include credit payment based on the payment software, pay out pay, account balance
The means of payment such as payment；Can also be the various bank cards (for example, credit card, debit card etc.) bound based on payment software.
In practical applications, status information of information to be paid, user information and the means of payment etc. may be upper primary
Payment is changed after completing, and therefore, each time when delivery operation, needs user in real information and letter to be paid
Breath, so as to be more accurately that user recommends the means of payment according to the information obtained in real time.
Step S204：According to the user information and the information to be paid, the recommendation machine learning mould of pre-training is utilized
Type, to having means of payment sequence.
In practical applications, the means of payment enabled by the payment software of each user and bound bank card
Situation is all different, also, the payment custom of each user also has difference.For example, user's first is when carrying out small amount payment,
Like using credit payment；When carrying out wholesale payment, like using debit payments.For another example, the available balance in user's debit card
It is to change, payment software needs to consider whether the state of each means of payment can be used when recommending the means of payment.
The existing means of payment mentioned here, the main various means of payment for including user's binding, including available payment
Mode and the not available means of payment；When being ranked up to the means of payment, can all means of payment be carried out with whole sequence,
It can also be to the not available means of payment without sequence, for example, it is assumed that the remaining sum in payment software is null element, then in the software
Remaining sum be not just available the means of payment, sequence when, recommend machine learning model the remaining sum means of payment will not be carried out
Sequence.
S206：Recommend the means of payment to user according to ranking results.
In general, ranking results are the modes of descending, that is, will with the maximum means of payment of the user information degree of correlation (that is,
The means of payment of user's most probable) it makes number one.
In this specification one or more embodiment, to having means of payment sequence, specifically include：According to the use
Family information determines the means of payment for having binding relationship with the user；It is ranked up for the means of payment determined；
Wherein, the user information includes：User's history payment information and userspersonal information；The information to be paid includes：It waits propping up
Pay scene information and merchandise news to be paid.
Known to as described in citing above, it is assumed that user mobile phone end case payment software can be obtained by the payment software
Take the individuals such as the userspersonal information for having logged on the software, including address name, gender, age, educational background and job category
Information.Meanwhile the payment software can also obtain the history payment information that user utilizes the payment software to complete, and can specifically wrap
It includes：History pays scene (for example, on line or under line and Merchant name), time of payment, payment region etc.；History is paid
Merchandise news：Quantity, goods amount of payment for merchandise etc..
It is readily appreciated that, the means of payment with user with binding relationship mentioned here, including：The payer that user enables
Formula or the means of payment of user's addition, can be understood as the means of payment with binding relationship.User enables or binding
The means of payment can just be used to recommend user's use.The means of payment that each user enables and binds is different, and is needed
Want payment software to obtain its according to user information and correspond to available means of payment information, including means of payment title, use state,
Access times can use amount etc..It accurately recommend payer needed for it to recommend machine learning model that can be directed to user
Formula can effectively promote user experience.
It should be noted that payment scene mentioned here includes being paid under payment and line on line.Specifically, being paid on line
Information may include the information such as matching relationship between electronic emporium information, electronic emporium and payment software on line；It is paid under line
Information may include the information such as the means of payment that shop is supported under line.
In this specification one or more embodiment, in the recommendation machine learning model using pre-training, to having
Before means of payment sequence, the method further includes：According to the information to be paid, corresponding characteristic to be paid is extracted；
According to default cleaning rule, the characteristic to be paid is cleaned.
Since each payment task of user is different, for example, user is different using the scene paid, uses payment
Type of merchandise difference, the payment amount difference etc. that region is different, pays.Therefore, it after obtaining information to be paid every time, needs
Data processing is carried out to the information to be paid, specifically, including：Using preset cleaning rule to characteristic to be paid into
Row cleaning；Characteristic to be paid therein is extracted according to information to be paid using Feature Engineering.To recommend machine learning
Model being capable of the accurate recommendation means of payment of feature based data realization.
In this specification one or more embodiment, if Fig. 3 is the recommendation engineering that this specification embodiment provides
The flow diagram of the training method of model is practised, pre-training is recommended machine learning model, be can specifically include：
S302：Training user's information and training means of payment information are obtained, determines training user's information and the instruction
Practice the correspondence between means of payment information.
In general, each user information corresponds to multiple means of payment information；When being trained to machine learning model,
Need the correspondence between trained training user's information and training means of payment information.
S304：According to the correspondence, the degree of correlation of training user and the means of payment is determined.
Training user mentioned here and the means of payment degree of correlation, in general, the degree of correlation is higher, is carrying out the means of payment
Sequence is more forward when recommendation.In practical applications, the degree of correlation of the training data, can be by artificially marking.
S306：Based on training user's information, the trained means of payment information and the degree of correlation, pushed away described in training
Recommend machine learning model.
Can more accurately it be recommended for designated user needed for it by the recommendation machine learning model to realize
The means of payment, when being trained to the machine learning model, the training data inputted more fully, including multidimensional characteristic, such as：
User information, means of payment information, the degree of correlation, payment scene information etc.；More accurate engineering is obtained so as to training
Practise model.
In this specification one or more embodiment, based on training user's information, the trained means of payment
Information and the degree of correlation, the training recommendation machine learning model, can specifically include：Based on the user information, described
Training means of payment information and the degree of correlation, extract corresponding characteristic；According to the characteristic, the training recommendation
Machine learning model.
It should be noted that when obtaining the characteristic for training, needs to clean interference data therein, such as prop up
Failure, payment repetition, payment daily record mistake etc. are paid, commodity are abnormal, scene does not support bank card or bank card channel at this stage
The various information such as busy.Then comprehensive information as above completes feature output by Feature Engineering.
Further, the recommendation machine learning model includes LTR sequence learning models.
It should be noted that Ranking Algorithm is a kind of discriminate learning method having supervision, it is illustrated in figure 4 this theory
The LTR model training method schematic diagrames that bright book embodiment provides.One typical training set generally comprises：N training inquiry q
(i) (i=1 ..., n) judges with each inquiry q (i) relevant collection of document and corresponding correlation.Right the latter is special
Fixed learning algorithm is used in one order models of study, the ground that it can as accurately as possible close training set
Truth label are predicted.In forecast period, when a new inquiry occurs, the model that the training stage learns well can be used
The sequencer procedure of document is instructed, and returns to corresponding the results list.The research of entire Ranking Algorithm can substantially be divided into three
Class：Pointwise methods, pairwise methods and listwise methods.
By taking pointwise methods as an example, concrete model prepares sample as shown in figure 5, Fig. 5 provides for this specification embodiment
Model training datagram.
Wherein, uid is classified as User ID, and frd_uid is the means of payment ID of user.
label：In the same qid in means of payment sequence, the more forward label of row is (that is, previously described correlation
Degree) data are bigger.If not pressing label size of data to sort, may result in：Performance when training pattern (such as selection NDCG@
K is as evaluation index) qualification can not be optimized；Also, performance of the model on forecast set, NDCG do not exceed 0.1.
qid：Training sample packet marking, the same uid correspond to identical qid, and the identical data of qid values are needed in number
Continuously occur according in table, for example, the qid of continuous several rows is 1,1,1,4,4,2,2,2 in training data, (1 1 can be grouped into
1) three groups of (4 4) (2 2 2)；But if (4) four groups of (1 1 1) (4) (2 2 2) can be grouped by being 1,1,1,4,2,2,2,4,
Can be considered as four different users, as four training samples.Therefore, it to give special heed to and allow the qid data of identical value
It is continuous to occur.
features：The characteristic sequence of original form is turned into index0:val0,index1:val1,index2:val2,
indexn:Valn, the format of this key-value pair.For example, this feature can be 46 dimensional features.
In addition to label, qid, features are arranged, other row are only intended to that the later stage is helped to more fully understand data, not directly
It connects and is used for training process.
According to above-described embodiment it can be appreciated that by using machine learning model is recommended, according to userspersonal information, use
Family history payment information, the available means of payment information of user, scene information to be paid and merchandise news to be paid etc., Duo Zhongte
It levies while considering, carry out the recommendation of the means of payment for designated user according to actual delivery situation, can effectively promote payer
The accuracy rate that formula is recommended.
Based on same thinking, this illustrates that embodiment also provides a kind of computer-readable medium, and the media storage has meter
Calculation machine readable instruction, the computer-readable instruction can be executed by processor to realize the side described in any embodiment as above
Method.
Based on same thinking, this specification embodiment also provides a kind of means of payment recommendation apparatus, is illustrated in figure 6 this
A kind of structural schematic diagram for means of payment recommendation apparatus that specification embodiment provides, the device can specifically include：
First acquisition module 601 obtains user information and information to be paid for paying each time；Sorting module 602,
According to the user information and the information to be paid, using the recommendation machine learning model of pre-training, to having the means of payment
Sequence；Recommending module 603 recommends the means of payment according to ranking results to user.
Further, further include：Second acquisition module 604, second acquisition module 604, according to the user information,
Corresponding means of payment information is obtained, determines the means of payment that there is binding relationship with the user；Wherein, the user information
Including：User's history payment information and userspersonal information；The information to be paid includes：Scene information to be paid and to be paid
Merchandise news.
Further, further include：Feature processing block 605；The feature processing block 605, according to default cleaning rule,
Clean the information to be paid；According to the information to be paid, corresponding characteristic to be paid is extracted.
Further, further include：Model training module 606；The model training module 606 obtains training user's information
With training means of payment information, the correspondence between training user's information and the trained means of payment information is determined；
According to the correspondence, the degree of correlation of training user and the means of payment is determined；Based on training user's information, the training
Means of payment information and the degree of correlation, the training recommendation machine learning model.
Further, training user's information, the trained means of payment information and the degree of correlation, training institute are based on
Recommendation machine learning model is stated, is specifically included：Based on the user information, the trained means of payment information to it is described related
Degree, cleans and extracts corresponding characteristic；According to the characteristic, the training recommendation machine learning model.
Further, the recommendation machine learning model includes LTR sequence learning models.
For be better understood from the numerical procedure of the application, if Fig. 7 is that the means of payment that this specification embodiment provides pushes away
Recommend system structure diagram.The training and methods for using them of the commending system is as follows：
Obtain basic data, including user data, contextual data, commodity data etc..For example, user data includes：History
Payment data (for example, payment acts payment amount, time of payment etc.)；And the personal information of user is (for example, age-sex
Essential information shopping class information).Contextual data includes：Scene information is paid (for example, market, offline booth are super under electric business, line
The information such as city).Commodity data includes：Merchandise news (for example, commodity price etc.) to be paid.
Feature is extracted using Feature Engineering to information above.It needs to carry out data cleansing to above- mentioned information, such as：Payment is lost
Lose, pay repeat, payment daily record mistake etc., commodity are abnormal, scene does not support bank card or bank card channel is busy at this stage
Etc. various information.
After characteristic is ready to complete, further, model is trained, includes mainly：Characteristic processing and model tune
It is whole.Wherein, characteristic processing includes：Feature extraction, feature normalization, specimen sample；Model treatment includes：Model training, model
Adjust ginseng, model evaluation etc..Offline evaluation may be used (for example, AUC (Area under Curve, song to the method for model evaluation
Area under line), NDCG (Normalized Discounted Cumulative Gain, normalization lose storage gain)).
When being predicted using the recommendation machine learning model, believed according to userspersonal information, payment scene, payment for merchandise
Breath etc. is predicted to sort in real time.
The realization of this programme can be based on distributed platform and use MR (MapReduce, distributed computing system) and SQL
(Structured Query Language, structured query language) is realized, as Fig. 8 puts down for what this specification embodiment provided
The schematic diagram of rack structure.
Based on same thinking, this specification embodiment also provides a kind of electronic equipment, including：
At least one processor；And
The memory being connect at least one processor communication；Wherein,
The memory is stored with the instruction that can be executed by least one processor, and described instruction is by described at least one
A processor executes, so that at least one processor can：
For paying each time, user information and information to be paid are obtained；
According to the user information and the information to be paid, using the recommendation machine learning model of pre-training, to having
The means of payment sorts；
Recommend the means of payment to user according to ranking results.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the action recorded in detail in the claims or step can be come according to different from the sequence in embodiment
It executes and desired result still may be implemented.In addition, the process described in the accompanying drawings not necessarily require show it is specific suitable
Sequence or consecutive order could realize desired result.In some embodiments, multitasking and parallel processing be also can
With or it may be advantageous.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for device,
For electronic equipment, nonvolatile computer storage media embodiment, since it is substantially similar to the method embodiment, so description
It is fairly simple, the relevent part can refer to the partial explaination of embodiments of method.
Device that this specification embodiment provides, electronic equipment, nonvolatile computer storage media with method are corresponding
, therefore, device, electronic equipment, nonvolatile computer storage media also there is the Advantageous similar with corresponding method to imitate
Fruit, since the advantageous effects of method being described in detail above, which is not described herein again corresponding intrument,
The advantageous effects of electronic equipment, nonvolatile computer storage media.
In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example,
Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So
And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit.
Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause
This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device
(Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate
Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer
Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker
Dedicated IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " patrols
Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development,
And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language
(Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, such as ABEL
(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description
Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL
(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby
Hardware Description Language) etc., VHDL (Very-High-Speed are most generally used at present
Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also answer
This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages,
The hardware circuit for realizing the logical method flow can be readily available.
Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing
The computer for the computer readable program code (such as software or firmware) that device and storage can be executed by (micro-) processor can
Read medium, logic gate, switch, application-specific integrated circuit (Application Specific Integrated Circuit,
ASIC), the form of programmable logic controller (PLC) and embedded microcontroller, the example of controller includes but not limited to following microcontroller
Device：ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, are deposited
Memory controller is also implemented as a part for the control logic of memory.It is also known in the art that in addition to
Pure computer readable program code mode is realized other than controller, can be made completely by the way that method and step is carried out programming in logic
Controller is obtained in the form of logic gate, switch, application-specific integrated circuit, programmable logic controller (PLC) and embedded microcontroller etc. to come in fact
Existing identical function.Therefore this controller is considered a kind of hardware component, and to including for realizing various in it
The device of function can also be considered as the structure in hardware component.Or even, it can will be regarded for realizing the device of various functions
For either the software module of implementation method can be the structure in hardware component again.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play
It is any in device, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment
The combination of equipment.
For convenience of description, it is divided into various units when description apparatus above with function to describe respectively.Certainly, implementing this
The function of each unit is realized can in the same or multiple software and or hardware when specification one or more embodiment.
It should be understood by those skilled in the art that, this specification embodiment can be provided as method, system or computer program
Product.Therefore, this specification embodiment can be used complete hardware embodiment, complete software embodiment or combine software and hardware
The form of the embodiment of aspect.Moreover, it wherein includes that computer is available that this specification embodiment, which can be used in one or more,
It is real in the computer-usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) of program code
The form for the computer program product applied.
This specification is with reference to the method, equipment (system) and computer program product according to this specification embodiment
Flowchart and/or the block diagram describes.It should be understood that can be realized by computer program instructions every in flowchart and/or the block diagram
The combination of flow and/or box in one flow and/or box and flowchart and/or the block diagram.These computers can be provided
Processor of the program instruction to all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices
To generate a machine so that the instruction executed by computer or the processor of other programmable data processing devices generates use
In the dress for realizing the function of being specified in one flow of flow chart or multiple flows and/or one box of block diagram or multiple boxes
It sets.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, computing device includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology realizes information storage.Information can be computer-readable instruction, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), fast flash memory bank or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storages, magnetic tape cassette, tape magnetic disk storage or other magnetic storage apparatus
Or any other non-transmission medium, it can be used for storage and can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitory media), such as data-signal and carrier wave of modulation.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Including so that process, method, commodity or equipment including a series of elements include not only those elements, but also wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including described
There is also other identical elements in the process of element, method, commodity or equipment.
This specification can describe in the general context of computer-executable instructions executed by a computer, such as journey
Sequence module.Usually, program module include routines performing specific tasks or implementing specific abstract data types, program, object,
Component, data structure etc..Specification can also be put into practice in a distributed computing environment, in these distributed computing environments,
By executing task by the connected remote processing devices of communication network.In a distributed computing environment, program module can
With in the local and remote computer storage media including storage device.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality
For applying example, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to embodiment of the method
Part explanation.
The foregoing is merely this specification embodiments, are not intended to limit this application.For those skilled in the art
For, the application can have various modifications and variations.It is all within spirit herein and principle made by any modification, equivalent
Replace, improve etc., it should be included within the scope of claims hereof.
Claims (13)
1. a kind of means of payment recommends method, including：
For paying each time, user information and information to be paid are obtained；
According to the user information and the information to be paid, using the recommendation machine learning model of pre-training, to existing payment
Mode sorts；
Recommend the means of payment to user according to ranking results.
2. the method as described in claim 1 is specifically included to having means of payment sequence：
According to the user information, the means of payment that there is binding relationship with the user is determined；
It is ranked up for the means of payment determined；
Wherein, the user information includes：User's history payment information and userspersonal information；The information to be paid includes：
Scene information to be paid and merchandise news to be paid.
3. the method as described in claim 1, in the recommendation machine learning model using pre-training, to having means of payment sequence
Before, the method further includes：
According to default cleaning rule, the information to be paid is cleaned；
According to the information to be paid, corresponding characteristic to be paid is extracted.
4. the method as described in claim 1, pre-training recommends machine learning model, specifically includes：
Training user's information and training means of payment information are obtained, determines training user's information and the trained means of payment
Correspondence between information；
According to the correspondence, the degree of correlation of training user and the means of payment is determined；
Based on training user's information, the trained means of payment information and the degree of correlation, the training recommendation engineering
Practise model.
5. method as claimed in claim 4 is based on training user's information, the trained means of payment information and the phase
Guan Du, the training recommendation machine learning model, specifically includes：
Based on the user information, the trained means of payment information and the degree of correlation, cleans and extract corresponding characteristic
According to；
According to the characteristic, the training recommendation machine learning model.
6. method as claimed in claim 4, the recommendation machine learning model includes LTR sequence learning models.
7. a kind of means of payment recommendation apparatus, including：
First acquisition module obtains user information and information to be paid for paying each time；
Sorting module is right using the recommendation machine learning model of pre-training according to the user information and the information to be paid
Has means of payment sequence；
Recommending module recommends the means of payment according to ranking results to user.
8. device as claimed in claim 7, further includes：Second acquisition module,
Second acquisition module determines the means of payment for having binding relationship with the user according to the user information；
Wherein, the user information includes：User's history payment information and userspersonal information；The information to be paid includes：
Scene information to be paid and merchandise news to be paid.
9. device as claimed in claim 7, further includes：Feature processing block；
The feature processing block cleans the information to be paid according to default cleaning rule；
According to the information to be paid, corresponding characteristic to be paid is extracted.
10. device as claimed in claim 7, including：Model training module；
The model training module obtains training user's information and training means of payment information, determines training user's information
With the correspondence between the trained means of payment information；
According to the correspondence, the degree of correlation of training user and the means of payment is determined；
Based on training user's information, the trained means of payment information and the degree of correlation, the training recommendation engineering
Practise model.
11. device as claimed in claim 10, based on training user's information, the trained means of payment information and described
The degree of correlation, the training recommendation machine learning model, specifically includes：
Based on the user information, the trained means of payment information and the degree of correlation, cleans and extract corresponding characteristic
According to；
According to the characteristic, the training recommendation machine learning model.
12. device as claimed in claim 10, the recommendation machine learning model includes LTR sequence learning models.
13. a kind of electronic equipment, including：
At least one processor；And
The memory being connect at least one processor communication；Wherein,
The memory is stored with the instruction that can be executed by least one processor, and described instruction is by least one place
It manages device to execute, so that at least one processor can：
For paying each time, user information and information to be paid are obtained；
According to the user information and the information to be paid, using the recommendation machine learning model of pre-training, to existing payment
Mode sorts；
Recommend the means of payment to user according to ranking results.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810284452.5A CN108734460A (en) | 2018-04-02 | 2018-04-02 | A kind of means of payment recommends method, apparatus and equipment |
| CN202210010535.1A CN114418568A (en) | 2018-04-02 | 2018-04-02 | Payment mode recommendation method, device and equipment |
| PCT/CN2019/073924 WO2019192261A1 (en) | 2018-04-02 | 2019-01-30 | Payment mode recommendation method and device and equipment |
| TW108104364A TW201942826A (en) | 2018-04-02 | 2019-02-11 | Payment mode recommendation method and device and equipment |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810284452.5A CN108734460A (en) | 2018-04-02 | 2018-04-02 | A kind of means of payment recommends method, apparatus and equipment |
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| Application Number | Title | Priority Date | Filing Date |
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| CN202210010535.1A Division CN114418568A (en) | 2018-04-02 | 2018-04-02 | Payment mode recommendation method, device and equipment |
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| CN201810284452.5A Pending CN108734460A (en) | 2018-04-02 | 2018-04-02 | A kind of means of payment recommends method, apparatus and equipment |
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| CN202210010535.1A Pending CN114418568A (en) | 2018-04-02 | 2018-04-02 | Payment mode recommendation method, device and equipment |
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| CN (2) | CN114418568A (en) |
| TW (1) | TW201942826A (en) |
| WO (1) | WO2019192261A1 (en) |
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Also Published As
| Publication number | Publication date |
|---|---|
| CN114418568A (en) | 2022-04-29 |
| WO2019192261A1 (en) | 2019-10-10 |
| TW201942826A (en) | 2019-11-01 |
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