CN110033247B - Payment channel recommendation method and system - Google Patents
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Abstract
The application relates to the field of online payment, and discloses a payment channel recommendation method and a payment channel recommendation system, which can effectively achieve the capacity target of a payment channel and enable a user to have good payment experience. The method comprises the following steps: acquiring the actual ratio of the payment capacity of each channel in the last time window; according to the actual occupation ratio and the preset expected occupation ratio of the payment capacity of each channel in the current time window, adjusting the recommendation ratio of the payment capacity of each channel in the previous time window to obtain the recommendation ratio of the payment capacity of each channel in the current time window; and obtaining a payment channel recommendation result for the specified user according to the recommendation ratio of the current time window and the payment preference parameter of the specified user.
Description
Technical Field
The application relates to the field of online payment, in particular to a recommendation technology of a payment channel.
Background
E-commerce websites often have large promotions, each of which is a challenge to the payment system. One problem faced by payment systems is the risk of payment channel capacity, which, if the use of a user payment channel is not guided, may result in a large number of users intensively using a certain payment channel, exceeding the processing capacity of the payment channel, thereby failing to complete payment in time, resulting in poor user payment experience, and eventually possibly affecting sales.
Related public technologies are not found at present about how to provide recommendation suggestions of payment channels to users in real time and realize recommendation personalized drainage so as to break through the problem of peak capacity of the payment channels.
Disclosure of Invention
The application aims to provide a payment channel recommendation method and a payment channel recommendation system, which can effectively achieve the capacity target of a payment channel and enable a user to have good payment experience.
In order to solve the above problem, the present application discloses a payment channel recommendation method, including:
acquiring the actual ratio of the payment capacity of each channel in the last time window;
according to the actual occupation ratio and a preset expected occupation ratio of the payment capacity of each channel in the current time window, adjusting the recommendation ratio of the payment capacity of each channel in the previous time window to obtain the recommendation ratio of the payment capacity of each channel in the current time window;
obtaining a payment channel recommendation result of the appointed user according to the recommendation ratio of the current time window and the payment preference parameter of the appointed user; wherein the payment preference parameter is a parameter reflecting the degree of preference of a user for each payment channel that the user can use.
In a preferred embodiment, the adjusting the recommended ratio of the last time window further comprises:
and dividing the expected ratio of the payment capacity of each channel in the current time window by the actual ratio of the payment capacity of each channel in the previous time window, and multiplying the actual ratio by the recommended ratio of the payment capacity of each channel in the previous time window to obtain the recommended ratio of the payment capacity of each channel in the current time window.
In a preferred embodiment, the obtaining a recommendation result of a payment channel to a specified user according to the recommendation ratio of the current time window and a payment preference parameter of the specified user further includes:
for each channel, multiplying the recommendation ratio of the channel in the current time window by the payment preference parameter of the specified user to the channel to obtain the recommendation value of the specified user to the channel;
and taking the channel with the maximum recommendation value as a payment channel recommendation result for the specified user.
In a preferred embodiment, the payment preference parameter is obtained according to the payment behavior of the user in the last period of time and/or the setting of the user.
In a preferred embodiment, the step of obtaining the payment channel recommendation result for the specified user according to the recommendation ratio of the current time window and the payment preference parameter of the specified user is triggered when the specified user submits an order.
In a preferred example, after the obtaining of the recommendation result of the payment channel to the specified user, the method further includes: and sending the payment channel recommendation result to the terminal used by the specified user.
In a preferred embodiment, the length of the time window is in the range of 10 seconds to 15 seconds.
The application also discloses a payment channel recommendation system, includes:
the actual ratio acquisition module is used for acquiring the actual ratio of the payment capacity of each channel in the last time window;
the recommendation ratio adjusting module is used for adjusting the recommendation ratio of the payment capacity of each channel in the previous time window according to the actual ratio and the preset expected ratio of the payment capacity of each channel in the current time window to obtain the recommendation ratio of the payment capacity of each channel in the current time window;
the recommending module is used for obtaining a payment channel recommending result of the appointed user according to the recommending ratio of the current time window and the payment preference parameter of the appointed user; wherein the payment preference parameter is a parameter reflecting the degree of preference of a user for each payment channel that the user can use.
In a preferred example, the recommendation ratio adjustment module adjusts the recommendation ratio by: and dividing the expected ratio of the payment capacity of each channel in the current time window by the actual ratio of the payment capacity of each channel in the previous time window, and multiplying the actual ratio by the recommended ratio of the payment capacity of each channel in the previous time window to obtain the recommended ratio of the payment capacity of each channel in the current time window.
In a preferred embodiment, the recommending module further comprises:
the recommendation value operator module is used for multiplying the recommendation ratio of the channel in the current time window by the payment preference parameter of the specified user to the channel to obtain the recommendation value of the specified user to the channel;
and the comparison submodule is used for taking the channel with the maximum recommendation value as a payment channel recommendation result for the specified user.
In a preferred embodiment, the payment preference parameter is obtained according to the payment behavior of the user in the last period of time and/or the setting of the user.
In a preferred embodiment, the recommendation module is triggered to calculate a payment channel recommendation for the specified user when the specified user submits an order.
In a preferred embodiment, the payment channel recommendation system further comprises a sending module, configured to send the payment channel recommendation result to the terminal used by the specified user.
In a preferred embodiment, the length of the time window is in the range of 10 seconds to 15 seconds.
The application also discloses a payment channel recommendation system, includes:
a memory for storing computer executable instructions; and the number of the first and second groups,
a processor for implementing the steps in the method as described hereinbefore when executing the computer executable instructions.
The present application also discloses a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the steps in the method as described hereinbefore.
The method and the device for realizing the payment channel capacity can quickly and effectively achieve the capacity target of the payment channel, meanwhile, the user has good payment experience, real-time dynamic adjustment is achieved on the user experience and resource allocation, and more priori knowledge related to scenes is not needed.
The present specification describes a number of technical features distributed throughout the various technical aspects, and if all possible combinations of technical features (i.e. technical aspects) of the present specification are listed, the description is made excessively long. In order to avoid this problem, the respective technical features disclosed in the above summary of the invention of the present application, the respective technical features disclosed in the following embodiments and examples, and the respective technical features disclosed in the drawings may be freely combined with each other to constitute various new technical solutions (which are considered to have been described in the present specification) unless such a combination of the technical features is technically infeasible. For example, in one example, the feature a + B + C is disclosed, in another example, the feature a + B + D + E is disclosed, and the features C and D are equivalent technical means for the same purpose, and technically only one feature is used, but not simultaneously employed, and the feature E can be technically combined with the feature C, then the solution of a + B + C + D should not be considered as being described because the technology is not feasible, and the solution of a + B + C + E should be considered as being described.
Drawings
FIG. 1 is a schematic flow chart of a payment channel recommendation method according to a first embodiment of the present application
FIG. 2 is a schematic structural diagram of a payment channel recommendation system according to a second embodiment of the present application
Detailed Description
In the following description, numerous technical details are set forth in order to provide a better understanding of the present application. However, it will be understood by those skilled in the art that the technical solutions claimed in the present application may be implemented without these technical details and with various changes and modifications based on the following embodiments.
Description of partial concepts:
time window: refers to a period of time of a predetermined length. For example, 1 minute may be divided into 6 time windows in order of 10 seconds in length.
And (3) payment channel: that is, the payment channel, for example, the bank card issued by different banks is different payment channels, and different third party payment methods also belong to different payment channels. In embodiments of the present application, the payment channel may also be referred to simply as a channel.
The ratio of the payment capacity of each channel in a time window is as follows: each payment channel is used a proportion of the total number of payments in a time window.
The actual proportion of the payment capacity of each channel in the last time window is as follows: in the last time window, the actual number of times each payment channel was used is a proportion of the total number of payments.
The expected duty ratio of the payment capacity of each channel in the current time window is as follows: each payment channel is expected to be used a proportion of the total number of payments in the current time window.
Payment preference parameters: reflecting the payment channels available to the user and their usage preferences, for example, a user has A, B, C total payment channels, the usage preference of the user for the three payment channels can be represented by [ a, B, C ], [0.1,0.6,0.3], and a larger value represents that the user prefers to use the corresponding payment channel.
The inventors of the present application found that the challenge of personalized payment channel recommendations is that the system does not know what the requesting user at the next moment owns the payment instrument, and the payment habits vary greatly from user to user, considering only the capacity target will seriously affect the user payment experience. Therefore, the method and the system for recommending the channel condition integrate the personalized payment habits of the users, the payment factors of the users at that time and the channel condition to be personalized recommendation.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
A first embodiment of the present application relates to a payment channel recommendation method, a flow of which is shown in fig. 1, and the method includes the following steps:
in step 101, the actual proportion of the payment capacity of each channel in the last time window is obtained. In one embodiment, the length of the time window may be in the range of 10 seconds to 15 seconds. In other embodiments, in different scenarios, the length of the time window may be set according to actual conditions, and is not limited to the range of 10 to 15 seconds.
And then, step 102 is carried out, the recommendation ratio of the payment capacity of each channel in the last time window is adjusted according to the obtained actual ratio and the preset expected ratio of the payment capacity of each channel in the current time window, and the recommendation ratio of the payment capacity of each channel in the current time window is obtained.
Optionally, the step further comprises: and dividing the expected ratio of the payment capacity of each channel in the current time window by the actual ratio of the payment capacity of each channel in the previous time window, and multiplying the actual ratio by the recommended ratio of the payment capacity of each channel in the previous time window to obtain the recommended ratio of the payment capacity of each channel in the current time window.
In one embodiment, assuming that the current time window is the nth time window, and n is a positive integer, the actual proportion of each channel's paid capacity in the last time window may be represented by a vector Vn-1The expected proportion of each channel payment capacity in the current time window can be expressed by a vector UnShowing that the recommended ratio of the payment capacity of each channel in the last time window and the current time window is X respectivelyn-1And XnThe lengths of the vectors are the number k of the payment channels, the ith element in the vector represents the ith channel, k is a positive integer, and i is more than 0 and less than or equal to k. Where U and X are both k in length. Then, Xn＝Xn-1·Un/Vn-1The formula expresses for Xn-1And UnMultiplying each element at the corresponding position, and dividing by Vn-1Each element in the corresponding position in the sequence to obtain Xn。
And then, entering step 103, and obtaining a payment channel recommendation result for the specified user according to the recommendation ratio of the current time window and the payment preference parameter of the specified user. Optionally, the step further comprises: for each channel, multiplying the recommendation ratio of the channel in the current time window by the payment preference parameter of the specified user to the channel to obtain the recommendation value of the specified user to the channel; and taking the channel with the maximum recommendation value as a payment channel recommendation result for the specified user.
The payment preference parameter is a parameter that reflects the degree of preference a user has for each payment channel that he or she can use. Optionally, the payment preference parameter is derived from the payment behavior of the user in the last period of time and/or the user's setting. The payment behavior includes payment record, payment success, payment failure, last payment success time from present time, and the like. For example, the number of payment preferences may be determined based on the number of times the user has used various payment channels within the last month. As another example, the payment order of the various available payment channels set by the user may also be used as a factor influencing the payment preference parameter, and the payment channel with the previous payment order has a larger weight in the payment preference parameter.
The steps 101, 102 and 103 are executed in each time window, the expected ratio of the payment capacity of each channel in the current time window is continuously calculated in an iterative manner, and personalized payment channel recommendation results are given to each user needing payment in the current time window, wherein each user needing payment can be regarded as the specified user.
In one embodiment, steps 101 and 102 are performed at the beginning of each time window to obtain the recommended ratio of the paid capacity of each channel in the current time window. Step 103 is triggered when the user submits the order, and the user who submits the order can be used as the designated user, and the user payment channel recommendation result is obtained through calculation in step 103. Steps 101, 102, and 103 are performed at a cloud (or a server, etc.), the cloud sends the payment channel recommendation result to a terminal (e.g., a smart phone or a laptop, etc.) used by the user, the user submits an order and enters a payment interface, and information related to the payment channel recommendation result can be displayed in the payment interface (e.g., a recommended payment channel is displayed, or related prompt information is displayed).
Through the technical scheme, the capacity capability of the payment channel and the payment habit of the user can be considered.
In order to better understand the technical solution of the present application, the following description is given with reference to a specific example, in which the listed details are mainly for the sake of understanding and are not intended to limit the scope of the present application.
Assuming that each time window is 10 seconds in length, there are a total of A, B, C payment channels. In the current time window (assumed to be the nth time window), the cloud receives requests of three users, i.e., user 1, user 2, and user 3.
Recommendation factor X of each payment channel in last time windown-1＝[0.4,0.4,0.2]The expected ratio of each payment channel in the current time window is Un＝[0.5,0.4,0.1]The actual ratio of the payment capacity of each channel in the last time window is Vn-1＝[0.4,0.4,0.2]. Then the recommendation factor X for each payment channel of the current time windown-1＝[0.4*0.5/0.4,0.4*0.4/0.4,0.2*0.1/0.2]＝[0.5,0.4,0.1]
Assuming that the payment channels owned by the three users and the corresponding payment preference parameters are [ a, B, C ] ═ 0.1,0.6,0.3, [ C ] ═ 1, [ a, B ] ═ 0.8,0.2, respectively, then the payment channels of the three users are recommended as:
user 1 max (0.5 x 0.1,0.4 x 0.6,0.3 x 0.1) recommendations B
User 2 max (0, 0, 0.1 x 1) recommendation C
User 3 max (0.5 x 0.8,0.4 x 0.1) recommendations a
Where max is a function of the maximum value.
A second embodiment of the present application relates to a payment channel recommendation system, the structure of which is shown in fig. 2, the payment channel recommendation system including:
and an actual ratio obtaining module 201, configured to obtain an actual ratio of the payment capacity of each channel in the last time window. Optionally, the length of the time window is in the range of 10 seconds to 15 seconds. Alternatively, in different scenarios, the length of the time window may be set according to actual conditions, and is not limited to the range of 10 to 15 seconds.
And the recommendation ratio adjusting module 202 is configured to adjust the recommendation ratio of the payment capacity of each channel in the previous time window according to the actual ratio and a preset expected ratio of the payment capacity of each channel in the current time window, so as to obtain the recommendation ratio of the payment capacity of each channel in the current time window.
Optionally, the recommendation ratio adjusting module adjusts the recommendation ratio by: and dividing the expected ratio of the payment capacity of each channel in the current time window by the actual ratio of the payment capacity of each channel in the previous time window, and multiplying the actual ratio by the recommended ratio of the payment capacity of each channel in the previous time window to obtain the recommended ratio of the payment capacity of each channel in the current time window.
And the recommending module 203 is used for obtaining a payment channel recommending result of the specified user according to the recommending ratio of the current time window and the payment preference parameter of the specified user. Wherein the payment preference parameter is a parameter reflecting the degree of preference of a user for each payment channel that the user can use. Optionally, the payment preference parameter is derived from the payment behavior of the user in the last period of time and/or the user's setting.
Optionally, the recommendation module further comprises: and the recommendation value operator module is used for multiplying the recommendation ratio of the channel in the current time window by the payment preference parameter of the specified user to the channel to obtain the recommendation value of the specified user to the channel. And the comparison submodule is used for taking the channel with the maximum recommendation value as a payment channel recommendation result for the specified user.
Optionally, the recommendation module is triggered to calculate a payment channel recommendation for the specified user when the specified user submits an order. The system may further include a transmitting module for transmitting the payment channel recommendation result to a terminal used by the designated user.
The first embodiment is a method embodiment corresponding to the present embodiment, and the technical details in the first embodiment may be applied to the present embodiment, and the technical details in the present embodiment may also be applied to the first embodiment.
It should be noted that, as will be understood by those skilled in the art, the implementation functions of the modules shown in the embodiment of the payment channel recommendation system can be understood by referring to the related description of the payment channel recommendation method. The functions of the modules shown in the embodiment of the payment channel recommendation system can be realized by a program (executable instructions) running on a processor, and can also be realized by a specific logic circuit. The payment channel recommendation system in the embodiment of the present application, if implemented in the form of a software function module and sold or used as an independent product, may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the computer-executable instructions implement the method embodiments of the present application. Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable storage medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
In addition, the embodiment of the application also provides a payment channel recommendation system, which comprises a memory for storing computer executable instructions and a processor; the processor is configured to implement the steps of the method embodiments described above when executing the computer-executable instructions in the memory. The Processor may be a Central Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or the like. The aforementioned memory may be a read-only memory (ROM), a Random Access Memory (RAM), a Flash memory (Flash), a hard disk, or a solid state disk. The steps of the method disclosed in the embodiments of the present invention may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
It is noted that, in the present patent application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element. In the present patent application, if it is mentioned that a certain action is executed according to a certain element, it means that the action is executed according to at least the element, and two cases are included: performing the action based only on the element, and performing the action based on the element and other elements. The expression of a plurality of, a plurality of and the like includes 2, 2 and more than 2, more than 2 and more than 2.
All documents mentioned in this application are to be considered as being incorporated in their entirety into the disclosure of this application so as to be subject to modification as necessary. It should be understood that the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present disclosure should be included in the scope of protection of one or more embodiments of the present disclosure.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
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| PCT/CN2019/124092 WO2020140697A1 (en) | 2019-01-04 | 2019-12-09 | Payment channel recommendation method and system therefor |
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2019
- 2019-01-04 CN CN201910007581.4A patent/CN110033247B/en active Active
- 2019-08-26 TW TW108130392A patent/TWI712970B/en not_active IP Right Cessation
- 2019-12-09 WO PCT/CN2019/124092 patent/WO2020140697A1/en not_active Ceased
Also Published As
| Publication number | Publication date |
|---|---|
| CN110033247A (en) | 2019-07-19 |
| TW202026975A (en) | 2020-07-16 |
| TWI712970B (en) | 2020-12-11 |
| WO2020140697A1 (en) | 2020-07-09 |
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