CN110874737A - Payment mode recommendation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a payment mode recommendation method, a payment mode recommendation device, electronic equipment and a storage medium, wherein the payment mode recommendation method comprises the following steps: routing users to be paid to a first user group or a second user group, wherein the users to be paid in the first user group recommend payment modes according to model prediction, and the users to be paid in the second user group recommend payment modes according to a preset recommendation mode; and adjusting the number of first users routed to the first user group and/or the number of second users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first predetermined time period, wherein the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the number of the first users to the number of the second users. The method and the device provided by the invention improve the accuracy of payment recommendation.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a payment mode recommendation method and device, electronic equipment and a storage medium.
Background
In a traditional payment cash-receiving page, a user needs to manually select a payment mode for payment, and under many conditions, the user can know whether to use a next-level sub-payment mode by clicking and expanding a parent payment mode. The more cumbersome the operation, the poorer the shopping experience for the user. In order to solve this problem, the following technical means are generally available: for a new user, counting the utilization rate of the payment mode within a period of time, and displaying the utilization rate to the user from high to low; and for the old user, directly selecting the last payment mode as the recommended payment mode.
However, for a new user, personalization cannot be achieved, and the display form of thousands of people and thousands of faces can be realized; for the old user, the recommendation method is not comprehensive, and the last payment mode is only the choice of the user for getting the mood. The recommendation accuracy of the payment method in the prior art needs to be improved.
Disclosure of Invention
The present invention is directed to a method, an apparatus, an electronic device, and a storage medium for recommending a payment method, which overcome the limitations and disadvantages of the related art, and thereby overcome one or more of the problems due to the limitations and disadvantages of the related art, at least to some extent.
According to an aspect of the present invention, there is provided a payment method recommendation method, including:
routing users to be paid to a first user group or a second user group, wherein the users to be paid in the first user group recommend payment modes according to model prediction, and the users to be paid in the second user group recommend payment modes according to a preset recommendation mode; and
and adjusting the number of first users routed to the first user group and/or the number of second users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first preset time period, wherein the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the number of the first users to the number of the second users.
Optionally, the method for recommending payment by a user to be paid in the first user group according to model prediction includes:
and if the user to be paid is a user with a historical payment record, recommending a payment mode to the user according to a first prediction model, wherein the input of the first prediction model at least comprises historical payment record data of the user.
Optionally, the method for recommending payment by a user to be paid in the first user group according to model prediction includes:
and if the user to be paid is a user without a historical payment record, recommending a payment mode to the user according to a second prediction model, wherein the input of the second prediction model at least comprises the current order data to be paid of the user.
Optionally, the recommending the payment method according to the preset recommendation method by the user to be paid in the second user group includes:
and if the user to be paid is the user with the historical payment record, recommending the last payment mode of the user to the user or recommending the payment mode with the maximum use times in the historical payment record of the user to the user.
Optionally, the recommending the payment method according to the preset recommendation method by the user to be paid in the second user group includes:
and if the user to be paid is a user without a historical payment record, recommending the payment mode with the maximum total use times to the user within a second preset time period.
Optionally, the first recommendation accuracy rate and the second recommendation accuracy rate P are N/N, where N is a number of times that the recommended payment method in the first predetermined time period is consistent with the actual payment method of the user, and N is a total number of times of payment in the first predetermined time period.
Optionally, in the recommendation data to the user to be paid and the actual payment data of the user to be paid:
and when the identification of the user to be paid and the identification of the order paid by the user to be paid are the same, matching the corresponding recommended payment mode with the actual payment mode of the user to determine whether the recommended payment mode is consistent with the actual payment mode of the user.
Optionally, when a plurality of ranked payment methods are recommended to the user to be paid, n is the number of times that the recommended first-ranked payment method is consistent with the actual payment method of the user within the first predetermined time period.
Optionally, the method further comprises:
and determining the user group with high accuracy in the third recommended accuracy rate of routing the user to be paid to the first user group and the fourth recommended accuracy rate of routing the user to be paid to the second user group within a third preset time period as the user group to which the user to be paid is to be routed currently.
According to another aspect of the present invention, there is also provided a payment method recommendation apparatus, including:
the system comprises a routing module, a payment module and a payment module, wherein the routing module is used for routing users to be paid to a first user group or a second user group, the users to be paid in the first user group recommend payment modes according to model prediction, and the users to be paid in the second user group recommend payment modes according to a preset recommendation mode; and
the adjusting module is used for adjusting the number of first users routed to the first user group and/or the number of second users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first preset time period, and the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the number of the first users to the number of the second users.
According to still another aspect of the present invention, there is also provided an electronic apparatus, including: a processor; a memory having stored thereon a computer program which, when executed by the processor, performs the steps as described above.
According to yet another aspect of the present invention, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps as described above.
Compared with the prior art, the invention has the advantages that:
the method comprises the steps that users to be paid are routed to one of two user groups, one user group conducts payment recommendation in a model prediction mode, the other user group conducts payment recommendation in a preset recommendation mode, and the routing of the users to be paid is adjusted according to the accuracy of the two user groups.
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The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 shows a flowchart of a payment means recommendation method according to an embodiment of the present invention.
Fig. 2 shows a timing diagram of a recommended payment method according to an embodiment of the invention.
FIG. 3 shows a timing diagram of recommendation accuracy calculation according to an embodiment of the present invention.
Fig. 4 illustrates a timing diagram for adjusting the proportion of routes routed to the first group of users and the second group of users based on a recommended accuracy rate, according to an embodiment of the invention.
Fig. 5 is a block diagram illustrating a payment means recommendation apparatus according to an embodiment of the present invention.
Fig. 6 is a block diagram illustrating a payment means recommendation apparatus according to an embodiment of the present invention.
Fig. 7 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the invention.
Fig. 8 schematically illustrates an electronic device in an exemplary embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. 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 devices and/or microcontroller devices.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a flowchart of a payment means recommendation method according to an embodiment of the present invention. Referring to fig. 1, the payment method recommendation method includes the steps of:
step S110: routing users to be paid to a first user group or a second user group, wherein the users to be paid in the first user group recommend payment modes according to model prediction, and the users to be paid in the second user group recommend payment modes according to a preset recommendation mode; and
step S120: and adjusting the number of first users routed to the first user group and/or the number of second users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first preset time period, wherein the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the number of the first users to the number of the second users.
In the payment method recommendation method of the exemplary embodiment of the invention, the user to be paid is routed to one user group of two user groups, wherein one user group adopts a model prediction mode to perform payment recommendation, the other user group adopts a preset recommendation mode to perform payment recommendation, and the routing of the user to be paid is adjusted according to the accuracy of the two user groups, so that the recommendation accuracy of the payment method recommended to the user is improved, redundant payment selection operations of the user are reduced, the user experience is improved, and the conversion rate is improved.
In various embodiments of the present invention, the payment method includes, but is not limited to, payment by each payment platform, debit card payment, credit card payment, payment by the mobile device itself (such as applet pay).
Specifically, embodiments of the present invention will be described below with reference to fig. 2 to 4, respectively.
Referring first to fig. 2, fig. 2 shows a timing diagram of a recommended payment method according to an embodiment of the invention. The timing interaction between the recommendation interface 210, the routing interface 220, the preset recommendation interface 230, and the model recommendation interface 240 is illustrated in fig. 2.
In step S201, the recommendation interface 210 interacts with the routing interface 220 to route the user to be paid to the first group of users or the second group of users.
Specifically, in a specific embodiment, the user name of the user to be paid is made to obtain the hash value H of the user name of the user to be paid via a mumeurhash (unencrypted hash function) algorithm, and if the routing ratio of the current first user group and the current second user group is n: m, when h%. m < n, the current user to be paid is routed to the first user group; and when h%. m is larger than or equal to n, routing the current user to be paid to the second user group. Therefore, the invention can realize a routing algorithm based on the user name of the user to be paid according to the preset proportion.
In step S202, the recommendation interface 210 determines to which group of users the user to be paid is routed.
If the user to be paid is routed to the first user group, step S203 is further executed at the recommendation interface 210 to determine whether the user to be paid has a historical payment record (the historical payment record may be only the historical payment record of the user of the current payment platform, or the historical payment records of the users of multiple payment platforms).
If the user to be paid is a user without a history payment record (i.e. a new user), the recommending interface 210 and the model recommending interface 240 interactively execute step S204, and recommend a payment mode to the user according to a second prediction model, where the input of the second prediction model at least includes data of a current order to be paid of the user. The current to-be-paid order data may include, for example, location information, merchandise information, time information, and the like.
If the user to be paid is a user with a historical payment record (i.e. an old user), the recommendation interface 210 and the model recommendation interface 240 interactively execute step S205, and recommend a payment method to the user according to a first prediction model, where the input of the first prediction model at least includes historical payment record data of the user. The historical payment record data may include, for example, historical payment methods, historical payment times, historical payment location information, historical payment merchandise information, etc. over a period of time. In some specific embodiments, the input of the first predictive model may further include current to-be-paid order data. The invention is not limited thereto.
The first prediction model and the second prediction model may be any machine-learned prediction model such as a BP neural network prediction model, a regression prediction model, and the like, which is not limited in the present invention. The first prediction model and the second prediction model can be trained off-line models, and can be optimized and updated according to actual prediction conditions. Furthermore, the prediction model generates a prediction value for each payment mode, and can also sort the payment modes according to the prediction value and recommend the payment modes according to the sorted payment modes.
If the user to be paid is routed to the second user group, step S206 is further executed at the recommendation interface 210 to determine whether the user to be paid has a historical payment record (the historical payment record may be only the historical payment record of the user of the current payment platform, or the historical payment records of the users of multiple payment platforms).
If the user to be paid is a user without a history payment record (i.e. a new user), the recommendation interface 210 and the preset recommendation interface 230 interactively execute step S207, and recommend the payment method with the maximum total number of times of use within the second predetermined time period to the user. For example, the total number of uses of the debit-card payment means is the largest in the first 10 minutes, and the debit-card payment means is recommended to the user to be paid. For another example, the payment methods may be ranked at most by the total number of times of use in the second predetermined time period, and the recommendation of the payment methods may be performed in the ranked order.
If the user to be paid is a user with a historical payment record (i.e. an old user), the recommendation interface 210 and the preset recommendation interface 230 interactively execute step S208, and recommend the last payment method of the user to the user or recommend the payment method with the largest number of times used in the historical payment record of the user to the user. For example, if the user last adopts the debit card payment method to pay, the debit card payment method is recommended to the user this time. For another example, if the user has paid for the most amount of time using the debit-card payment method in the previous week, the debit-card payment method is recommended to the user to be paid. For another example, the payment manners may be ranked at most according to the number of times the user uses each payment manner within a predetermined time period, and the recommendation of the payment manners may be performed in the ranking order.
Further, step S203 and step S206 may be combined into the same step, and determined before interacting with different interfaces.
Referring now to FIG. 3, FIG. 3 illustrates a timing diagram of recommendation accuracy calculation according to an embodiment of the present invention. The time-sequential interaction between the recommendation monitoring module 310, the payment monitoring module 320, and the recommendation interface 330 is shown in fig. 3. The steps in the recommendation interface 330 may also be performed in the payment interface, but the invention is not limited thereto.
First, the recommendation monitoring module 310 interacts with the recommendation interface 330 to perform step S301, and the recommendation monitoring module 310 sends recommendation data within a first predetermined time period (for example, 1 minute, 5 minutes, 10 minutes, and the like, which is not limited by the invention) to the recommendation interface 330. In embodiments where only one payment method is recommended, the recommendation data provides only the one recommended payment method. In the embodiment of recommending according to the ranking order, the recommendation data only provides the payment method ranked first, in other words, in the calculation of the subsequent recommendation accuracy, only the payment method ranked first is adopted to be matched with the actual payment method.
The payment monitoring module 320 interacts with the recommendation interface 330 to perform step S302, and the payment monitoring module 320 sends the payment data within a first predetermined time period (for example, 1 minute, 5 minutes, 10 minutes, and the like, which is not limited by the invention) to the recommendation interface 330. The payment data is the payment mode actually selected by each user.
The recommendation interface 330 performs step S303 to calculate a first recommendation accuracy rate of the first user group and a second recommendation accuracy rate of the second user group within a first predetermined time period.
In particular, the first recommendation accuracy rate P1＝Pf1+Po1＝nf1/Nf1+no1/No1Wherein P isf1Recommendation accuracy, n, for new users in the first group of usersf1The number of times that the recommended payment mode of the new user in the first user group is consistent with the actual payment mode of the user within a first preset time period, Nf1The total payment times of the new users in the first user group in a first preset time period; po1Recommendation accuracy, n, for old users in the first group of usersf1The number of times that the recommended payment mode of the old users in the first user group is consistent with the actual payment mode of the users within the first preset time period, Nf1The total number of payments made for the old users in the first group of users over the first predetermined period of time.
The second recommendation accuracy rate P2＝Pf2+Po2＝nf2/Nf2+no2/No2Wherein P isf2Recommendation accuracy for new users in the second user group, nf2The number of times that the recommended payment mode of the new user in the second user group in the first preset time period is consistent with the actual payment mode of the user, Nf2For new users in the second user group in the first subscriptionTotal number of payments over a period of time; po2Recommendation accuracy for old users in the second user group, nf2The number of times that the recommended payment mode of the old users in the second user group is consistent with the actual payment mode of the users within the first preset time period, Nf2The total number of payments made by the old users in the second group of users over the first predetermined period of time.
The present embodiment shows a procedure of calculating the recommendation accuracy rate based on the recommendation data and the payment data for the first predetermined period of time. In some variations, the recommendation accuracy may be calculated in real time according to whether the recommendation data and the payment data sent by each monitoring module in real time are consistent, and the recommendation accuracy of the user group in which the user is located is calculated. Specifically, in the recommendation data to the user to be paid and the actual payment data of the user to be paid: and when the identification of the user to be paid and the identification of the order paid by the user to be paid are the same, matching the corresponding recommended payment mode with the actual payment mode of the user to determine whether the recommended payment mode is consistent with the actual payment mode of the user.
Further, the step S301 and the step S302 may be executed synchronously or in an alternative order, which is not limited in the present invention.
Referring now to fig. 4, fig. 4 illustrates a timing diagram for adjusting a ratio of routing to a first group of users and a second group of users based on a recommended accuracy rate, according to an embodiment of the present invention. The timing interaction of the routing interface 410, and the recommendation module 420 is shown in fig. 4. The steps in the recommendation interface 420 may also be performed in the payment interface, but the invention is not limited thereto.
First, step S401 is performed at the recommendation interface 420, and a ratio of the first recommendation accuracy rate and the second recommendation accuracy rate is calculated according to the first recommendation accuracy rate and the second recommendation accuracy rate calculated at step S303 in fig. 3.
Then, the recommendation interface 420 transmits the calculated ratio of the first recommendation accuracy rate and the second recommendation accuracy rate to the routing interface 410 to perform step S402.
Finally, the routing interface 410 executes step S403, and adjusts the number of first users routed to the first user group and/or the number of second users routed to the second user group according to the first recommended accuracy of the first user group and the second recommended accuracy of the second user group within a first predetermined time period, where a ratio of the recommended accuracy to the second recommended accuracy is positively correlated with a ratio of the number of first users to the number of second users.
Specifically, the routing interface 410 may initially preset, for example, that the ratio of the number of the first users to the number of the second users is 1: 1, then according to a ratio (for example, 2: 1) of a first recommended accuracy rate of a first user group and a second recommended accuracy rate of a second user group in a first predetermined time period, adjusting the ratio of the number of routes to the first user and the second user to be 2: 1. in other words, after the first predetermined period of time, the routing interface 410 updates the routing table by 2: a ratio of 1 routes the user to be paid to the first group of users and the second group of users. Further, after the next first predetermined time period, the routing interface 410 may again update the ratio of the routing of the user to be paid to the first user group and the second user group according to the ratio of the first recommended accuracy rate of the first user group and the second recommended accuracy rate of the second user group.
In yet another embodiment of the present invention, when the ratio of the first recommended accuracy of the first user group and the second recommended accuracy of the second user group mapped to the spatial acquisition accuracy of [ -1,1] corresponds to the spatial value of [ -1,1] being x, the ratio f (x) of the number of first users routed to the first user group and the number of second users routed to the second user group may be calculated as follows:
f(x)＝1/[1+e(-5x)]，x∈[-1,1]。
therefore, through the specific adjustment algorithm, the functional relation between the ratio of the first recommended accuracy of the first user group and the second recommended accuracy of the second user group and the routing proportion is optimized.
The foregoing is merely an exemplary illustration of various embodiments of the present invention, and the present invention is not limited thereto.
The following describes a payment method recommendation apparatus provided by the present invention with reference to fig. 5. Fig. 5 is a block diagram illustrating a payment means recommendation apparatus according to an embodiment of the present invention. The payment method recommendation apparatus 500 includes a routing module 510 and an adjustment module 520.
The routing module 510 is configured to route users to be paid to a first user group or a second user group, where the users to be paid in the first user group recommend a payment method according to model prediction, and the users to be paid in the second user group recommend a payment method according to a preset recommendation method; and
the adjusting module 520 is configured to adjust a first number of users routed to the first user group and/or a second number of users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first predetermined time period, where a ratio of the recommended accuracy and the second recommended accuracy is positively correlated with a ratio of the first number of users and the second number of users.
In the payment method recommendation device of the exemplary embodiment of the invention, the user to be paid is routed to one user group of two user groups, wherein one user group adopts a model prediction mode to perform payment recommendation, the other user group adopts a preset recommendation mode to perform payment recommendation, and the routing of the user to be paid is adjusted according to the accuracy of the two user groups, so that the recommendation accuracy of the payment method recommended to the user is improved, redundant payment selection operations of the user are reduced, the user experience is improved, and the conversion rate is improved.
Referring now to fig. 6, fig. 6 is a block diagram illustrating a payment means recommendation apparatus according to an embodiment of the present invention. The payment method recommendation apparatus 600 includes a routing module 610, an adjustment module 620, a first prediction model recommendation module 630, a second prediction model recommendation module 640, a first preset recommendation module 650, and a second preset recommendation module 660. The routing module 610, the adjustment module 620 and the routing module 510 and the adjustment module 520 have the same function.
In the first user group, if the user to be paid is a user with a historical payment record (i.e. an old user), the first prediction model recommendation module 630 recommends a payment mode to the user according to a first prediction model, and the input of the first prediction model at least comprises the historical payment record data of the user. The historical payment record data may include, for example, historical payment methods, historical payment times, historical payment location information, historical payment merchandise information, etc. over a period of time. In some specific embodiments, the input of the first predictive model may further include current to-be-paid order data. The invention is not limited thereto.
In the first user group, if the user to be paid is a user without a history payment record (i.e. a new user), the second prediction model recommendation module 640 recommends a payment mode to the user according to a second prediction model, where the input of the second prediction model at least includes current order data to be paid of the user. The current to-be-paid order data may include, for example, location information, merchandise information, time information, and the like.
In the second user group, if the user to be paid is a user with a historical payment record (i.e. an old user), the first preset recommendation module 650 recommends the last payment method of the user to the user or recommends the payment method with the largest number of times used in the historical payment record of the user to the user. For example, if the user last adopts the debit card payment method to pay, the debit card payment method is recommended to the user this time. For another example, if the user has paid for the most amount of time using the debit-card payment method in the previous week, the debit-card payment method is recommended to the user to be paid. For another example, the payment manners may be ranked at most according to the number of times the user uses each payment manner within a predetermined time period, and the recommendation of the payment manners may be performed in the ranking order.
In the second user group, if the user to be paid is a user without a history payment record (i.e., a new user), the second preset recommendation module 660 recommends the payment method with the largest total number of times of use within the second predetermined time period to the user. For example, the total number of uses of the debit-card payment means is the largest in the first 10 minutes, and the debit-card payment means is recommended to the user to be paid. For another example, the payment methods may be ranked at most by the total number of times of use in the second predetermined time period, and the recommendation of the payment methods may be performed in the ranked order.
In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by, for example, a processor, can implement the steps of the electronic prescription flow processing method described in any one of the above embodiments. In some possible embodiments, aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of this specification, when the program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The 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 tenant computing device, partly on the tenant device, as a stand-alone software package, partly on the tenant computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing devices may be connected to the tenant 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 an exemplary embodiment of the invention, there is also provided an electronic device that may include a processor and a memory for storing executable instructions of the processor. Wherein the processor is configured to execute the steps of the electronic prescription flow processing method in any one of the above embodiments via execution of the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 800 according to this embodiment of the invention is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components (including the memory unit 820 and the processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code executable by the processing unit 810 to cause the processing unit 810 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 810 may perform the steps shown in fig. 1.
The memory unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM)8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The memory unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 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 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a tenant to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 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 860. The network adapter 860 may communicate with other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, 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 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 may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above-mentioned electronic prescription flow processing method according to the embodiment of the present invention.
Compared with the prior art, the invention has the advantages that:
the method comprises the steps that users to be paid are routed to one of two user groups, one user group conducts payment recommendation in a model prediction mode, the other user group conducts payment recommendation in a preset recommendation mode, and the routing of the users to be paid is adjusted according to the accuracy of the two user groups.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (12)
1. A payment method recommendation method is characterized by comprising the following steps:
routing users to be paid to a first user group or a second user group, wherein the users to be paid in the first user group recommend payment modes according to model prediction, and the users to be paid in the second user group recommend payment modes according to a preset recommendation mode; and
and adjusting the number of first users routed to the first user group and/or the number of second users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first preset time period, wherein the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the number of the first users to the number of the second users.
2. The payment method recommendation method of claim 1, wherein the model-predictive recommendation of payment methods by the users to be paid in the first user group comprises:
and if the user to be paid is a user with a historical payment record, recommending a payment mode to the user according to a first prediction model, wherein the input of the first prediction model at least comprises historical payment record data of the user.
3. The payment method recommendation method of claim 1, wherein the model-predictive recommendation of payment methods by the users to be paid in the first user group comprises:
and if the user to be paid is a user without a historical payment record, recommending a payment mode to the user according to a second prediction model, wherein the input of the second prediction model at least comprises the current order data to be paid of the user.
4. The payment method recommendation method of claim 1, wherein the recommending a payment method by the user to be paid in the second user group according to a preset recommendation method comprises:
and if the user to be paid is the user with the historical payment record, recommending the last payment mode of the user to the user or recommending the payment mode with the maximum use times in the historical payment record of the user to the user.
5. The payment method recommendation method of claim 1, wherein the recommending a payment method by the user to be paid in the second user group according to a preset recommendation method comprises:
and if the user to be paid is a user without a historical payment record, recommending the payment mode with the maximum total use times to the user within a second preset time period.
6. The payment method recommendation method of claim 1, wherein the first recommendation accuracy rate and the second recommendation accuracy rate P ═ N/N, where N is a number of times that the recommended payment method is consistent with the user's actual payment method in the first predetermined period of time, and N is a total number of payments in the first predetermined period of time.
7. The payment means recommendation method of claim 6, wherein in the recommendation data to the user to be paid and the actual payment data of the user to be paid:
and when the identification of the user to be paid and the identification of the order paid by the user to be paid are the same, matching the corresponding recommended payment mode with the actual payment mode of the user to determine whether the recommended payment mode is consistent with the actual payment mode of the user.
8. The payment means recommendation method of claim 6, wherein when a plurality of ranked payment means are recommended to the user to be paid, n is the number of times that the recommended ranked first payment means is consistent with the user's actual payment means within the first predetermined time period.
9. The payment method recommendation method of claim 1, further comprising:
and determining the user group with high accuracy in the third recommended accuracy rate of routing the user to be paid to the first user group and the fourth recommended accuracy rate of routing the user to be paid to the second user group within a third preset time period as the user group to which the user to be paid is to be routed currently.
10. A payment method recommendation device, comprising:
the system comprises a routing module, a payment module and a payment module, wherein the routing module is used for routing users to be paid to a first user group or a second user group, the users to be paid in the first user group recommend payment modes according to model prediction, and the users to be paid in the second user group recommend payment modes according to a preset recommendation mode; and
the adjusting module is used for adjusting the number of first users routed to the first user group and/or the number of second users routed to the second user group according to a first recommended accuracy of the first user group and a second recommended accuracy of the second user group within a first preset time period, and the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the number of the first users to the number of the second users.
11. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory having stored thereon a computer program which, when executed by the processor, performs the steps of any of claims 1 to 9.
12. A storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of any of claims 1 to 9.
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| CN201811021931.4A CN110874737A (en) | 2018-09-03 | 2018-09-03 | Payment mode recommendation method and device, electronic equipment and storage medium |
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| CN201811021931.4A CN110874737A (en) | 2018-09-03 | 2018-09-03 | Payment mode recommendation method and device, electronic equipment and storage medium |
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