CN110874737B - Payment mode recommendation method and device, electronic equipment and storage medium - Google Patents
Payment mode recommendation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN110874737B CN110874737B CN201811021931.4A CN201811021931A CN110874737B CN 110874737 B CN110874737 B CN 110874737B CN 201811021931 A CN201811021931 A CN 201811021931A CN 110874737 B CN110874737 B CN 110874737B
- Authority
- CN
- China
- Prior art keywords
- user
- payment
- paid
- user group
- recommendation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 100
- 230000002596 correlated effect Effects 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 230000000875 corresponding effect Effects 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 12
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000005111 flow chemistry technique Methods 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004883 computer application Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
- G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
- G06Q20/322—Aspects of commerce using mobile devices [M-devices]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Computer Networks & Wireless Communication (AREA)
- Accounting & Taxation (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention provides a payment mode recommending method, a device, electronic equipment and a storage medium, wherein the payment mode recommending method comprises the following steps: routing the 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 predict a recommended payment mode according to a model, and the users to be paid in the second user group recommend the payment mode according to a preset recommended mode; and adjusting 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 recommendation accuracy of the first user group and a second recommendation accuracy of the second user group within a first preset time period, wherein the ratio of the recommendation accuracy to the second recommendation accuracy is positively related to the ratio of the first number of users to the second number of users. The method and the device provided by the invention improve the accuracy of payment recommendation.
Description
Technical Field
The present invention relates to the field of computer applications, and in particular, to a payment method recommendation method, a payment method recommendation device, an electronic device, and a storage medium.
Background
In the conventional payment and cashing page, a user needs to manually select one payment mode to pay, and in many cases, the user can click on the unfolded parent payment mode to know whether the next-stage child payment mode can be used. The more cumbersome the operation is for the user, the worse the shopping experience is. In order to solve this problem, the following technical means are generally available at present: counting the use rate of the payment mode in a period of time for a new user, and displaying the use 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 new users, personalization cannot be achieved, and a presentation form of thousands of people and thousands of faces is realized; for old users, the recommendation method is not fully considered, and the last payment mode is only the choice of the heart tide of the user. The recommendation accuracy of the payment method in the prior art is to be improved.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the related art, and providing a payment method recommendation method, apparatus, electronic device, and storage medium, which, at least in part, overcome one or more of the problems due to the limitations and disadvantages of the related art.
According to an aspect of the present invention, there is provided a payment method recommendation method, including:
routing the 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 predict a recommended payment mode according to a model, and the users to be paid in the second user group recommend the payment mode according to a preset recommended mode; and
And adjusting the first user number routed to the first user group and/or the second user number routed to the second user group according to the first recommendation accuracy of the first user group and the second recommendation accuracy of the second user group in a first preset time period, wherein the ratio of the recommendation accuracy to the second recommendation accuracy is positively correlated with the ratio of the first user number to the second user number.
Optionally, predicting, by the model, a recommended payment manner by the user to be paid in the first user group 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 the historical payment record data of the user.
Optionally, predicting, by the model, a recommended payment manner by the user to be paid in the first user group includes:
And if the user to be paid is a user without the 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, recommending the payment mode by the user to be paid in the second user group according to the preset recommendation mode includes:
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 largest using times in the historical payment record of the user to the user.
Optionally, recommending the payment mode by the user to be paid in the second user group according to the preset recommendation mode includes:
if the user to be paid is the user without the 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 recommended accuracy and the second recommended accuracy p=n/N, where N is the number of times that the recommended payment mode in the first predetermined period is consistent with the actual payment mode of the user, and N is the total payment number in the first predetermined period.
Optionally, in the recommendation data to the user to be paid and the actual payment data of the user to be paid:
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 so as to determine whether the recommended payment mode is consistent with the actual payment mode of the user.
Optionally, when recommending a plurality of ordered payment methods to the user to be paid, n is the number of times that the recommended first ordered payment method in the first predetermined period is consistent with the actual payment method of the user.
Optionally, the method further comprises:
And determining the user group with high accuracy in the third recommendation accuracy of the user to be paid to the first user group and the fourth recommendation accuracy of 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 currently routed.
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 first user group and a second user group, wherein the routing module is used for routing users to be paid to the first user group or the second user group, the users to be paid in the first user group predict a recommended payment mode according to a model, and the users to be paid in the second user group recommend the payment mode according to a preset recommendation mode; and
The adjustment module is used for adjusting the first user number routed to the first user group and/or the second user number routed to the second user group according to the first recommendation accuracy of the first user group and the second recommendation accuracy of the second user group in a first preset time period, and the ratio of the recommendation accuracy to the second recommendation accuracy is positively correlated with the ratio of the first user number to the second user number.
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 a further 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:
and routing the user to be paid to one of the two user groups, wherein one user group carries out payment recommendation in a model prediction mode, the other user group carries out recommendation of a payment mode in a preset recommendation mode, and the route 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 mode recommended to the user is improved, redundant payment selection operation of the user is reduced, user experience is improved, and conversion rate is improved.
Drawings
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 method 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 a recommended accuracy calculation according to an embodiment of the invention.
Fig. 4 shows a timing diagram for adjusting the ratio of routes to a first user group and a second user group according to a recommended accuracy according to an embodiment of the invention.
Fig. 5 shows a block diagram of a payment method recommendation device according to an embodiment of the present invention.
Fig. 6 shows a block diagram of a payment method recommendation device according to an embodiment of the present invention.
Fig. 7 schematically illustrates a computer-readable storage medium according to an exemplary embodiment of the present invention.
Fig. 8 schematically illustrates an electronic device according to an exemplary embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many 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 the 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 present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof 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 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 diagrams depicted in the figures are exemplary only and not necessarily all steps are included. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 shows a flowchart of a payment method 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 the 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 predict a recommended payment mode according to a model, and the users to be paid in the second user group recommend the payment mode according to a preset recommended mode; and
Step S120: and adjusting the first user number routed to the first user group and/or the second user number routed to the second user group according to the first recommendation accuracy of the first user group and the second recommendation accuracy of the second user group in a first preset time period, wherein the ratio of the recommendation accuracy to the second recommendation accuracy is positively correlated with the ratio of the first user number to the second user number.
In the payment mode recommendation method of the exemplary embodiment of the invention, the user to be paid is routed to one of the two user groups, wherein one user group carries out payment recommendation in a model prediction mode, the other user group carries out recommendation of the payment mode in a preset recommendation mode, 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 mode recommended to the user is improved, redundant payment selection operation of the user is reduced, user experience is improved, and conversion rate is improved.
In various embodiments of the invention, the payment means include, but are not limited to, individual paymate payments, debit card payments, credit card payments, mobile device-carried payments (such as apple 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. Timing interactions between the recommendation interface 210, the routing interface 220, the preset recommendation interface 230, and the model recommendation interface 240 are shown 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 user group or the second user group.
Specifically, in one 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 through murmurhash (non-encrypted hash function) algorithm, if the routing ratio of the current first user group and the second user group is n: m, when h% m < n, routing the current user to be paid 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. Thus, the invention can implement a routing algorithm based on the user name of the user to be paid in a predetermined proportion.
In step S202, the recommendation interface 210 determines to which user group the user to be paid is routed.
If the user to be paid is routed to the first user group, step S203 is further performed at the recommendation interface 210 to determine whether the user to be paid has a history payment record (the history payment record may be only the history payment record of the user of the current paymate or the history payment records of the users of multiple paymate).
If the user to be paid is a user (i.e. a new user) without a history payment record, the recommendation interface 210 interacts with the model recommendation interface 240 to execute step S204, and recommends a payment method to the user according to a second prediction model, where the input of the second prediction model at least includes the current order data to be paid of the user. The current order data to be paid 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 interacts with the model recommendation interface 240 to execute step S205, and recommends a payment method to the user according to a first prediction model, where the input of the first prediction model at least includes the historical payment record data of the user. The historical payment record data may include, for example, a historical payment manner over a period of time, a historical payment time, historical payment location information, historical payment merchandise information, and the like. In some specific embodiments, the input of the first predictive model may also 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 learning prediction model, such as a BP neural network prediction model, a regression prediction model, and the like, and the present invention is not limited thereto. The first prediction model and the second prediction model can be trained offline models, and can be optimized and updated according to actual prediction conditions. Further, the prediction model generates a prediction value for each payment mode, and can sort the payment modes according to the prediction value and recommend the payment modes after sorting.
If the user to be paid is routed to the second user group, step S206 is further performed at the recommendation interface 210 to determine whether the user to be paid has a history payment record (the history payment record may be only the history payment record of the user of the current paymate or the history payment records of the users of multiple paymate).
If the user to be paid is a user (i.e. a new user) without the history payment record, the recommendation interface 210 interacts with the preset recommendation interface 230 to execute step S207, and recommends the payment mode with the largest total usage frequency to the user in the second predetermined period. For example, if the total number of uses of the debit card payment method is the greatest in the first 10 minutes, the debit card payment method is recommended to the user to be paid. For another example, the payment methods may be ranked more or less by total number of uses within the second predetermined time period, and the recommendation of the payment methods may be performed in the ranking order.
If the user to be paid is a user with a historical payment record (i.e. an old user), the recommendation interface 210 interacts with the preset recommendation interface 230 to execute step S208, and recommends the last payment mode of the user to the user or recommends the payment mode with the largest use times in the historical payment record of the user to the user. For example, if the user last paid by using a debit card payment method, the debit card payment method is recommended to the user this time. For another example, if the user makes the most payments 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 user may sort the payment methods by more or less according to the number of times the payment methods are used within a predetermined period of time, and recommend the payment methods according to the sorting order.
Further, the step S203 and the step S206 may be combined into the same step and determined before interacting with different interfaces.
Referring now to fig. 3, fig. 3 shows a timing diagram of a recommended accuracy calculation according to an embodiment of the invention. Timing interactions between recommendation monitor module 310, payment monitor module 320, and recommendation interface 330 are shown in fig. 3. The steps in the recommendation interface 330 may also be performed in the payment interface, which is not limited to the present invention.
First, the recommendation monitoring module 310 interacts with the recommendation interface 330 to perform step S301, where the recommendation monitoring module 310 sends recommendation data within a first predetermined period of time (e.g. 1 minute, 5 minutes, 10 minutes, etc., but the present invention is not limited thereto) 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 recommended in the order of the order, the recommendation data only provides the first-ordered payment method, in other words, in the calculation of the subsequent recommendation accuracy, only the first-ordered payment method is adopted to match with the actual payment method.
The payment listening module 320 interacts with the recommendation interface 330 to perform step S302, and the payment listening module 320 sends payment data within a first predetermined period of time (e.g., 1 minute, 5 minutes, 10 minutes, etc., but the invention is not limited thereto) 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 of the first user group and a second recommendation accuracy of the second user group within a first predetermined period.
Specifically, the first recommendation accuracy P 1＝Pf1+Po1＝nf1/Nf1+no1/No1, where P f1 is the recommendation accuracy of the new user in the first user group, N f1 is the number of times that the payment mode recommended by the new user in the first user group in the first predetermined time period is consistent with the actual payment mode of the user, and N f1 is the total payment number of times of the new user in the first user group in the first predetermined time period; p o1 is the recommendation accuracy of the old users in the first user group, N f1 is the number of times that the payment mode recommended by the old users in the first user group in the first preset time period is consistent with the actual payment mode of the users, and N f1 is the total payment number of the old users in the first user group in the first preset time period.
The second recommendation accuracy P 2＝Pf2+Po2＝nf2/Nf2+no2/No2, where P f2 is the recommendation accuracy of the new user in the second user group, N f2 is the number of times that the payment mode recommended by the new user in the second user group in the first predetermined time period is consistent with the actual payment mode of the user, and N f2 is the total payment number of times of the new user in the second user group in the first predetermined time period; p o2 is the recommendation accuracy of the old users in the second user group, N f2 is the number of times that the payment mode recommended by the old users in the second user group in the first preset time period is consistent with the actual payment mode of the users, and N f2 is the total payment number of the old users in the second user group in the first preset time period.
The present embodiment shows a process of calculating the recommendation accuracy based on the recommendation data and the payment data for the first predetermined period. 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, where the user is located. Specifically, in the recommendation data to the user to be paid and the actual payment data of the user to be paid: 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 so as 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 the execution sequence may be exchanged, which is not limited to the present invention.
Referring now to fig. 4, fig. 4 shows a timing diagram of adjusting the ratio of routes to a first user group and a second user group according to a recommended accuracy according to an embodiment of the present invention. The timing interactions of the routing interface 410, and the recommendation module 420 are shown in fig. 4. The steps in the recommendation interface 420 may also be performed in the payment interface, which is not limited to the present invention.
First, step S401 is performed at the recommendation interface 420, and the ratio of the first recommendation accuracy and the second recommendation accuracy is calculated from the first recommendation accuracy and the second recommendation accuracy calculated in step S303 in fig. 3.
Then, the recommendation interface 420 transmits the calculated ratio of the first recommendation accuracy and the second recommendation accuracy to the routing interface 410 to perform step S402.
Finally, the routing interface 410 performs step S403, where the first number of users routed to the first user group and/or the second number of users routed to the second user group are adjusted according to the first recommended accuracy of the first user group and the second recommended accuracy of the second user group within the first predetermined period, and the ratio of the recommended accuracy to the second recommended accuracy is positively correlated with the ratio of the first number of users to the second number of users.
Specifically, the routing interface 410 may, for example, initially preset the ratio of the number of routes to the first user and the second user to be 1:1, and then adjusting the ratio of the number of users routed to the first user to the number of users to be 2 according to the ratio of the first recommendation accuracy of the first user group to the second recommendation accuracy of the second user group (for example, 2:1) within a first preset time period: 1. in other words, after the first predetermined period of time, the routing interface 410 follows a 2: the ratio of 1 routes the users to be paid to the first user group and the second user group. Further, after the next first predetermined period of time, the ratio of routing interface 410 to route the user to be paid to the first user group and the second user group may again be updated according to the ratio of the first recommended accuracy of the first user group and the second recommended accuracy of the second user group.
In yet another embodiment of the present invention, when the space value of [ -1,1] corresponding to the ratio of the first recommended accuracy of the first user group and the second recommended accuracy of the second user group is mapped to [ -1,1] is x, the ratio f (x) of the first number of users routed to the first user group and the second number of users routed to the second user group may be calculated as follows:
f(x)＝1/[1+e(-5x)]，x∈[-1,1]。
thus, by means of the specific adjustment algorithm, the functional relationship 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 above are merely illustrative of various embodiments of the present invention, and the present invention is not limited thereto.
The payment method recommending device provided by the invention is described below with reference to fig. 5. Fig. 5 shows a block diagram of a payment method recommendation device according to an embodiment of the present invention. The payment method recommendation device 500 includes a routing module 510 and an adjustment module 520.
The routing module 510 is configured to route a user to be paid to a first user group or a second user group, where the user to be paid in the first user group predicts a recommended payment manner according to a model, and the user to be paid in the second user group recommends a payment manner according to a preset recommendation manner; and
The adjustment 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 recommendation accuracy of the first user group and a second recommendation accuracy of the second user group within a first predetermined period of time, where a ratio of the recommendation accuracy to the second recommendation accuracy is positively correlated with a ratio of the first number of users to the second number of users.
In the payment mode recommending device of the exemplary embodiment of the invention, the user to be paid is routed to one of the two user groups, wherein one user group carries out payment recommendation in a model prediction mode, the other user group carries out recommendation of the payment mode in a preset recommendation mode, 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 mode recommended to the user is improved, redundant payment selection operation of the user is reduced, user experience is improved, and conversion rate is improved.
Referring now to fig. 6, fig. 6 is a block diagram illustrating a payment method recommendation device according to an embodiment of the present invention. The payment method recommendation device 600 includes a routing module 610, an adjusting 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. Routing module 610, adjustment module 620 and routing module 510 and 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 recommending module 630 recommends a payment mode to the user according to a first prediction model, where the input of the first prediction model at least includes the historical payment record data of the user. The historical payment record data may include, for example, a historical payment manner over a period of time, a historical payment time, historical payment location information, historical payment merchandise information, and the like. In some specific embodiments, the input of the first predictive model may also 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 (i.e. a new user) without a history payment record, the second prediction model recommending 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 order data to be paid 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 (i.e. an old user) having a historical payment record, the first preset recommendation module 650 recommends the last payment method of the user to the user or recommends the payment method of the user with the most usage times in the historical payment record. For example, if the user last paid by using a debit card payment method, the debit card payment method is recommended to the user this time. For another example, if the user makes the most payments 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 user may sort the payment methods by more or less according to the number of times the payment methods are used within a predetermined period of time, and recommend the payment methods according to the sorting order.
In the second user group, if the user to be paid is a user (i.e. a new user) without a history payment record, the second preset recommendation module 660 recommends the payment mode with the largest total usage number to the user in a second preset time period. For example, if the total number of uses of the debit card payment method is the greatest in the first 10 minutes, the debit card payment method is recommended to the user to be paid. For another example, the payment methods may be ranked more or less by total number of uses within the second predetermined time period, and the recommendation of the payment methods may be performed in the ranking 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 of the above embodiments. In some possible embodiments, the 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 carry out the steps according to the various exemplary embodiments of the invention as described in the electronic prescription stream processing method section of this specification, when said program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above-described 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 thereto, and in this 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. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk 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 data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium 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 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, partially on the tenant device, as a stand-alone software package, partially on the tenant computing device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of remote computing devices, the remote computing device 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., connected through the internet using an internet service provider).
In an exemplary embodiment of the invention, an electronic device is also provided, which may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the electronic prescription flow processing method of any of the embodiments described above via execution of the executable instructions.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 800 according to such an embodiment of the invention is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 8, the electronic device 800 is embodied in the form of a general purpose computing device. Components of 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 that connects the different system components (including memory unit 820 and processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs the steps according to various exemplary embodiments of the present invention described in the above-described electronic prescription flow processing methods section of the present specification. For example, the processing unit 810 may perform the steps shown in fig. 1.
The storage unit 820 may include a readable medium in the form of a volatile memory unit, 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 storage 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 or some combination of which may include an implementation of a network environment.
Bus 830 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a tenant to interact with the electronic device 800, and/or any device (e.g., router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850. Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. Network adapter 860 may communicate with other modules of electronic device 800 via bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, 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 (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the electronic prescription flow processing method according to the embodiment of the present invention.
Compared with the prior art, the invention has the advantages that:
and routing the user to be paid to one of the two user groups, wherein one user group carries out payment recommendation in a model prediction mode, the other user group carries out recommendation of a payment mode in a preset recommendation mode, and the route 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 mode recommended to the user is improved, redundant payment selection operation of the user is reduced, user experience is improved, and conversion rate is improved.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
Claims (11)
1.A payment method recommendation method, comprising:
routing the 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 predict a recommended payment mode according to a model, and the users to be paid in the second user group recommend the payment mode according to a preset recommended mode; and
Adjusting a first user number routed to the first user group and/or a second user number 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 first recommended accuracy to the second recommended accuracy is positively related to the ratio of the first user number to the second user number;
Wherein adjusting the first number of users routed to the first user group and/or the second number of users routed to the second user group comprises: obtaining a space value x of the ratio of the first recommended accuracy rate and the second recommended accuracy rate mapped to [ -1,1] space, obtaining a ratio f (x) of a first number of users routed to the first user group and a second number of users routed to the second user group, f (x) =1/[ 1+e (-5x) ], x e [ -1,1].
2. The payment method recommendation method of claim 1, wherein predicting, by model, a recommended payment method for a user 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 the historical payment record data of the user.
3. The payment method recommendation method of claim 1, wherein predicting, by model, a recommended payment method for a user to be paid in the first user group comprises:
And if the user to be paid is a user without the 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 as claimed in claim 1, wherein the recommending the payment method by the user to be paid in the second user group according to the preset recommendation method includes:
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 largest using times in the historical payment record of the user to the user.
5. The payment method recommendation method as claimed in claim 1, wherein the recommending the payment method by the user to be paid in the second user group according to the preset recommendation method includes:
if the user to be paid is the user without the 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 according to claim 1, wherein the first recommendation accuracy and the second recommendation accuracy p=n/N, where N is a number of times that the recommended payment method in the first predetermined period coincides with the actual payment method of the user, and N is a total payment number in the first predetermined period.
7. The payment method 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:
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 so as to determine whether the recommended payment mode is consistent with the actual payment mode of the user.
8. The payment method of claim 6, wherein n is a number of times the first-ordered payment method recommended in the first predetermined period is consistent with the actual payment method of the user when recommending a plurality of ordered payment methods to the user to be paid.
9. A payment method recommendation device, comprising:
The system comprises a routing module, a first user group and a second user group, wherein the routing module is used for routing users to be paid to the first user group or the second user group, the users to be paid in the first user group predict a recommended payment mode according to a model, and the users to be paid in the second user group recommend the payment mode according to a preset recommendation mode; and
The adjustment module is used for adjusting the first user number routed to the first user group and/or the second user number routed to the second user group according to the first recommendation accuracy of the first user group and the second recommendation accuracy of the second user group in a first preset time period, and the ratio of the first recommendation accuracy to the second recommendation accuracy is positively correlated with the ratio of the first user number to the second user number;
Wherein the adjusting module adjusts a first number of users routed to the first user group and/or a second number of users routed to the second user group, comprising: obtaining a space value x of the ratio of the first recommended accuracy rate and the second recommended accuracy rate mapped to [ -1,1] space, obtaining a ratio f (x) of a first number of users routed to the first user group and a second number of users routed to the second user group, f (x) =1/[ 1+e (-5x) ], x e [ -1,1].
10. An electronic device, the electronic device comprising:
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 8.
11. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811021931.4A CN110874737B (en) | 2018-09-03 | 2018-09-03 | Payment mode recommendation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811021931.4A CN110874737B (en) | 2018-09-03 | 2018-09-03 | Payment mode recommendation method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110874737A CN110874737A (en) | 2020-03-10 |
| CN110874737B true CN110874737B (en) | 2024-06-18 |
Family
ID=69716792
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811021931.4A Active CN110874737B (en) | 2018-09-03 | 2018-09-03 | Payment mode recommendation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110874737B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113052579B (en) * | 2021-04-23 | 2021-12-07 | 深圳市亚飞电子商务有限公司 | Payment method and system of mobile payment platform |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651357A (en) * | 2016-11-16 | 2017-05-10 | 网易乐得科技有限公司 | Method and device for recommending payment mode |
| CN107341650A (en) * | 2016-01-19 | 2017-11-10 | 广州爱九游信息技术有限公司 | The transfer method and device of virtual resource |
| CN107807967A (en) * | 2017-10-13 | 2018-03-16 | 平安科技（深圳）有限公司 | Real-time recommendation method, electronic equipment and computer-readable recording medium |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8001008B2 (en) * | 2006-10-24 | 2011-08-16 | Garett Engle | System and method of collaborative filtering based on attribute profiling |
| US20090018955A1 (en) * | 2007-07-13 | 2009-01-15 | Yen-Fu Chen | Method and apparatus for providing user access to payment methods |
| US20090163183A1 (en) * | 2007-10-04 | 2009-06-25 | O'donoghue Hugh | Recommendation generation systems, apparatus and methods |
| KR101030653B1 (en) * | 2009-01-22 | 2011-04-20 | 성균관대학교산학협력단 | User-based collaborative filtering recommender system amending similarity using information entropy |
| US8244564B2 (en) * | 2009-03-31 | 2012-08-14 | Richrelevance, Inc. | Multi-strategy generation of product recommendations |
| US10984397B2 (en) * | 2009-03-31 | 2021-04-20 | Ebay Inc. | Application recommendation engine |
| US8566261B2 (en) * | 2010-12-17 | 2013-10-22 | Microsoft Corporation | Interactive recommendations |
| US20130024464A1 (en) * | 2011-07-20 | 2013-01-24 | Ness Computing, Inc. | Recommendation engine that processes data including user data to provide recommendations and explanations for the recommendations to a user |
| US10210261B2 (en) * | 2014-06-18 | 2019-02-19 | Facebook, Inc. | Ranking and filtering groups recommendations |
| CN106997347A (en) * | 2016-01-22 | 2017-08-01 | 华为技术有限公司 | Information recommendation method and server |
| CN106228353A (en) * | 2016-07-21 | 2016-12-14 | 北京三快在线科技有限公司 | A kind of method for processing payment information, device and system |
| CN107562917B (en) * | 2017-09-12 | 2021-04-06 | 广州酷狗计算机科技有限公司 | User recommendation method and device |
| KR20170117944A (en) * | 2017-09-28 | 2017-10-24 | 에스케이플래닛 주식회사 | System and method for code based settlement |
-
2018
- 2018-09-03 CN CN201811021931.4A patent/CN110874737B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341650A (en) * | 2016-01-19 | 2017-11-10 | 广州爱九游信息技术有限公司 | The transfer method and device of virtual resource |
| CN106651357A (en) * | 2016-11-16 | 2017-05-10 | 网易乐得科技有限公司 | Method and device for recommending payment mode |
| CN107807967A (en) * | 2017-10-13 | 2018-03-16 | 平安科技（深圳）有限公司 | Real-time recommendation method, electronic equipment and computer-readable recording medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110874737A (en) | 2020-03-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11386496B2 (en) | Generative network based probabilistic portfolio management | |
| CN110046965A (en) | Information recommendation method, device, equipment and medium | |
| CN109272348B (en) | Method and device for determining number of active users, storage medium and electronic equipment | |
| CN107832365B (en) | Multi-class travel product pushing method and device, electronic equipment and storage medium | |
| US11200587B2 (en) | Facilitating use of select hyper-local data sets for improved modeling | |
| US10750022B2 (en) | Enhancing customer service processing using data analytics and cognitive computing | |
| US10904109B2 (en) | Real-time cloud-based resource reallocation recommendation generation | |
| CN111583018A (en) | Credit granting strategy management method and device based on user financial performance analysis and electronic equipment | |
| CN114140947A (en) | Interface display method and device, electronic equipment, storage medium and program product | |
| US20210064507A1 (en) | Detecting and predicting application performance | |
| CN110874737B (en) | Payment mode recommendation method and device, electronic equipment and storage medium | |
| CN110766202A (en) | Contract settlement system, method and equipment for predicting channel capacity | |
| CN106815765B (en) | Asset allocation method and equipment | |
| CN112528151A (en) | Object display method and device, electronic equipment and storage medium | |
| US11023530B2 (en) | Predicting user preferences and requirements for cloud migration | |
| US10664041B2 (en) | Implementing a customized interaction pattern for a device | |
| CN112686705B (en) | Method and device for predicting sales effect data and electronic equipment | |
| US20230289650A1 (en) | Continuous machine learning system for containerized environment with limited resources | |
| US11645595B2 (en) | Predictive capacity optimizer | |
| CN111681093B (en) | Method and device for displaying resource page and electronic equipment | |
| CN109634500B (en) | User data filling method and device, terminal equipment and storage medium | |
| US10891664B2 (en) | System and method for facilitating non-parametric weighted correlation analysis | |
| US20200089812A1 (en) | Updating social media post based on subsequent related social media content | |
| US11561667B2 (en) | Semi-virtualized portable command center | |
| US11295355B1 (en) | User feedback visualization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant after: Jingdong Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant before: Jingdong Digital Technology Holding Co.,Ltd. Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant after: Jingdong Digital Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant before: JINGDONG DIGITAL TECHNOLOGY HOLDINGS Co.,Ltd. Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant after: JINGDONG DIGITAL TECHNOLOGY HOLDINGS Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Beijing Economic and Technological Development Zone, 100176 Applicant before: BEIJING JINGDONG FINANCIAL TECHNOLOGY HOLDING Co.,Ltd. |
|
| GR01 | Patent grant |