WO2020019969A1 - 支付工具推荐方法、装置、设备及计算机可读存储介质 - Google Patents

支付工具推荐方法、装置、设备及计算机可读存储介质 Download PDF

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WO2020019969A1
WO2020019969A1 PCT/CN2019/094906 CN2019094906W WO2020019969A1 WO 2020019969 A1 WO2020019969 A1 WO 2020019969A1 CN 2019094906 W CN2019094906 W CN 2019094906W WO 2020019969 A1 WO2020019969 A1 WO 2020019969A1
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Prior art keywords
payment
user
instrument
payment instrument
portrait
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French (fr)
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周书
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q20/02—Payment architectures, schemes or protocols involving a neutral party, e.g. certification authority, notary or trusted third party [TTP]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks

Definitions

  • Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for recommending payment tools.
  • Third-party payment platforms can set up multiple payment instruments.
  • the order selection of payment instruments depends on the user's settings or habits (the user's most recent payment instrument). When the user is not sensitive to the settings (do not remember that he has set the payment order himself), or the habits do not match the settings (the last payment used is not commonly used Card) or an abnormality on the partner side, payment failure is likely to occur.
  • the first aspect of the present disclosure provides a method for recommending payment instruments, including:
  • Acquiring characteristic data required for a user to recommend a payment tool wherein the characteristic data includes a portrait of a payment behavior of the user
  • the payment instruments are presented on the payment instrument presentation page in the order of the payment instruments.
  • a second aspect of the present disclosure provides a payment tool recommendation device, including:
  • An obtaining module configured to obtain characteristic data required for a user to make a payment tool recommendation, wherein the characteristic data includes a portrait of a payment behavior of the user;
  • a generation module configured to analyze the characteristic data using a preset payment instrument recommendation model to generate a sequence of payment instruments ordered from high to low payment success rate;
  • a receiving module configured to receive a payment confirmation instruction from the user
  • the presentation module is configured to, in response to the payment confirmation instruction, present the payment instruments in the payment instrument presentation page in the order of the payment instruments.
  • a third aspect of the present disclosure provides an electronic device including a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to Implement the method as described in the first aspect.
  • a fourth aspect of the present disclosure provides a computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the method described in the first aspect.
  • feature data required for recommending payment tools for a user is obtained, wherein the feature data includes a portrait of a payment behavior of the user; the feature data is preset using a preset payment tool recommendation model.
  • it also optimizes the payment tool selection process to avoid user defaults or exceptions caused by partner institutions. Setting up (or customizing) the order of payment instruments determined, thus improving payment efficiency.
  • the order of payment tools is completely completed in the background, not limited to payment devices, and does not require user settings. Therefore, the payment tool recommendation scheme according to the present disclosure improves the payment success rate, reduces the burden on users, and improves the user experience.
  • FIG. 1 shows a flowchart of a payment tool recommendation method according to an embodiment of the present disclosure
  • FIG. 2 shows a structural block diagram of a payment tool recommendation device according to an embodiment of the present disclosure
  • FIG. 3 shows a structural block diagram of a device according to an embodiment of the present disclosure
  • FIG. 4 is a schematic structural diagram of a computer system suitable for implementing a text content identification method according to an embodiment of the present disclosure.
  • this disclosure proposes to use a payment tool recommendation model to analyze the user's payment behavior portrait and recommend the most likely The payment instruments with successful payment and the order of payment instruments with the highest payment success rate.
  • FIG. 1 illustrates a flowchart of a payment tool recommendation method according to an embodiment of the present disclosure.
  • the method may include steps S101, S102, S103, and S104.
  • step S101 feature data required for recommending a payment tool for a user is obtained, where the feature data includes a portrait of a user's payment behavior.
  • step S102 the preset data tool recommendation model is used to analyze the characteristic data to generate a payment tool sequence that is ranked according to the payment success rate from high to low.
  • step S103 a payment confirmation instruction is received from the user.
  • step S104 in response to the payment confirmation instruction, the payment instruments are presented in the order of payment instruments on the payment instrument presentation page.
  • the payment instrument may refer to an internal and external loan asset instrument that a user can select for payment when making a payment, for example, it may be a bank credit card, a bank debit card, or a network payment operated by the payment platform itself Tools, etc.
  • the characteristic data required by the user when making a payment tool recommendation may be obtained through a payment platform including multiple payment tools and other cooperation agencies.
  • the characteristic data required by a user to make a payment tool recommendation can be obtained through a cash register of a payment platform and a financial network that cooperates with the payment platform.
  • the feature data further includes a user portrait.
  • a preset payment tool recommendation model may be used to analyze a user's payment behavior portrait and user portrait to generate a payment tool order that is ranked according to the payment success rate from high to low.
  • the characteristic data in addition to the user portrait, includes at least one of the following: user credit data, payment environment data, external funding channel operation status, and product business indicators. That is, in one embodiment of the present disclosure, the feature data may include a portrait of a user's payment behavior and a portrait of a user. In one embodiment of the present disclosure, in addition to the user ’s payment behavior portrait and user portrait, the characteristic data may include at least one of the following: user credit data, payment environment data, external funding channel operation status, and product business indicators .
  • a preset payment tool recommendation model may be used to depict a user ’s payment behavior portrait, user portrait, and at least one of the following: user credit data, payment environment data, external funding channel operation status, and product business
  • the indicators are analyzed to generate a sequence of payment instruments ranked from high to low payment success rate.
  • the payment environment data may refer to, for example, a specific mobile device model and a network environment.
  • the operation status of external funding channels can refer to, for example, whether the external funding channels such as banks are abnormal and the limits of external funding channels.
  • Product business indicators refer to the number of monthly active users, daily active users, user retention, and user growth rates for products that call payment tools.
  • the user's payment behavior portrait reflects the user's payment behavior and includes at least one of the following characteristics: the user's historical payment history, historical payment frequency, payment limit, payment tool preferences, and payment success rate , Payment instrument health, payment instrument switching rate, payment instrument stability, credit asset usage habits.
  • the historical payment record of the user refers to the historical payment record of the user obtained through the cash register of the payment platform and the financial network that cooperates with the payment platform.
  • Historical payment frequency refers to the number of times a user has used a payment tool within a certain period of time (within one year, three months, one month, seven days, and one day).
  • the payment amount refers to the amount of each payment instrument.
  • Payment instrument preferences refer to the percentage of times a user uses a payment instrument relative to the total number of transactions.
  • the payment success rate refers to the number of successful payment transactions by the user divided by the number of payment transactions initiated by the user. In one embodiment, the payment success rate may further include a payment instrument payment success rate.
  • the payment instrument payment success rate refers to the actual number of successful payment instruments of the user divided by the number of payment transactions initiated by the user with the payment instrument.
  • the health of a payment instrument can refer to the operating status of the payment instrument.
  • the payment instrument switching rate refers to the number of successful payment transactions divided by the number of user-initiated payment transactions after the user switches payment instruments.
  • the stability of a payment instrument refers to the number of successful payments made by a user with a payment instrument divided by the number of payments initiated by the user through the payment instrument.
  • the user portrait reflects user attributes, and includes at least one of the following characteristics: the user's age, occupation, gender, asset status, and payment complaint record.
  • feature data is not limited to the foregoing, and other types of feature data can also be adopted as needed.
  • the payment confirmation instruction may be an immediate payment instruction issued by the user, or may be a debit instruction executed after the user signs a withholding agreement with the payment platform.
  • the preset payment instrument recommendation model is a logistic regression model.
  • the payment instrument recommendation model is not limited to the logistic regression model, but can also Other types of models are needed.
  • feature data required for recommending payment tools for users is obtained, where the feature data includes a portrait of a user's payment behavior; the preset data is used to analyze the feature data to generate a payment-based payment method.
  • Orders of payment instruments ordered from high to low success rate; receiving payment confirmation instructions from users; in response to payment confirmation instructions, presenting payment instruments in the order of payment instruments on the payment instrument presentation page can recommend the payment instrument with the highest payment success rate for users, At the same time, it recommends the order of payment instruments with the highest payment success rate for users, and also optimizes the payment tool selection process to avoid the order of payment instruments that can only be determined according to the user's default settings (or habits) due to user errors or abnormalities on the side of the partner institution. Improved payment efficiency.
  • the order of payment tools is completely completed in the background, not limited to payment devices, and does not require user settings. Therefore, the payment tool recommendation scheme according to the present disclosure improves the payment success rate, reduces the burden on users, and improves the user experience.
  • the following uses three scenarios as examples to illustrate a payment tool recommendation method according to an embodiment of the present disclosure.
  • Scenario 1 The user is not sensitive to the order of the payment tools set on the payment platform. An error such as insufficient balance and unavailable card occurs during payment, resulting in payment failure. The user needs to switch the payment tool again to complete the payment.
  • the payment instrument recommendation method it is possible to recommend the payment instrument most likely to be successful.
  • the user has set “Payment Tool 1" as the preferred payment tool (not sensitive to the payment order set by himself) because of the operation activities.
  • Payment Tool 1 the preferred payment tool (not sensitive to the payment order set by himself) because of the operation activities.
  • Through a cumulative analysis of the user's payment behavior data for one year it is found that users use “Payment Tool 2" to pay more at the beginning of each month (probable reason: pay through the payment tool 2 at the beginning of the month), so the user is recommended to "Payment Tools” 2 "instead of" Payment Tool 1 "set by the user.
  • Scenario 2 In a specific scenario, the user signs an agreement with the payment platform to withhold money. Without the user's participation, the payment platform actively polls the payment tool under the user name for deduction. When debiting according to the default order, all payment instruments will be polled one by one until the debit is successful. If you encounter users with multiple payment instruments, polling one by one is less efficient.
  • the payment tool recommendation method disclosed in the present disclosure it is possible to recommend the payment tool most likely to successfully debit to complete the payment, improve the deduction efficiency, and save the deduction cost.
  • Scenario 3 The stability of the payment channel of the current cooperative banking institution of the payment platform is abnormal, and the user still chooses the institution's bank card for payment before receiving the announcement, resulting in payment failure.
  • the method of recommending payment instruments it is possible to determine that the payment channel of the bank is abnormal through the characteristic data, and the payment success rate is lower than the daily level. Therefore, it is possible to recommend the most likely successful payment instruments, and to rank the payment instruments in order from high to low. It lowered the payment order of the bank's payment channel. Therefore, the payment success rate under abnormal conditions can be improved.
  • the payment instrument recommendation device of the present disclosure is described below with reference to FIG. 2.
  • FIG. 2 shows a structural block diagram of a payment tool recommendation device according to another embodiment of the present disclosure.
  • the payment tool recommendation device shown in FIG. 2 includes an obtaining module 201, a generating module 202, a receiving module 203, and a presentation module 204.
  • the obtaining module 201 is configured to obtain feature data required for a user to recommend a payment tool, wherein the feature data includes a portrait of a user's payment behavior.
  • the generating module 202 is configured to analyze the feature data using a preset payment instrument recommendation model, and generate a sequence of payment instruments ordered from high to low payment success rate;
  • the receiving module 203 is configured to receive a payment confirmation instruction from a user
  • the presentation module 204 is configured to, in response to the payment confirmation instruction, present the payment instruments in the order of payment instruments on the payment instrument presentation page.
  • the payment instrument may refer to an internal and external loan asset instrument that a user can select for payment when making a payment, for example, it may be a bank credit card, a bank debit card, or a network payment operated by the payment platform itself Tools, etc.
  • the characteristic data required by the user when making a payment tool recommendation may be obtained through a payment platform including multiple payment tools and other cooperation agencies.
  • the characteristic data required by a user to make a payment tool recommendation can be obtained through a cash register of a payment platform and a financial network that cooperates with the payment platform.
  • the feature data further includes a user portrait.
  • a preset payment tool recommendation model may be used to analyze a user's payment behavior portrait and user portrait to generate a payment tool order that is ranked according to the payment success rate from high to low.
  • the characteristic data in addition to the user portrait, includes at least one of the following: user credit data, payment environment data, external funding channel operation status, and product business indicators. That is, in one embodiment of the present disclosure, the feature data may include a portrait of a user's payment behavior and a portrait of a user. In one embodiment of the present disclosure, in addition to the user ’s payment behavior portrait and user portrait, the characteristic data may include at least one of the following: user credit data, payment environment data, external funding channel operation status, and product business indicators .
  • a preset payment tool recommendation model may be used to depict a user ’s payment behavior portrait, user portrait, and at least one of the following: user credit data, payment environment data, external funding channel operation status, and product business
  • the indicators are analyzed to generate a sequence of payment instruments ranked from high to low payment success rate.
  • the payment environment data may refer to, for example, a specific mobile device model and a network environment.
  • the operation status of external funding channels can refer to, for example, whether the external funding channels such as banks are abnormal and the limits of external funding channels.
  • Product business indicators refer to the number of monthly active users, daily active users, user retention, and user growth rates for products that call payment tools.
  • the user's payment behavior portrait reflects the user's payment behavior and includes at least one of the following characteristics: the user's historical payment history, historical payment frequency, payment limit, payment tool preferences, and payment success rate , Payment instrument health, payment instrument switching rate, payment instrument stability, credit asset usage habits.
  • the historical payment record of the user refers to the historical payment record of the user obtained through the cash register of the payment platform and the financial network that cooperates with the payment platform.
  • Historical payment frequency refers to the number of times a user has used a payment tool within a certain period of time (within one year, three months, one month, seven days, and one day).
  • the payment amount refers to the amount of each payment instrument.
  • Payment instrument preferences refer to the percentage of times a user uses a payment instrument relative to the total number of transactions.
  • the payment success rate refers to the number of successful payment transactions by the user divided by the number of payment transactions initiated by the user. In one embodiment, the payment success rate may further include a payment instrument payment success rate.
  • the payment tool payment success rate refers to the actual number of successful payment transactions of the user's payment instrument divided by the number of payment transactions initiated by the user with the payment instrument.
  • the health of a payment instrument can refer to the operating status of the payment instrument.
  • the payment instrument switching rate refers to the number of successful payment transactions divided by the number of user-initiated payment transactions after the user switches payment instruments.
  • the stability of a payment instrument refers to the number of successful payments made by a user with a payment instrument divided by the number of payments initiated by the user through the payment instrument.
  • the user portrait reflects user attributes, and includes at least one of the following characteristics: the user's age, occupation, gender, asset status, and payment complaint record.
  • feature data is not limited to the foregoing, and other types of feature data can also be adopted as needed.
  • the payment confirmation instruction may be an immediate payment instruction issued by the user, or may be a debit instruction executed after the user signs a withholding agreement with the payment platform.
  • the preset payment instrument recommendation model is a logistic regression model.
  • the payment instrument recommendation model is not limited to the logistic regression model, but can also be based on Other types of models are needed.
  • the acquisition module is configured to acquire feature data required for a user to make a payment tool recommendation, wherein the feature data includes a portrait of a user's payment behavior; the generation module is configured to use a preset payment
  • the tool recommendation model analyzes the feature data to generate a payment instrument sequence that is ordered from high to low payment success rate; the receiving module is configured to receive payment confirmation instructions from the user; the presentation module is configured to respond to the payment confirmation instruction in
  • the payment instrument presentation page presents the payment instruments in the order of the payment instrument. It can recommend the payment instrument with the highest payment success rate for users, and recommend the payment instrument sequence with the highest payment success rate for users.
  • the payment instrument selection process has been optimized to avoid user errors.
  • the order of payment instruments that can only be determined according to the user's default settings (or habits) caused by an abnormality on the side of the cooperative institution, thereby improving payment efficiency.
  • the order of payment tools is completely completed in the background, not limited to payment devices, and does not require user settings. Therefore, the payment tool recommendation scheme according to the present disclosure improves the payment success rate, reduces the burden on users, and improves the user experience.
  • the structure of the text content identification device may be implemented as a text content identification device.
  • the processing device 300 may include a processor. 301 and memory 302.
  • the memory 302 is configured to store a program that supports the text content identification device to execute the text content identification method in any one of the foregoing embodiments, and the processor 301 is configured to execute the program stored in the memory 302.
  • the memory 302 is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 301.
  • the processor 301 is configured to execute all or part of the foregoing method steps.
  • the structure of the text content identification device may further include a communication interface, which is used for the text content identification device to communicate with other devices or a communication network.
  • An exemplary embodiment of the present disclosure also provides a computer storage medium for storing computer software instructions used by the text content identification device, which includes a program for executing a text content identification method in any one of the foregoing embodiments.
  • FIG. 4 is a schematic structural diagram of a computer system suitable for implementing a text content identification method according to an embodiment of the present disclosure.
  • the computer system 400 includes a central processing unit (CPU) 401 that can be loaded into a random access memory (RAM) 403 according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408. Instead, various processes in the embodiment shown in FIG. 1 are executed. In the RAM 403, various programs and data required for the operation of the system 400 are also stored.
  • the CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404.
  • An input / output (I / O) interface 405 is also connected to the bus 404.
  • the following components are connected to the I / O interface 405: an input portion 406 including a keyboard, a mouse, and the like; an output portion 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the speaker; a storage portion 408 including a hard disk and the like And a communication section 409 including a network interface card such as a LAN card, a modem, and the like.
  • the communication section 409 performs communication processing via a network such as the Internet.
  • the driver 410 is also connected to the I / O interface 405 as needed.
  • a removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
  • the method described above with reference to FIG. 1 may be implemented as a computer software program.
  • embodiments of the present disclosure include a computer program product including a computer program tangibly embodied on a readable medium thereon, the computer program containing program code for performing the data processing method of FIG. 1.
  • the computer program may be downloaded and installed from a network through the communication section 409, and / or installed from a removable medium 411.
  • each block in the roadmap or block diagram may represent a module, program segment, or portion of code that contains one or more components that implement a specified logical function Executable instructions.
  • the functions noted in the blocks may also occur in a different order than those marked in the drawings. For example, two successively represented boxes may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts can be implemented by a dedicated hardware-based system that performs the specified function or operation , And / or can be implemented with a combination of dedicated hardware and computer instructions.
  • the units or modules described in the embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware.
  • the described units or modules may also be provided in the processor, and the names of these units or modules do not, in some cases, define the unit or module itself.
  • the present disclosure also provides a computer-readable storage medium.
  • the computer-readable storage medium may be a computer-readable storage medium included in the device described in the foregoing embodiments; Computer-readable storage media incorporated into a device.
  • the computer-readable storage medium stores one or more programs, which are used by one or more processors to perform the methods described in the present disclosure.

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Abstract

本公开实施例提供支付工具推荐方法、装置、设备及计算机可读存储介质。支付工具推荐方法包括：获取为用户进行支付工具推荐时所需的特征数据，其中，所述特征数据包括所述用户的支付行为画像；使用预设的支付工具推荐模型对所述特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；从所述用户接收支付确认指令；响应于所述支付确认指令，在支付工具呈现页面按照所述支付工具顺序呈现支付工具，可以为用户推荐支付成功率最高的支付工具，同时为用户推荐支付成功率最高的支付工具顺序，此外还优化了支付工具选择流程，避免因用户失误或者合作机构侧异常导致的只能根据用户默认设置确定的支付工具顺序，因此提高了支付效率。

Description

支付工具推荐方法、装置、设备及计算机可读存储介质 技术领域
本公开实施例涉及计算机技术领域，尤其涉及支付工具推荐方法、装置、设备及计算机可读存储介质。
背景技术
随着智能终端的普及，人们越来越依赖第三方支付方式进行购物。第三方支付平台可以设置多个支付工具。支付工具的顺序选择依赖于用户设置或习惯(用户最近一次支付工具)，当用户对设置不敏感(不记得有自己设置过支付顺序)，或者习惯不符合设置(上次支付使用了不是常用的卡)，或者合作机构侧出现异常时，容易发生支付失败的情况。
因此，亟需一种能够提高支付成功率的支付工具顺序推荐方案。
发明内容
有鉴于此，本公开第一方面提供了一种支付工具推荐方法，包括：
获取为用户进行支付工具推荐时所需的特征数据，其中，所述特征数据包括所述用户的支付行为画像；
使用预设的支付工具推荐模型对所述特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；
从所述用户接收支付确认指令；
响应于所述支付确认指令，在支付工具呈现页面按照所述支付工具顺序呈现支付工具。
本公开第二方面提供了一种支付工具推荐装置，包括：
获取模块，被配置为获取为用户进行支付工具推荐时所需的特征数据，其中，所述特征数据包括所述用户的支付行为画像；
生成模块，被配置为使用预设的支付工具推荐模型对所述特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；
接收模块，被配置为从所述用户接收支付确认指令；
呈现模块，被配置为响应于所述支付确认指令，在支付工具呈现页面按照所述支付工具顺序呈现支付工具。
本公开第三方面提供了一种电子设备，包括存储器和处理器；其中，所述存储器用于存储一条或多条计算机指令，其中，所述一条或多条计算机指令被所述处理器执行以实现如第一方面所述的方法。
本公开第四方面提供了一种计算机可读存储介质，其上存储有计算机指令，该计算机指令被处理器执行时实现如第一方面所述的方法。
在本公开实施方式中，通过获取为用户进行支付工具推荐时所需的特征数据，其中，所述特征数据包括所述用户的支付行为画像；使用预设的支付工具推荐模型对所述特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；从所述用户接收支付确认指令；响应于所述支付确认指令，在支付工具呈现页面按照所述支付工具顺序呈现支付工具，可以为用户推荐支付成功率最高的支付工具，同时为用户推荐支付成功率最高的支付工具顺序，此外还优化了支付工具选择流程，避免因用户失误或者合作机构侧异常导致的只能根据用户默认设置(或习惯)确定的支付工具顺序，因此提高了支付效率。另外，支付工具顺序推荐完全在后台完成，不受限于支付设备，无需用户设置。因此，根据本公开的支付工具推荐方案提高了支付成功率，减轻了用户负担，提升了用户体验。
本公开的这些方面或其他方面在以下实施例的描述中会更加简明易懂。
附图说明
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对示例性实施例或相关技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本公开的一些示例性实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1示出根据本公开一实施方式的支付工具推荐方法的流程图；
图2示出根据本公开一实施方式的支付工具推荐装置的结构框图；
图3示出根据本公开一实施方式的设备的结构框图；
图4是适于用来实现根据本公开一实施方式的文本内容标识方法的计算机系统的结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本公开方案，下面将结合本公开示例性实施例中的附图，对本公开示例性实施例中的技术方案进行清楚、完整地描述。
在本公开的说明书和权利要求书及上述附图中的描述的一些流程中，包含了按照特定顺序出现的多个操作，但是应该清楚了解，这些操作可以不按照其在本文中出现的顺序来执行或并行执行，操作的序号如101、102等，仅仅是用于区分开各个不同的操作，序号本身不代表任何的执行顺序。另外，这些流程可以包括更多或更少的操作，并且这些操作可以按顺序执行或并行执行。需要说明的是，本文中的“第一”、“第二”等描述，是用于区分不同的消息、设备、模块等，不代表先后顺序，也不限定“第一”和“第二”是不同的类型。
下面将结合本公开示例性实施例中的附图，对本公开示例性实施例中的技术方案进行清楚、完整地描述，显然，所描述的示例性实施例仅仅是本公开一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。.
为提升用户在支付过程中的成功率，降低支付过程中的切换率(因交易失败或不习惯当前设置)，本公开提出使用支付工具推荐模型分析用户的支付行为画像，并为用户推荐最可能支付成功的支付工具以及支付成功率最高的支付工具顺序。
图1示出根据本公开一实施方式的支付工具推荐方法的流程图。该方法可以包括步骤S101、S102、S103和S104。
在步骤S101中，获取为用户进行支付工具推荐时所需的特征数据，其中，特征数据包括述用户的支付行为画像。
在步骤S102中，使用预设的支付工具推荐模型对特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序。
在步骤S103中，从用户接收支付确认指令。
在步骤S104中，响应于支付确认指令，在支付工具呈现页面按照支付工具顺序呈现支付工具。
在本公开的一个实施例中，支付工具可以指的是用户进行支付时可选择用于支付的内外部借贷资产工具，例如，可以是银行信用卡、银行借记卡、支付平台自身运营的网络支付工具等。
在本公开的一个实施例中，可以通过包括多个支付工具的支付平台以及其他合作机构来获取用户进行支付工具推荐时所需的特征数据。例如，可以通过支付平台的收银台以及与支付平台合作的金融网络来获取用户进行支付工具推荐时所需的特征数据。
在本公开的一个实施例中，特征数据还包括用户画像。在本公开的一个实施例中，可以使用预设的支付工具推荐模型对用户的支付行为画像和用户画像进行分析，生成按照支付成功率从高到低排序的支付工具顺序。
在本公开的一个实施例中，除了包括用户画像，特征数据还包括以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标。即，在本公开的一个实施例中，特征数据可以包括用户的支付行为画像和用户画像。在本公开的一个实施例中，特征数据除了包括用户的支付行为画像和用户画像之外，还可以包括以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标。
在本公开的一个实施例中，可以使用预设的支付工具推荐模型对用户的支付行为画像、用户画像以及以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标进行分析，生成按照支付成功率从高到低排序的支付工具顺序。在本公开的一个实施例中，支付环境数据可以指的是例如特定移动设备型号和网络环境等。外部资金渠道运行状况可以指的是例如银行之类的外部资金渠道是否异常以及外部资金渠道的限额等。产品业务指标指的是调用支付工具的产品的月活跃用户数、日活跃用户数、用户留存率、用户增长率等。
在本公开的一个实施例中，用户的支付行为画像体现用户的支付行为，并且包括以下特征中的至少之一：用户的历史支付记录、历史支付频次、支付额度、支付工具喜好、支付成功率、支付工具健康度、支付工具切换率、支付工具稳定性、信贷资产使用习惯。
在本公开的实施例中，用户的历史支付记录指的是通过支付平台的收银台以及与支付平台合作的金融网络获取到的用户的历史支付记录。历史支付频次指的是用户在一定时间段内(一年内、3个月内、一个月内、7天内、1天内)使用某支付工具的次数。支付额度指的是每个支付工具的额度。支付工具喜好指的是用户自身使用某支付工具的 次数相对于所有交易次数的百分比。支付成功率指的是用户实际支付成功笔数除以用户发起支付笔数。在一个实施例中，支付成功率还可以包括支付工具支付成功率，支付工具支付成功率指的是用户的支付工具实际支付成功笔数除以用户用支付工具发起的支付笔数。支付工具健康度可以指的是支付工具的运营状态。支付工具切换率指的是用户切换支付工具后支付成功笔数除以用户发起支付笔数。支付工具稳定性指的是用户用一支付工具支付成功笔数除以用户通过该支付工具发起支付笔数。
在本公开的一个实施例中，用户画像体现用户属性，并且包括以下特征中的至少之一：用户的年龄、职业、性别、资产情况和支付投诉记录。
根据本公开的相关描述，本领域技术人员可以理解，特征数据不限于前述内容，还可以根据需要采用其他类型的特征数据。
在本公开的一个实施例中，支付确认指令可以是用户下达的立即支付的指令，也可以是用户与支付平台签署代扣协议后被执行的扣款指令。
在本公开的一个实施例中，预设的支付工具推荐模型是逻辑回归模型。
根据本公开的相关描述，本领域技术人员可以理解，只要能够对特征数据进行分析来生成按照支付成功率从高到低排序的支付工具顺序，支付工具推荐模型不限于逻辑回归模型，还可以根据需要采用其他类型的模型。
在本公开实施方式中，通过获取为用户进行支付工具推荐时所需的特征数据，其中，特征数据包括用户的支付行为画像；使用预设的支付工具推荐模型对特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；从用户接收支付确认指令；响应于支付确认指令，在支付工具呈现页面按照支付工具顺序呈现支付工具，可以为用户推荐支付成功率最高的支付工具，同时为用户推荐支付成功率最高的支付工具顺序，此外还优化了支付工具选择流程，避免因用户失误或者合作机构侧异常导致的只能根据用户默认设置(或习惯)确定的支付工具顺序，因此提高了支付效率。另外，支付工具顺序推荐完全在后台完成，不受限于支付设备，无需用户设置。因此，根据本公开的支付工具推荐方案提高了支付成功率，减轻了用户负担，提升了用户体验。
以下以三个场景作为示例来说明根据本公开实施方式的支付工具推荐方法。
场景1：用户对在支付平台上设置的支付工具顺序不敏感，支付时发生余额不足、卡不可用等报错，导致支付失败，需要用户重新切换支付工具才能完成支付。
通过根据本公开的支付工具推荐方法，可以推荐最可能成功的支付工具。例如， 用户因为运营活动设置了“支付工具1”作为首选支付工具(对自己设置了支付顺序不敏感)。通过累计分析用户一年的支付行为数据，发现用户每月月初时使用“支付工具2”支付更多(可能原因：月初通过支付工具2发工资)，所以在月初时间段给用户推荐“支付工具2”支付，而不是使用用户设置的“支付工具1”。
场景2：特定场景下用户在支付平台签署协议代扣，在无用户参与情况下，支付平台主动轮询用户名下支付工具用于扣款。根据默认顺序扣款时，会逐个轮询所有支付工具，直到扣款成功为止。如果遇到用户有多个支付工具，逐个轮询效率较低。
通过根据本公开的支付工具推荐方法，可以推荐最可能成功扣款的支付工具完成支付，提升扣款效率，节约扣款成本。
场景3：支付平台的当前合作银行机构支付渠道稳定性出现异常，用户在还未收到公告之前仍自主选择该机构银行卡进行支付，导致支付失败。
通过根据本公开的支付工具推荐方法，可以通过特征数据确定该银行支付通道异常，支付成功率低于日常水平，因此可以推荐最可能成功的支付工具，并且在从高到低排序的支付工具顺序中降低该银行支付通道的支付顺位。因此，可提升异常情况下的支付成功率。
以下参照图2对本公开的支付工具推荐装置进行描述。
图2示出根据本公开另一实施方式的支付工具推荐装置的结构框图。
如图2所示的支付工具推荐装置包括获取模块201、生成模块202、接收模块203和呈现模块204。
获取模块201被配置为获取为用户进行支付工具推荐时所需的特征数据，其中，特征数据包括用户的支付行为画像。
生成模块202被配置为使用预设的支付工具推荐模型对特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；
接收模块203被配置为从用户接收支付确认指令；
呈现模块204被配置为响应于支付确认指令，在支付工具呈现页面按照支付工具顺序呈现支付工具。
在本公开的一个实施例中，支付工具可以指的是用户进行支付时可选择用于支付的内外部借贷资产工具，例如，可以是银行信用卡、银行借记卡、支付平台自身运营 的网络支付工具等。
在本公开的一个实施例中，可以通过包括多个支付工具的支付平台以及其他合作机构来获取用户进行支付工具推荐时所需的特征数据。例如，可以通过支付平台的收银台以及与支付平台合作的金融网络来获取用户进行支付工具推荐时所需的特征数据。
在本公开的一个实施例中，特征数据还包括用户画像。在本公开的一个实施例中，可以使用预设的支付工具推荐模型对用户的支付行为画像和用户画像进行分析，生成按照支付成功率从高到低排序的支付工具顺序。
在本公开的一个实施例中，除了包括用户画像，特征数据还包括以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标。即，在本公开的一个实施例中，特征数据可以包括用户的支付行为画像和用户画像。在本公开的一个实施例中，特征数据除了包括用户的支付行为画像和用户画像之外，还可以包括以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标。
在本公开的一个实施例中，可以使用预设的支付工具推荐模型对用户的支付行为画像、用户画像以及以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标进行分析，生成按照支付成功率从高到低排序的支付工具顺序。在本公开的一个实施例中，支付环境数据可以指的是例如特定移动设备型号和网络环境等。外部资金渠道运行状况可以指的是例如银行之类的外部资金渠道是否异常以及外部资金渠道的限额等。产品业务指标指的是调用支付工具的产品的月活跃用户数、日活跃用户数、用户留存率、用户增长率等。
在本公开的一个实施例中，用户的支付行为画像体现用户的支付行为，并且包括以下特征中的至少之一：用户的历史支付记录、历史支付频次、支付额度、支付工具喜好、支付成功率、支付工具健康度、支付工具切换率、支付工具稳定性、信贷资产使用习惯。
在本公开的实施例中，用户的历史支付记录指的是通过支付平台的收银台以及与支付平台合作的金融网络获取到的用户的历史支付记录。历史支付频次指的是用户在一定时间段内(一年内、3个月内、一个月内、7天内、1天内)使用某支付工具的次数。支付额度指的是每个支付工具的额度。支付工具喜好指的是用户自身使用某支付工具的次数相对于所有交易次数的百分比。支付成功率指的是用户实际支付成功笔数除以用户发起支付笔数。在一个实施例中，支付成功率还可以包括支付工具支付成功率，支付工 具支付成功率指的是用户的支付工具实际支付成功笔数除以用户用支付工具发起的支付笔数。支付工具健康度可以指的是支付工具的运营状态。支付工具切换率指的是用户切换支付工具后支付成功笔数除以用户发起支付笔数。支付工具稳定性指的是用户用一支付工具支付成功笔数除以用户通过该支付工具发起支付笔数。
在本公开的一个实施例中，用户画像体现用户属性，并且包括以下特征中的至少之一：用户的年龄、职业、性别、资产情况和支付投诉记录。
根据本公开的相关描述，本领域技术人员可以理解，特征数据不限于前述内容，还可以根据需要采用其他类型的特征数据。
在本公开的一个实施例中，支付确认指令可以是用户下达的立即支付的指令，也可以是用户与支付平台签署代扣协议后被执行的扣款指令。
在本公开的一个实施例中，预设的支付工具推荐模型是逻辑回归模型。
根据本公开的相关描述，本领域技术人员可以理解，只要能够对特征数据进行分析来生成按照支付成功率从高到低排序的支付工具顺序，支付工具推荐模型不限于逻辑回归模型，还可以根据需要采用其他类型的模型。
在本公开实施方式中，通过获取模块，被配置为获取为用户进行支付工具推荐时所需的特征数据，其中，特征数据包括用户的支付行为画像；生成模块，被配置为使用预设的支付工具推荐模型对特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；接收模块，被配置为从用户接收支付确认指令；呈现模块，被配置为响应于支付确认指令，在支付工具呈现页面按照支付工具顺序呈现支付工具，可以为用户推荐支付成功率最高的支付工具，同时为用户推荐支付成功率最高的支付工具顺序，此外还优化了支付工具选择流程，避免因用户失误或者合作机构侧异常导致的只能根据用户默认设置(或习惯)确定的支付工具顺序，因此提高了支付效率。另外，支付工具顺序推荐完全在后台完成，不受限于支付设备，无需用户设置。因此，根据本公开的支付工具推荐方案提高了支付成功率，减轻了用户负担，提升了用户体验。
以上描述了文本内容标识装置的内部功能和结构，在一个可能的设计中，该文本内容标识装置的结构可实现为文本内容标识设备，如图3中所示，该处理设备300可以包括处理器301以及存储器302。
所述存储器302用于存储支持文本内容标识装置执行上述任一实施例中文本内容标识方法的程序，所述处理器301被配置为用于执行所述存储器302中存储的程序。
所述存储器302用于存储一条或多条计算机指令，其中，所述一条或多条计算机指令被所述处理器301执行。
所述处理器301用于执行前述各方法步骤中的全部或部分步骤。
其中，所述文本内容标识设备的结构中还可以包括通信接口，用于文本内容标识设备与其他设备或通信网络通信。
本公开示例性实施例还提供了一种计算机存储介质，用于储存所述文本内容标识装置所用的计算机软件指令，其包含用于执行上述任一实施例中文本内容标识方法所涉及的程序。
图4是适于用来实现根据本公开一实施方式的文本内容标识方法的计算机系统的结构示意图。
如图4所示，计算机系统400包括中央处理单元(CPU)401，其可以根据存储在只读存储器(ROM)402中的程序或者从存储部分408加载到随机访问存储器(RAM)403中的程序而执行上述图1所示的实施方式中的各种处理。在RAM403中，还存储有系统400操作所需的各种程序和数据。CPU401、ROM402以及RAM403通过总线404彼此相连。输入/输出(I/O)接口405也连接至总线404。
以下部件连接至I/O接口405：包括键盘、鼠标等的输入部分406；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分407；包括硬盘等的存储部分408；以及包括诸如LAN卡、调制解调器等的网络接口卡的通信部分409。通信部分409经由诸如因特网的网络执行通信处理。驱动器410也根据需要连接至I/O接口405。可拆卸介质411，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器410上，以便于从其上读出的计算机程序根据需要被安装入存储部分408。
特别地，根据本公开的实施方式，上文参考图1描述的方法可以被实现为计算机软件程序。例如，本公开的实施方式包括一种计算机程序产品，其包括有形地包含在及其可读介质上的计算机程序，所述计算机程序包含用于执行图1的数据处理方法的程序代码。在这样的实施方式中，该计算机程序可以通过通信部分409从网络上被下载和安装，和/或从可拆卸介质411被安装。
附图中的流程图和框图，图示了按照本公开各种实施方式的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，路程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含 一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，并且/或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施方式中所涉及到的单元或模块可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的单元或模块也可以设置在处理器中，这些单元或模块的名称在某种情况下并不构成对该单元或模块本身的限定。
作为另一方面，本公开还提供了一种计算机可读存储介质，该计算机可读存储介质可以是上述实施方式中所述装置中所包含的计算机可读存储介质；也可以是单独存在，未装配入设备中的计算机可读存储介质。计算机可读存储介质存储有一个或者一个以上程序，所述程序被一个或者一个以上的处理器用来执行描述于本公开的方法。
以上描述仅为本公开的较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本公开中所涉及的发明范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离所述发明构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本公开中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。

Claims (14)

  1. 一种支付工具推荐方法，其特征在于，包括：
    获取为用户进行支付工具推荐时所需的特征数据，其中，所述特征数据包括所述用户的支付行为画像；
    使用预设的支付工具推荐模型对所述特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；
    从所述用户接收支付确认指令；
    响应于所述支付确认指令，在支付工具呈现页面按照所述支付工具顺序呈现支付工具。
  2. 根据权利要求1所述的方法，其特征在于，所述特征数据还包括用户画像。
  3. 根据权利要求2所述的方法，其特征在于，所述特征数据还包括以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标。
  4. 根据权利要求1所述的方法，其特征在于，所述支付行为画像体现用户的支付行为，并且包括以下特征中的至少之一：用户的历史支付记录、历史支付频次、支付额度、支付工具喜好、支付成功率、支付工具健康度、支付工具切换率、支付工具稳定性、信贷资产使用习惯。
  5. 根据权利要求2所述的方法，其特征在于，所述用户画像体现用户属性，并且包括以下特征中的至少之一：用户的年龄、职业、性别、资产情况和支付投诉记录。
  6. 根据权利要求1所述的方法，其特征在于，所述预设的支付工具推荐模型是逻辑回归模型。
  7. 一种支付工具推荐装置，其特征在于，包括：
    获取模块，被配置为获取为用户进行支付工具推荐时所需的特征数据，其中，所述特征数据包括所述用户的支付行为画像；
    生成模块，被配置为使用预设的支付工具推荐模型对所述特征数据进行分析，生成按照支付成功率从高到低排序的支付工具顺序；
    接收模块，被配置为从所述用户接收支付确认指令；
    呈现模块，被配置为响应于所述支付确认指令，在支付工具呈现页面按照所述支付工具顺序呈现支付工具。
  8. 根据权利要求7所述的装置，其特征在于，所述特征数据还包括用户画像。
  9. 根据权利要求8所述的装置，其特征在于，所述特征数据还包括以下至少之一：用户征信数据、支付环境数据、外部资金渠道运行状况和产品业务指标。
  10. 根据权利要求7所述的装置，其特征在于，所述支付行为画像体现用户的支付行为，并且包括以下特征中的至少之一：用户的历史支付记录、历史支付频次、支付额度、支付工具喜好、支付成功率、支付工具健康度、支付工具切换率、支付工具稳定性、信贷资产使用习惯。
  11. 根据权利要求8所述的装置，其特征在于，所述用户画像体现用户属性，并且包括以下特征中的至少之一：用户的年龄、职业、性别、资产情况和支付投诉记录。
  12. 根据权利要求7所述的装置，其特征在于，所述预设的支付工具推荐模型是逻辑回归模型。
  13. 一种电子设备，其特征在于，包括存储器和处理器；其中，所述存储器用于存储一条或多条计算机指令，其中，所述一条或多条计算机指令被所述处理器执行以实现如权利要求1-6任一项所述的方法。
  14. 一种计算机可读存储介质，其上存储有计算机指令，其特征在于，该计算机指令被处理器执行时实现如权利要求1-6任一项所述的方法。
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