WO2016177278A1 - 一种匹配业务场景的方法和系统 - Google Patents

一种匹配业务场景的方法和系统 Download PDF

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Publication number
WO2016177278A1
WO2016177278A1 PCT/CN2016/079811 CN2016079811W WO2016177278A1 WO 2016177278 A1 WO2016177278 A1 WO 2016177278A1 CN 2016079811 W CN2016079811 W CN 2016079811W WO 2016177278 A1 WO2016177278 A1 WO 2016177278A1
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Prior art keywords
matched
user
product
matching
scene
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Ceased
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PCT/CN2016/079811
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English (en)
French (fr)
Chinese (zh)
Inventor
沈栋
肖汉平
刘堂衡
叶家杰
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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Application filed by Alibaba Group Holding Ltd filed Critical Alibaba Group Holding Ltd
Priority to SG11201708717RA priority Critical patent/SG11201708717RA/en
Priority to JP2017557445A priority patent/JP6745817B2/ja
Priority to KR1020177033969A priority patent/KR102125120B1/ko
Priority to EP16789272.8A priority patent/EP3293688A4/en
Publication of WO2016177278A1 publication Critical patent/WO2016177278A1/zh
Priority to US15/800,823 priority patent/US11010783B2/en
Anticipated expiration legal-status Critical
Priority to US17/322,894 priority patent/US11481798B2/en
Ceased legal-status Critical Current

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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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0242—Determining effectiveness of advertisements
    • G06Q30/0243—Comparative campaigns
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • G06Q30/00—Commerce
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications

Definitions

  • the present application relates to the field of Internet technologies, and in particular, to a method and system for matching a service scenario.
  • Websites or service providers can create different scenarios by categorizing and assembling different pages.
  • a scene can correspond to a web page or a collection of hierarchical web pages, and can also include a page popped up by clicking a page button.
  • Common scenarios are, for example, the categorization and aggregation of web pages in accordance with the subject matter and/or functionality involved.
  • Each sub-station can aggregate multiple pages:
  • each substation scene may involve multiple specific scenarios.
  • Sina Sports sports.sina.com.cn
  • NBA also includes NBA, CBA, Chinese football, international football and other scenarios
  • Sina Technology also includes mobile phones, Multiple scenes such as cameras and home appliances; and so on.
  • an access port of a different type of service can be set.
  • the Alipay wallet main page can set up access ports for different types of services including airline tickets, movies, taxis, and express delivery.
  • the scenario may also correspond to these different access ports.
  • the web service provider or application service provider needs to set up further business interfaces in different scenarios.
  • the application of the mobile terminal may need to set a new incoming star on the page of the express access port jump.
  • the business interface of the delivery for example, the website service provider needs to set the business interface of a newly entered brand car in the car sub-station, such as clicking the button of a certain page to pop up the relevant business interface of the brand car (such as purchase consultation or reservation purchase, etc.) ) pages; and, for example, setting advertisements in different scenarios.
  • the content to be set can be referred to as a product, such as the above-mentioned star express, event promotion of a certain brand car, advertisement, and the like.
  • a scenario in which a product needs to be adapted is generally determined by a website service provider or an application service provider based on the amount of access of each scenario.
  • the page view generally includes a page view amount or a click amount, and a page count or a click amount of the page is calculated once for a normal user every time the page is refreshed.
  • the amount of access to the page in the scene can be counted as the amount of access to the scene.
  • the main substation of Sina has been sorted by total visits in the most recent month, as shown in Figure 1.
  • the Sina blog has the highest number of visits, and the existing technology often adapts the automobile product to the scene, and sets the business interface in the scene of the Sina blog.
  • a Star Express service interface is also adapted to the taxi scenario in the application based on the traffic of different scenarios.
  • the product and the scene are adapted according to the amount of access, and the product is relatively large, and it is difficult to adapt the product to a truly suitable scene.
  • the above-mentioned car products set up a business interface in the Sina blog scene, which is not a good choice. It may be a more suitable choice for the Sina car scene; the above-mentioned star express setting is a better choice in the express delivery scene.
  • An object of the embodiments of the present application is to provide a method and system for matching a service scenario to match a product into a more suitable scenario.
  • a method for matching a business scenario including:
  • a system for matching business scenarios including:
  • An obtaining unit configured to acquire a user feature of the access user corresponding to different scenarios and a matching feature of the product to be matched;
  • a calculating unit calculating a user feature mapping value of each scene based on the matching feature of the product to be matched
  • a selecting unit configured to select a service scenario of the product to be matched according to the user feature mapping value.
  • the technical solution provided by the embodiment of the present application can be used to calculate the user feature mapping value of each scenario based on the matching feature of the product to be matched, and the user feature mapping that is more compatible with the product to be matched can be calculated.
  • the value is used to select the corresponding scenario as the business scenario of the product to be matched according to the calculated user feature mapping value.
  • the scene 4 for example, a car scene
  • the scene 3 a courier product
  • the foregoing embodiment of the present application considers the characteristics of the product to be matched, that is, the user feature mapping value of each scene is calculated according to the matching feature of the product to be matched, so that the different scenarios and the products to be matched can be more realistically reflected.
  • the degree of fit can help to choose the right business scenario.
  • FIG. 1 is a schematic diagram of sorting the total number of visits by the main substations of Sina in the recent month according to the present application;
  • FIG. 2 is a flow chart of an embodiment of a method according to the present application.
  • FIG. 3 is a block diagram of a system embodiment of the present application.
  • FIG. 4 is a block diagram of a system embodiment of the present application.
  • FIG. 5 is a block diagram of a system embodiment of the present application.
  • the embodiment of the present application provides a method and system for matching a service scenario.
  • a method implementation method for matching a service scenario includes:
  • S110 Acquire user characteristics of the access user corresponding to the different scenarios and matching characteristics of the products to be matched.
  • the user accesses different scenarios set by the website service provider or the application service provider through the terminal or the application, and the server of the website service provider or the application service provider can obtain or record the user characteristics of the access user.
  • These user features may include, for example, certain registration information of the user, such as ID, gender, age, etc.; may also include items of interest set by the user, such as registered hobbies, subscription layouts, favorite pages, etc.; The historical behavior of the user about the website or application, such as the total amount of maternal and child transactions in the website or application providing the shopping, the total amount of home appliances transactions or the total amount of outdoor transactions.
  • the above various features can be used as a basis for subsequent evaluation of user feature mapping values of different scenarios.
  • the user feature may be obtained from an access record and/or registration information.
  • the access feature is, for example, in the form of ⁇ X1, X2, X3, . . . , wherein X1, X2, and X3 may specifically represent the following content:
  • Each user may have the above access features or may have different access features.
  • the server of the website service provider or the application service provider can obtain the user characteristics of the access user corresponding to different scenarios.
  • the products to be matched can be products that need to be adapted to the truly suitable scene.
  • different products to be matched have different matching characteristics.
  • a maternal and child product such as a maternal and child advertisement or application interface
  • Amount the above-mentioned content is generally reflected in the user characteristics.
  • an automotive product such as a car product purchase consulting interface
  • the corresponding matching features can be determined according to the prior selection.
  • the server of the website service provider or the application service provider can obtain the matching characteristics of the products to be matched.
  • S120 Calculate a user feature mapping value of each scene based on the matching feature of the product to be matched.
  • the product to be matched has matching characteristics ⁇ X2, X3, X5, X8 ⁇ .
  • the user feature mapping values of each scenario may be calculated according to the matching features of the products to be matched.
  • the feature values of the corresponding X2, X3, X5, and X8 in the user characteristics of each user in the scenario 1 may be quantized and averaged as the user feature mapping value of the scenario, such as the user feature mapping.
  • Value Scenario 1 The eigenvalues corresponding to X2, X3, X5, and X8 in the user characteristics of each user in the scene 2 may be quantized and then averaged as the user feature map value of the scene, such as the user characteristic map value scene 2 .
  • this calculation method can obtain a relatively realistic value that reflects the relative fit of the scene to the product to be matched.
  • user feature map value scenario 1 1.5
  • user feature map value scenario 2 2.7.
  • the feature values of X2, X3, X5, and X8 in the user features of each user in the scenario 3 may be quantized and superimposed as the user feature mapping value of the scenario , such as the user feature mapping value scenario 3 ;
  • the feature values corresponding to X2, X3, X5, and X8 of each user's user feature are quantized and then averaged to be averaged as the user feature map value of the scene , such as user feature map value scene 4 .
  • this calculation method can obtain a relatively true value that reflects the relative fit of the scene to the product to be matched.
  • the user feature map value scene 3 15000
  • the user feature map value scene 4 7280.
  • the first user feature corresponding to the matching feature of the product to be matched in the user features of the corresponding user in different scenarios is quantized according to the same standard, and the user feature mapping value of the first user feature of the different scenario is obtained based on the same mapping rule.
  • the user feature map value of each scene as a subsequent selection criterion can be obtained.
  • S130 Select a service scenario of the product to be matched according to the user feature mapping value.
  • the matching features of the products to be matched are more consistent with the products to be matched.
  • the scenario with the high user feature mapping value can be selected as the service scenario of the product to be matched.
  • the user characteristic phase map value 3 repeat scenario where matching features is greater, and therefore the user characteristic map value is significantly larger than the scene 3 scene the user characteristic map value 4.
  • scenario 3 is more compatible with scenario 4 than the business scenario of the product to be matched.
  • the matching features of the repeatedly matched products to be accessed by the user in the current scenario will also make the current scenario more compatible with the products to be matched.
  • the products to be matched are maternal and child products, and the users who purchase the maternal and child products are mainly concentrated in scenario 2; thus, the access users in scenario 2 have a high impact on the user feature mapping values of the scene on certain matching features. For example, the impact of the higher total amount of maternal and child transactions.
  • the user feature mapping value scenario 1 is significantly smaller than the user feature mapping value scenario 2 according to the calculation manner in the above S120. In this way, scenario 2 is more compatible with scenario 1 and the business scenario of the product to be matched.
  • the matching characteristics of the products to be matched are less repeated in different scenarios, and the products to be matched are more suitable.
  • the scenario with the low user feature mapping value can be selected as the service scenario of the product to be matched. This is not limited.
  • the user feature mapping value of each scenario is calculated based on the matching feature of the product to be matched, and the user feature mapping value that is more compatible with the product to be matched may be calculated, thereby facilitating the calculation according to the calculation.
  • the user feature mapping value selects the corresponding scenario as the business scenario of the product to be matched.
  • the scene 4 for example, a car scene
  • the scene 3 a courier product
  • the newly introduced star express for the product to be matched it is obviously more suitable as the interface setting page of the newly introduced Star Express.
  • the foregoing embodiment of the present application considers the characteristics of the product to be matched, that is, the user feature mapping value of each scene is calculated according to the matching feature of the product to be matched, so that the different scenarios and the products to be matched can be more realistically reflected. The degree of fit can help to choose the right business scenario.
  • the matching feature of the product to be matched can be realized in an automatic manner in addition to being determined according to the prior selection.
  • the law from these big data by means of mature big data processing technology.
  • logistic regression GBD (Gradient Boosting Decision Tree), decision tree, and even machine learning methods such as deep learning can be used to model these big data to obtain potential key features of users who use such products. That is, the matching characteristics of the products to be matched.
  • the relationship between these features and the weights can also be obtained, and a unified equation or calculation formula can be obtained. This is equivalent to a relatively standard, targeted feature extraction problem.
  • Logistic regression is taken as an example to illustrate how to obtain suitable matching features by modeling, and how to obtain the range of values based on the matching features in different scenarios between 0 and 1.
  • User feature map value is taken as an example to illustrate how to obtain suitable matching features by modeling, and how to obtain the range of values based on the matching features in different scenarios between 0 and 1.
  • the main features can be selected through logistic regression. Also known as principal component analysis in some machine learning algorithms.
  • the main feature is the feature that reflects the main main concern of the product when matching the scene. Includes the following:
  • A1 Set all user characteristics according to the user and mark each user whether to use the target product. For example, a user feature of 1000 users of 1000 dimensions is set as a row by the user, and after the user feature of the user is marked, whether the user uses the target product, for example, for the target product, the usage of the user is marked as 1, The case of not using is marked as 0.
  • Such a record can be embodied in the form of a data sheet.
  • A2 Calculate the information value (Information Value) between all user characteristics of each user in A1 and whether or not to use the target product. For example, by means of principal component analysis, the degree of influence of each dimension on whether the user uses the target product can be calculated, and the degree of influence is used as the calculated factor, that is, the above information value.
  • A3 Sort by Information Value and retain the characteristics of the largest preset number of dimensions. For example, after 1000 dimension features are sorted according to Information Value, the first 200-dimensional features are retained from large to small.
  • A4 The preset number of dimensional features retained by each user and whether the target product is fitted by a logistic regression algorithm, according to the statistical significance requirement (p_value) in the logistic regression algorithm, the non-significant indicator is excluded and the significant indicator is retained. .
  • the logistic regression algorithm can be used to find out a plurality of dimensions that are most relevant to whether the user uses a certain product from a plurality of dimensions, that is, obtain matching features of the products to be matched.
  • mapping feature can be used in S120 to calculate a user feature mapping value of each scene based on the matching feature of the product to be matched. Since such a mapping relationship is obtained by the logistic regression algorithm for the collection, trial and trend simulation of big data, it can better reflect the user's characteristics. The relationship of the matching features is matched, and the relationship of the features to the user feature mapping values is matched.
  • FIG. 3 A system embodiment of a unit matching service scenario of the present application is described below with reference to FIG. 3. As shown in FIG. 3, the system includes:
  • the acquiring unit 310 is configured to acquire a user feature of the access user corresponding to the different scenarios and a matching feature of the product to be matched.
  • the calculating unit 320 calculates a user feature mapping value of each scene based on the matching feature of the product to be matched;
  • the selecting unit 330 is configured to select a service scenario of the product to be matched according to the user feature mapping value.
  • the obtaining unit 310 may include a first obtaining unit 311, configured to acquire, from the access record and/or the registration information, user characteristics of the access user corresponding to different scenarios.
  • the obtaining unit 310 may include a second obtaining unit 312, configured to determine matching features of the product to be matched according to a prior selection.
  • the computing unit 320 may include a quantization unit 321 and a mapping unit 322, where:
  • the quantization unit 321 is configured to quantize the first user feature corresponding to the matching feature of the product to be matched among the user features of the access user corresponding to different scenarios according to the same standard;
  • the mapping unit 322 is configured to obtain a user feature mapping value of the first user feature of the different scenario based on the same mapping rule.
  • the selecting unit may select a scenario with a low user feature mapping value as a service scenario of the product to be matched; or select a scenario with a high user feature mapping value as a service scenario of the product to be matched.
  • the acquiring unit acquires a matching feature of the product to be matched, and may acquire a matching feature of the product to be matched by using a machine learning method.
  • the mapping unit 322 may include a mapping manner obtaining unit 323 and a feature value calculating unit 324, where:
  • mapping manner obtaining unit 323, configured to obtain a first mapping manner by using a machine learning method
  • the feature value calculation unit 324 is configured to calculate a user feature mapping value of each scene by using the first mapping manner.
  • the above machine learning method may include at least one of a logistic regression algorithm, a GBDT algorithm, a decision tree algorithm, and a deep learning algorithm.
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • HDL Hardware Description Language
  • the controller can be implemented in any suitable manner, for example, the controller can take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (eg, software or firmware) executable by the (micro)processor.
  • computer readable program code eg, software or firmware
  • examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, The Microchip PIC18F26K20 and the Silicone Labs C8051F320, the memory controller can also be implemented as part of the memory's control logic.
  • the controller can be logically programmed by means of logic gates, switches, ASICs, programmable logic controllers, and embedding.
  • Such a controller can therefore be considered a hardware component, and the means for implementing various functions included therein can also be considered as a structure within the hardware component.
  • a device for implementing various functions can be considered as a software module that can be both a method of implementation and a structure within a hardware component.
  • the system, device, module or unit illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product having a certain function.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
  • RAM random access memory
  • ROM read only memory
  • Memory is an example of a computer readable medium.
  • Computer readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information storage can be implemented by any method or technology.
  • the information can be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, Magnetic cassette tape, magnetic tape storage or other magnetic storage device or any other non-transportable medium that can be used for storage can be calculated Information accessed by the device.
  • computer readable media does not include temporary storage of computer readable media, such as modulated data signals and carrier waves.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the application can be described in the general context of computer-executable instructions executed by a computer, such as a program module.
  • program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types.
  • the present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are connected through a communication network.
  • program modules can be located in both local and remote computer storage media including storage devices.

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PCT/CN2016/079811 2015-05-04 2016-04-21 一种匹配业务场景的方法和系统 Ceased WO2016177278A1 (zh)

Priority Applications (6)

Application Number Priority Date Filing Date Title
SG11201708717RA SG11201708717RA (en) 2015-05-04 2016-04-21 Service scenario matching method and system
JP2017557445A JP6745817B2 (ja) 2015-05-04 2016-04-21 サービスシナリオマッチング方法及びシステム
KR1020177033969A KR102125120B1 (ko) 2015-05-04 2016-04-21 서비스 시나리오 매칭 방법 및 시스템
EP16789272.8A EP3293688A4 (en) 2015-05-04 2016-04-21 Method and system for matching service scene
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190061431A (ko) * 2017-11-28 2019-06-05 동국대학교 산학협력단 설명력이 있는 딥 러닝 방법 및 시스템

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202088A (zh) 2015-05-04 2016-12-07 阿里巴巴集团控股有限公司 一种匹配业务场景的方法和系统
CN107066518B (zh) * 2017-02-08 2021-01-08 创新先进技术有限公司 数据处理方法及系统
CN107402881A (zh) * 2017-04-14 2017-11-28 阿里巴巴集团控股有限公司 一种项目测试的选取方法及装置
CN107590205A (zh) * 2017-08-23 2018-01-16 阿里巴巴集团控股有限公司 一种业务展示方法、装置及设备
CN107562461B (zh) * 2017-09-08 2021-09-03 北京京东尚科信息技术有限公司 特征计算系统及方法、存储介质和电子设备
CN109447776A (zh) * 2018-10-22 2019-03-08 中国银行股份有限公司 收税方法及装置、存储介质及电子设备
CN111078313A (zh) * 2019-12-09 2020-04-28 神州数码融信软件有限公司 一种业务参数配置方法
CN111242741B (zh) * 2020-01-15 2023-08-04 新石器慧通（北京）科技有限公司 一种基于场景的商品文案生成方法、系统及无人零售车
CN111383077A (zh) * 2020-03-09 2020-07-07 浙江大华技术股份有限公司 一种数据管理及客户选型系统、方法及装置
CN111428994B (zh) * 2020-03-20 2022-06-03 支付宝(杭州)信息技术有限公司 业务处理方法、装置及电子设备
US20240161130A1 (en) * 2022-11-15 2024-05-16 Kyndryl, Inc. Determining a new candidate feature for a predetermined product based on an implicit request of a user
CN116126976B (zh) * 2023-04-06 2023-07-04 之江实验室 一种数据同步的方法、装置、存储介质及电子设备
CN116527765A (zh) * 2023-05-29 2023-08-01 中国工商银行股份有限公司 一种用于场景式服务的方法、服务器及系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090063249A1 (en) * 2007-09-04 2009-03-05 Yahoo! Inc. Adaptive Ad Server
CN104077714A (zh) * 2014-06-16 2014-10-01 微梦创科网络科技（中国）有限公司 访问网站的用户的偏好获取、广告推送方法和系统
US20140304086A1 (en) * 2013-02-25 2014-10-09 Turn Inc. Methods and systems for modeling campaign goal adjustment
CN104317822A (zh) * 2014-09-29 2015-01-28 新浪网技术(中国)有限公司 网络用户的人口属性预测方法和装置

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS488207Y1 (enExample) 1966-12-08 1973-03-03
JP2003006224A (ja) * 2001-06-26 2003-01-10 Kawasaki Heavy Ind Ltd Ｗｗｗページの自動更新方法および自動更新装置
US8352499B2 (en) * 2003-06-02 2013-01-08 Google Inc. Serving advertisements using user request information and user information
US20080270398A1 (en) * 2007-04-30 2008-10-30 Landau Matthew J Product affinity engine and method
KR101049889B1 (ko) * 2007-10-22 2011-07-19 주식회사 이베이지마켓 검색을 통한 행태분석에 기반한 키워드 그룹에 대하여광고를 수주하고 타겟 광고하는 웹 사이트 운영 방법 및온라인 시스템
JP4808207B2 (ja) * 2007-12-11 2011-11-02 ヤフー株式会社 広告配信装置、広告配信方法、広告配信プログラム及び広告入札方法
JP5332356B2 (ja) 2008-07-08 2013-11-06 日産自動車株式会社 車両用運転支援装置
KR20100020365A (ko) * 2008-08-12 2010-02-22 건국대학교 산학협력단 상품 추천 시스템 및 그 추천방법
US20130013428A1 (en) * 2011-07-08 2013-01-10 Cbs Interactive Inc. Method and apparatus for presenting offers
CN102663026B (zh) * 2012-03-22 2015-09-23 浙江盘石信息技术股份有限公司 一种定向投放网络广告的实现方法
CN102708151A (zh) 2012-04-16 2012-10-03 广州市幻像信息科技有限公司 一种实现互联网情景论坛方法和装置
US9922303B2 (en) * 2012-08-30 2018-03-20 Oracle International Corporation Method and system for implementing product group mappings
JP5903376B2 (ja) * 2012-12-11 2016-04-13 日本電信電話株式会社 情報推薦装置、情報推薦方法、及び情報推薦プログラム
CN104679769B (zh) * 2013-11-29 2018-04-06 国际商业机器公司 对产品的使用场景进行分类的方法及装置
US20160055563A1 (en) * 2014-08-20 2016-02-25 Roopnath Grandhi Methods and systems of discovery of products in e-commerce
CN106202088A (zh) 2015-05-04 2016-12-07 阿里巴巴集团控股有限公司 一种匹配业务场景的方法和系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090063249A1 (en) * 2007-09-04 2009-03-05 Yahoo! Inc. Adaptive Ad Server
US20140304086A1 (en) * 2013-02-25 2014-10-09 Turn Inc. Methods and systems for modeling campaign goal adjustment
CN104077714A (zh) * 2014-06-16 2014-10-01 微梦创科网络科技（中国）有限公司 访问网站的用户的偏好获取、广告推送方法和系统
CN104317822A (zh) * 2014-09-29 2015-01-28 新浪网技术(中国)有限公司 网络用户的人口属性预测方法和装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3293688A4 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190061431A (ko) * 2017-11-28 2019-06-05 동국대학교 산학협력단 설명력이 있는 딥 러닝 방법 및 시스템
KR102093789B1 (ko) 2017-11-28 2020-03-27 동국대학교 산학협력단 설명력이 있는 딥 러닝 방법 및 시스템

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