CN108667893A - Data recommendation method, device and electronic equipment - Google Patents

Data recommendation method, device and electronic equipment Download PDF

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Publication number
CN108667893A
CN108667893A CN201810185129.2A CN201810185129A CN108667893A CN 108667893 A CN108667893 A CN 108667893A CN 201810185129 A CN201810185129 A CN 201810185129A CN 108667893 A CN108667893 A CN 108667893A
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China
Prior art keywords
flow
data
shunt
shunting
flow shunt
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Granted
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CN201810185129.2A
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Chinese (zh)
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CN108667893B (en
Inventor
周志超
熊军
周峰
蒋建
黄国进
郑岩
冯健
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Advanced New Technologies Co Ltd
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Alibaba Group Holding Ltd
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Priority to CN201810185129.2A priority Critical patent/CN108667893B/en
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Classifications

    • H04L67/55
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/06Buying, selling or leasing transactions
    • G06Q30/0601Electronic shopping
    • G06Q30/0631Item recommendations
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00Traffic regulation in packet switching networks
    • H04L47/10Flow control or congestion control
    • H04L47/24Flow control or congestion control depending on the type of traffic, e.g. priority or quality of service [QoS]
    • H04L47/2441Flow classification

Abstract

This application discloses a kind of data recommendation method, device and electronic equipment, this method includes:Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;The corresponding data characteristics in flow shunt section is obtained from feature database, wherein, the corresponding data characteristics in flow shunt section in the feature database is the data characteristics that the offline flow for being diverted to the flow shunt section based on the default Diffluence Algorithm and the shunting parameter passes through that data mining obtains;Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate.

Description

Data recommendation method, device and electronic equipment
Technical field
This application involves a kind of internet arena more particularly to data recommendation method, device and electronic equipments.
Background technology
With the development of mobile Internet and big data technology, the marketing activity of the enterprises such as electric business, operator is also all being enclosed It is excavated and is expanded around user data, the potential demand of user can be transferred through user data and predict, these user data May include online user's data and offline user data.It effectively excavates, identify potential user, and then targetedly to not Same user implements different recommendation results, precision marketing can be realized, to improve the conversion ratio of website user.
Since number of users is more in website, the data volume of user data is larger, when recommending user, it usually needs Processes user data, is easy computationally intensive because of user data, causes the recommendation of user data less efficient.
Invention content
A kind of data recommendation method of the embodiment of the present application offer, device and electronic equipment, for improving data recommendation efficiency.
The embodiment of the present application uses following technical proposals:
In a first aspect, providing a kind of data recommendation method, this method includes:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt area in the feature database Between corresponding data characteristics, be that the flow shunt section is diverted to based on the default Diffluence Algorithm and the shunting parameter Offline flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate In.
Second aspect, provides a kind of data recommendation device, which includes:
Determination unit is determined based on default Diffluence Algorithm and shunting parameter in the corresponding flow shunt section of linear flow rate;
Acquiring unit obtains the corresponding data characteristics in flow shunt section, wherein flowed in the feature database from feature database The corresponding data characteristics in amount shunting section, is to be diverted to the flow point based on the default Diffluence Algorithm and the shunting parameter The offline flow in stream section passes through the data characteristics that data mining obtains;
The acquiring unit, data characteristics also based on flow shunt section obtain that the flow shunt section is corresponding to push away Recommend data;
The corresponding recommending data in the flow shunt section is pushed to and belongs to the flow shunt section by push unit In linear flow rate.
The third aspect, it is proposed that a kind of electronic equipment, the electronic equipment include:
Processor;And
It is arranged to the memory of storage computer executable instructions, the executable instruction makes the place when executed It manages device and executes following operation:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt area in the feature database Between corresponding data characteristics, be that the flow shunt section is diverted to based on the default Diffluence Algorithm and the shunting parameter Offline flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate In.
Fourth aspect, it is proposed that a kind of computer readable storage medium, the computer-readable recording medium storage one Or multiple programs, one or more of programs by the electronic equipment including multiple application programs when being executed so that the electricity Sub- equipment executes following operation:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt area in the feature database Between corresponding data characteristics, be that the flow shunt section is diverted to based on the default Diffluence Algorithm and the shunting parameter Offline flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate In.
Above-mentioned at least one technical solution that the embodiment of the present application uses can reach following advantageous effect:
By being pushed away to being shunted in linear flow rate, and based on the corresponding off-line data feature determination in shunting section after shunting Recommend data, then will shunt the corresponding recommending data in section be pushed to shunting section it is corresponding in linear flow rate, so as to subtract The small calculation amount brought by on-line analysis data characteristics, and then improve and recommend efficiency.
Description of the drawings
Attached drawing described herein is used for providing further understanding of the present application, constitutes part of this application, this Shen Illustrative embodiments and their description please do not constitute the improper restriction to the application for explaining the application.In the accompanying drawings:
Fig. 1 is one embodiment data recommendation method flow chart of the application.
Fig. 2 is the interaction diagrams of one embodiment data recommendation of the application.
Fig. 3 is the structural schematic diagram of one embodiment electronic equipment of the application.
Fig. 4 is the structural schematic diagram of one embodiment data recommendation device of the application.
Specific implementation mode
To keep the purpose, technical scheme and advantage of the application clearer, below in conjunction with the application specific embodiment and Technical scheme is clearly and completely described in corresponding attached drawing.Obviously, described embodiment is only the application one Section Example, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not doing Go out the every other embodiment obtained under the premise of creative work, shall fall in the protection scope of this application.
Fig. 1 is a kind of data recommendation method flow chart of one embodiment of the application.
S110 is determined based on default Diffluence Algorithm and shunting parameter in the corresponding flow shunt section of linear flow rate.
It should be understood that in the embodiment of the present application, shunting parameter that can be based on preset Diffluence Algorithm and flow, to flowing online Amount is classified.
Parameter is shunted, for example, may include user identifier in linear flow rate, position, timestamp, Internet protocol IP At least one of location, MAC address, timestamp etc..Certainly, it is also not excluded for using the other parameters in linear flow rate As shunting parameter.
Default Diffluence Algorithm, such as, it may include it is following at least one:Hash seed Hash seed Diffluence Algorithms, AB tests Diffluence Algorithm, etc..
Below using Hash seed Diffluence Algorithms as Diffluence Algorithm, and illustrated using user identifier as shunting parameter.No Harm assumes there are tetra- flows of A, B, C, D, carries out Hash operation according to user identifier, respectively obtains 0,1,0,1 four value, then can incite somebody to action A, C is divided into a flow shunt section, and B, D are divided into another flow shunt section.It, might as well be by A, C for ease of difference Flow shunt section be named as section 1, the flow shunt section of B, D are named as section 2.
S120 obtains the corresponding data characteristics in flow shunt section from feature database.
Wherein, the corresponding data characteristics in flow shunt section in the feature database, be based on the default Diffluence Algorithm and The offline flow that the shunting parameter is diverted to the flow shunt section passes through the data characteristics that data mining obtains.
It should be understood that in the embodiment of the present application, for offline flow, also use Diffluence Algorithm identical with step S110 and Shunting parameter is shunted, and carries out data mining based on the flow after shunting, corresponding to obtain each flow shunt section Data characteristics.
For example, offline flow is equally using Hash seed Diffluence Algorithms as Diffluence Algorithm, and using user identifier as shunting ginseng Number to obtain flow shunt section (section 1 and section 2), and then obtains data characteristics and the area in section 1 by data mining Between 2 data characteristics.
At this point, according to the flow shunt section after line traffic partition, it is special that the corresponding data in flow shunt section can be obtained Sign.
S130, the data characteristics based on flow shunt section obtain the corresponding recommending data in the flow shunt section.
In the embodiment of the present application, it is based on data characteristics, it is corresponding data characteristics can be obtained from recommendation database Recommending data.
The corresponding recommending data in the flow shunt section is pushed to and belongs to the online of the flow shunt section by S140 In flow.
After the corresponding recommending data in flow shunt section being obtained by step S130, you can the recommending data is pushed to category In the flow shunt section in linear flow rate.
In the embodiment of the present application, by being shunted in linear flow rate, and based on the shunting section after shunting it is corresponding from Line data characteristics determines recommending data, then will shunt the corresponding recommending data in section and is pushed to the corresponding online stream in shunting section In amount, so as to reduce the calculation amount brought by on-line analysis data characteristics, and then improves and recommend efficiency.
It should be understood that flow shunt section of the linear flow rate after shunting can only there are one, can also include multiple.
Optionally, if described in the corresponding flow shunt section of linear flow rate includes first flow shunting section and second Amount shunting section, then step S120 specifically can be achieved be:
Obtain corresponding first data characteristics in first flow shunting section;
Obtain corresponding second data characteristics in second flow shunting section.
Further, step S130, which can be realized, is:
The first recommending data is determined based on corresponding first data characteristics in first flow shunting section;
The second recommending data is determined based on corresponding second data characteristics in second flow shunting section.
Further, step S140, which can be realized, is:
First recommending data of online flow feedback in section is shunted to first flow;
Second recommending data of online flow feedback in section is shunted to second flow.
It should be understood, of course, that before step S110, this method may also include:
Based on the default Diffluence Algorithm and the shunting parameter, the corresponding flow shunt section of offline flow is determined;
Data mining is carried out based on the offline flow in flow shunt section, it is special to obtain the corresponding data in flow shunt section Sign.
It should be understood that when linear flow rate shunts, it is possible to use only a kind of one level shunt of Diffluence Algorithm progress also may be used To use many algorithms, multi-stage diffluence is carried out.
Optionally, step S110 specifically can be achieved be:
Based on the first Diffluence Algorithm and the first shunting parameter, determine that the first flow belonging to linear flow rate shunts section;
Based on the second Diffluence Algorithm and the second shunting parameter, is shunted to first flow being divided again in linear flow rate in section Stream, to determine that the second flow belonging to linear flow rate in first flow shunting section shunts section.
It should be understood, of course, that for a kind of Diffluence Algorithm, shunting parameter may include an attribute of flow, may also comprise A variety of attributes of flow.In the following, will be further described through in conjunction with specific embodiments to the method for the embodiment of the present application.
Fig. 2 is the interaction diagrams of one embodiment data recommendation of the application.As shown in Fig. 2, in data recommendation process In, it may include online traffic handling portion and offline traffic handling portion.
In offline traffic handling portion, it may include:
1.1 off-line datas import
The user data of offline flow can specifically be imported into offline diverter module by the step, to make offline divergent die Block shunts the user data of offline flow.
User data at this, for example, may include user identifier, user access web page contents, input keyword, Current position and terminal device information etc..
1.2 read default Diffluence Algorithm and shunting parameter
The step can specifically read from shunting synchronization module and preset Diffluence Algorithm and shunting parameter.
Preferably as one embodiment, the default Diffluence Algorithm that step 1.2 is read is the Diffluence Algorithm of AB tests, is read The shunting parameter taken is user identifier.
1.3 offline flow shunts
Based on the above-mentioned default Diffluence Algorithm read and shunting parameter, the user data of offline flow is shunted, In the embodiment, it can specifically be divided into first flow shunting section and second flow shunting section, wherein data mining A modules Section can be shunted to first flow and carry out data characteristics excavation;Data mining B modules can to second flow shunt section into Row data feature mining.
1.4 data characteristicses export
Specifically, data mining A modules carry out data characteristics excavation to first flow shunting section, obtain the spy of A versions Sign;Data mining B modules carry out data characteristics excavation to second flow shunting section, the feature of B versions are obtained, for above-mentioned A The feature of the feature and B versions of version, specifically for example, being the feature etc. that can reflect user's Income situation.In addition, the step is also The above-mentioned feature by the feature of A versions and B versions can be imported into feature database.
Online traffic handling portion, it may include:
2.1 flows are retrieved
The step specifically can will imported into online diverter module in the user data of linear flow rate, to make online divergent die Block shunts the user data in linear flow rate, to determine in the corresponding flow shunt section of linear flow rate.
User data at this, it is similar with offline traffic handling portion, such as may include that user identifier, user access Web page contents, the keyword of input, current position and terminal device information etc..
2.2 read default Diffluence Algorithm and shunting parameter
The step corresponds to the 1.2 of above-mentioned offline traffic handling portion, can specifically be read from shunting synchronization module default Diffluence Algorithm and shunting parameter, what is read with above-mentioned offline traffic handling portion is identical default Diffluence Algorithm and identical point Parameter is flowed, for example, default Diffluence Algorithm is the Diffluence Algorithm of AB tests, the shunting parameter of reading is user identifier.
2.3 online flow shunts
Based on the above-mentioned default Diffluence Algorithm read and shunting parameter, the user data in linear flow rate is shunted, In the embodiment, it can specifically be divided into first flow shunting section and second flow shunting section, wherein online logic A modules Section is shunted corresponding to processing first flow, online logic B modules correspond to second flow and shunt section.
2.4 obtain the corresponding data characteristics in flow shunt section from feature database
The step, online logic A modules correspond to the feature for reading A versions, and online logic B modules, which correspond to, reads B versions Feature.
2.5 push recommending datas
Specifically, in the step, the feature that online logic A modules shunt the A versions in section based on first flow obtains the One recommending data, and the first recommending data is pushed to belong to first flow shunting section in linear flow rate;Online logic B moulds The feature that block shunts the B versions in section based on second flow obtains the second recommending data, and the second recommending data is pushed to category In second flow shunting section in linear flow rate.
Recommending data in the embodiment, if for example, the feature of A versions and the feature of B versions are specifically that can reflect use The feature etc. of family Income situation, the recommending data at this are recommended for example, in first flow shunting section to booming income user The larger tourism route of spending amount, the tourism route etc. for recommending spending amount smaller to low income user;In second flow point It flows in section, recommends approximate tourism route of spending amount etc. to booming income user and low income user.
In addition, the embodiment can also assess above-mentioned first flow shunting according to the feedback result after push recommending data The quality etc. of the recommending data in recommending data and second flow the shunting section in section.
In the embodiment of the present application, by being shunted in linear flow rate, and based on the shunting section after shunting it is corresponding from Line data characteristics determines recommending data, then will shunt the corresponding recommending data in section and is pushed to the corresponding online stream in shunting section In amount, so as to reduce the calculation amount brought by on-line analysis data characteristics, and then improves and recommend efficiency.
It is shunted by the user data to offline flow, to all user data of offline flow Reason, due to distributing offline flow user data of the user data much smaller than whole of offline flow, to improve at data Manage efficiency.
The data recommendation method according to the embodiment of the present application is described in detail above in association with Fig. 1 and Fig. 2.Below in conjunction with figure 3 are described in detail the electronic equipment according to the embodiment of the present application.With reference to figure 3, in hardware view, electronic equipment includes processor, can Selection of land, including internal bus, network interface, memory.Wherein, memory may be deposited comprising memory, such as high random access Reservoir (Random-Access Memory, RAM), it is also possible to further include nonvolatile memory (non-volatile Memory), for example, at least 1 magnetic disk storage etc..Certainly, which, which is also possible that, realizes that other business are required Hardware.
Processor, network interface and memory can be connected with each other by internal bus, which can be industry Standard architecture (Industry Standard Architecture, ISA) bus, Peripheral Component Interconnect standard (Peripheral Component Interconnect, PCI) bus or expanding the industrial standard structure (Extended Industry Standard Architecture, EISA) bus etc..The bus can be divided into address bus, data/address bus, Controlling bus etc..For ease of indicating, only indicated with a four-headed arrow in Fig. 3, it is not intended that an only bus or one kind The bus of type.
Memory, for storing program.Specifically, program may include program code, and said program code includes calculating Machine operational order.Memory may include memory and nonvolatile memory, and provide instruction and data to processor.
Processor is from then operation in corresponding computer program to memory is read in nonvolatile memory, in logical layer The device of forwarding chat message is formed on face.Processor executes the program that memory is stored, and specifically for executing following behaviour Make:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt area in the feature database Between corresponding data characteristics, be that the flow shunt section is diverted to based on the default Diffluence Algorithm and the shunting parameter Offline flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate In.
The method that the above-mentioned data recommendation method as disclosed in the application Fig. 1 and embodiment illustrated in fig. 2 executes can be applied to In processor, or realized by processor.Processor may be a kind of IC chip, the processing capacity with signal. During realization, each step of the above method can pass through the integrated logic circuit of the hardware in processor or software form Instruction is completed.Above-mentioned processor can be general processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network Processor, NP) etc.;It can also be digital signal processor (Digital Signal Processor, DSP), it is application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), existing It is field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete Door or transistor logic, discrete hardware components.It may be implemented or execute the disclosed each side in the embodiment of the present application Method, step and logic diagram.General processor can be microprocessor or the processor can also be any conventional processing Device etc..The step of method in conjunction with disclosed in the embodiment of the present application, can be embodied directly in hardware decoding processor and execute completion, Or in decoding processor hardware and software module combination execute completion.Software module can be located at random access memory, dodge It deposits, read-only memory, this fields such as programmable read only memory or electrically erasable programmable memory, register are ripe to deposit In storage media.The storage medium is located at memory, and processor reads the information in memory, and the above method is completed in conjunction with its hardware The step of.
The method that the electronic equipment can also carry out Fig. 1 and Fig. 2, and realize that data recommendation method is implemented shown in Fig. 1 and Fig. 2 The function of example, details are not described herein for the embodiment of the present application.
Certainly, other than software realization mode, other realization methods are not precluded in the electronic equipment of the application, for example patrol Collect the mode etc. of device or software and hardware combining, that is to say, that the executive agent of following process flow is not limited to each patrol Unit is collected, can also be hardware or logical device.
The embodiment of the present application also proposed a kind of computer readable storage medium, the computer-readable recording medium storage one A or multiple programs, the one or more program include instruction, which works as is held by the electronic equipment including multiple application programs When row, the method that the electronic equipment can be made to execute Fig. 1 and embodiment illustrated in fig. 2, and specifically for executing following methods:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt area in the feature database Between corresponding data characteristics, be that the flow shunt section is diverted to based on the default Diffluence Algorithm and the shunting parameter Offline flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate In.
Fig. 4 is the structural schematic diagram of one embodiment data recommendation device of the application, as shown in figure 4, the device includes:
Determination unit 401 is determined based on default Diffluence Algorithm and shunting parameter in the corresponding flow shunt area of linear flow rate Between;
Acquiring unit 402 obtains the corresponding data characteristics in flow shunt section, wherein the feature database from feature database The corresponding data characteristics in middle flow shunt section is to be diverted to the stream based on the default Diffluence Algorithm and the shunting parameter The offline flow in amount shunting section passes through the data characteristics that data mining obtains;
The acquiring unit 402, the data characteristics also based on flow shunt section obtain the flow shunt section and correspond to Recommending data;
The corresponding recommending data in the flow shunt section is pushed to and belongs to the flow shunt area by push unit 403 Between in linear flow rate.
Data recommendation device provided in an embodiment of the present invention, by being shunted in linear flow rate, and based on shunting after The corresponding off-line data feature in shunting section determines recommending data, then will shunt the corresponding recommending data in section and is pushed to shunting Section is corresponding in linear flow rate, so as to reduce the calculation amount brought by on-line analysis data characteristics, and then improves and recommends Efficiency.
Optionally, as one embodiment, above-mentioned data recommendation device (can not also scheme including data characteristics processing module Show), data characteristics nursing module can be used for:
Based on the default Diffluence Algorithm and the shunting parameter, the corresponding flow shunt section of offline flow is determined;
Data mining is carried out based on the offline flow in flow shunt section, it is special to obtain the corresponding data in flow shunt section Sign;
It will be in flow shunt section and the storage to the feature database of corresponding data characteristics.
Optionally, in the corresponding flow shunt section of linear flow rate include first flow if described as one embodiment It shunts section and second flow shunts section, then the corresponding data characteristics in the acquisition flow shunt of acquiring unit 402 section includes:
Obtain corresponding first data characteristics in first flow shunting section;
Obtain corresponding second data characteristics in second flow shunting section.
Optionally, as one embodiment, data characteristics of the acquiring unit 402 based on flow shunt section obtains the stream The corresponding recommending data in amount shunting section, including:
The first recommending data is determined based on corresponding first data characteristics in first flow shunting section;
The second recommending data is determined based on corresponding second data characteristics in second flow shunting section.
Optionally, as one embodiment, push unit 403 pushes the corresponding recommending data in the flow shunt section To belong to the flow shunt section in linear flow rate, including:
Push unit 403 shunts first recommending data of online flow feedback in section to first flow;
Second recommending data of online flow feedback in section is shunted to second flow.
Optionally, as one embodiment, determination unit 401 is based on default Diffluence Algorithm and shunting parameter, determines online Flow shunt section belonging to flow, including:
Determination unit 401 is based on the first Diffluence Algorithm and the first shunting parameter, determines the first flow belonging to linear flow rate Shunt section;
Based on the second Diffluence Algorithm and the second shunting parameter, is shunted to first flow being divided again in linear flow rate in section Stream, to determine that the second flow belonging to linear flow rate in first flow shunting section shunts section.
Optionally, as one embodiment, the shunting parameter includes user identifier, position, the Internet protocol of flow At least one of IP address, MAC address, timestamp.
Optionally, as one embodiment, the default Diffluence Algorithm includes following at least one:
The Diffluence Algorithm that Hash seed Hash seed Diffluence Algorithms, AB are tested.
Above-mentioned data recommendation device according to the ... of the embodiment of the present invention is referred to the data of the corresponding embodiment of the present invention above The flow of recommendation method, also, each unit/module in the data recommendation device and other above-mentioned operation and/or functions point Not in order to realize the corresponding flow in data recommendation method, for sake of simplicity, details are not described herein.
It should be understood by those skilled in the art that, embodiments herein can be provided as method, system or computer program Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application Apply the form of example.Moreover, the application can be used in one or more wherein include computer usable program code computer The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) The form of product.
The application is with reference to method, the flow of equipment (system) and computer program product according to the embodiment of the present application Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one The step of function of being specified in a box or multiple boxes.
In a typical configuration, computing device includes one or more processors (CPU), input/output interface, net Network interface and memory.
Memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method Or technology realizes information storage.Information can be computer-readable instruction, data structure, the module of program or other data. The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), moves State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable Programmable read only memory (EEPROM), fast flash memory bank or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM), Digital versatile disc (DVD) or other optical storages, magnetic tape cassette, tape magnetic disk storage or other magnetic storage apparatus Or any other non-transmission medium, it can be used for storage and can be accessed by a computing device information.As defined in this article, it calculates Machine readable medium does not include temporary computer readable media (transitory media), such as data-signal and carrier wave of modulation.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability Including so that process, method, commodity or equipment including a series of elements include not only those elements, but also wrap Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including element There is also other identical elements in process, method, commodity or equipment.
It these are only embodiments herein, be not intended to limit this application.To those skilled in the art, The application can have various modifications and variations.It is all within spirit herein and principle made by any modification, equivalent replacement, Improve etc., it should be included within the scope of claims hereof.

Claims (11)

1. a kind of data recommendation method, including:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt section pair in the feature database The data characteristics answered is to be diverted to the offline of the flow shunt section based on the default Diffluence Algorithm and the shunting parameter Flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate.
2. the method as described in claim 1 is determined based on default Diffluence Algorithm and shunting parameter in the corresponding stream of linear flow rate Before amount shunting section, the method further includes:
Based on the default Diffluence Algorithm and the shunting parameter, the corresponding flow shunt section of offline flow is determined;
Data mining is carried out based on the offline flow in flow shunt section, obtains the corresponding data characteristics in flow shunt section;
It will be in flow shunt section and the storage to the feature database of corresponding data characteristics.
In the corresponding flow shunt section of linear flow rate include first flow point if described 3. the method as described in claim 1 It flows section and second flow shunts section, then obtaining the corresponding data characteristics in flow shunt section includes:
Obtain corresponding first data characteristics in first flow shunting section;
Obtain corresponding second data characteristics in second flow shunting section.
4. method as claimed in claim 3,
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section, including:
The first recommending data is determined based on corresponding first data characteristics in first flow shunting section;
The second recommending data is determined based on corresponding second data characteristics in second flow shunting section.
5. method as claimed in claim 4,
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate, packet It includes:
First recommending data of online flow feedback in section is shunted to first flow;
Second recommending data of online flow feedback in section is shunted to second flow.
6. the method as described in any one of claim 1-5, which is characterized in that
Based on default Diffluence Algorithm and shunting parameter, the flow shunt section belonging to linear flow rate is determined, including:
Based on the first Diffluence Algorithm and the first shunting parameter, determine that the first flow belonging to linear flow rate shunts section;
Based on the second Diffluence Algorithm and the second shunting parameter, being shunted again in linear flow rate in section is shunted to first flow, To determine that the second flow belonging to linear flow rate in first flow shunting section shunts section.
7. the method as described in any one of claim 1-5,
The shunting parameter include the user identifier of flow, position, internet protocol address, MAC address, At least one of timestamp.
8. the method as described in any one of claim 1-5,
The default Diffluence Algorithm includes following at least one:
The Diffluence Algorithm that Hash seed Hash seed Diffluence Algorithms, AB are tested.
9. a kind of data recommendation device, including:
Determination unit is determined based on default Diffluence Algorithm and shunting parameter in the corresponding flow shunt section of linear flow rate;
Acquiring unit obtains the corresponding data characteristics in flow shunt section from feature database, wherein flow point in the feature database The corresponding data characteristics in section is flowed, is that the flow shunt area is diverted to based on the default Diffluence Algorithm and the shunting parameter Between offline flow pass through the obtained data characteristics of data mining;
The acquiring unit, the data characteristics also based on flow shunt section obtain the corresponding recommendation number in the flow shunt section According to;
The corresponding recommending data in the flow shunt section is pushed to and belongs to the online of the flow shunt section by push unit In flow.
10. a kind of electronic equipment, including:
Processor;And
It is arranged to the memory of storage computer executable instructions, the executable instruction makes the processor when executed Execute following operation:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt section pair in the feature database The data characteristics answered is to be diverted to the offline of the flow shunt section based on the default Diffluence Algorithm and the shunting parameter Flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate.
11. a kind of computer readable storage medium, the computer-readable recording medium storage one or more program, described one A or multiple programs by the electronic equipment including multiple application programs when being executed so that the electronic equipment executes following behaviour Make:
Based on default Diffluence Algorithm and shunting parameter, determine in the corresponding flow shunt section of linear flow rate;
The corresponding data characteristics in flow shunt section is obtained from feature database, wherein flow shunt section pair in the feature database The data characteristics answered is to be diverted to the offline of the flow shunt section based on the default Diffluence Algorithm and the shunting parameter Flow passes through the data characteristics that data mining obtains;
Data characteristics based on flow shunt section obtains the corresponding recommending data in the flow shunt section;
The corresponding recommending data in the flow shunt section is pushed to belong to the flow shunt section in linear flow rate.
CN201810185129.2A 2018-03-07 2018-03-07 Data recommendation method and device and electronic equipment Active CN108667893B (en)

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