CN110267104A - A kind of user matching method and device - Google Patents
A kind of user matching method and device Download PDFInfo
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- CN110267104A CN110267104A CN201910590321.4A CN201910590321A CN110267104A CN 110267104 A CN110267104 A CN 110267104A CN 201910590321 A CN201910590321 A CN 201910590321A CN 110267104 A CN110267104 A CN 110267104A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/4508—Management of client data or end-user data
- H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4661—Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4662—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms
- H04N21/4665—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms involving classification methods, e.g. Decision trees
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application
- H04N21/4788—Supplemental services, e.g. displaying phone caller identification, shopping application communicating with other users, e.g. chatting
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- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The embodiment of the present application discloses a kind of user matching method and device, for promoting social depth, and improves viewing experience.The embodiment of the present application method includes: the viewing request for receiving the first video user and sending；Group belonging to first video user is obtained, the group is classified to obtain according to user characteristic data；Obtain the second video user that viewing request is sent in the group；The target video user to match with first video user is obtained from second video user according to preset matching rule；The target video user and first video user are formed into common viewing group.
Description
Technical field
This application involves computer field more particularly to a kind of user matching methods and device.
Background technique
Start to provide a kind of new video-see mode in current video application (APP), i.e., more people watch same portion simultaneously
Video is chatted when seeing.User can by way of random fit, form group with stranger together with watch video.At random
Matched mode directly affects successful match rate and organizes interior the activity of the user.
Current random fit system is mostly relatively simple direct.It is small to one to have plenty of completely random matching stranger
Group；Has plenty of the priority match opposite sex or with city user etc..
These matching systems there is a problem of common, i.e., matching dimensionality is relatively simple, and matching logic is fairly simple.Pass through this
A little random fit system matches often lack topics common to user together, and no words can be chatted, terribly cold clear in viewing group, have
It dismisses soon.Do not achieve the purpose that strange user's depth is facilitated to interact.
Summary of the invention
The embodiment of the present application provides a kind of user matching method and device, for promoting social depth, and improves viewing
Experience.
Specific as follows in a first aspect, the embodiment of the present application provides a kind of user matching method: user's coalignment receives the
The viewing request that one video user is sent；Then user's coalignment obtains group belonging to first video user, the group
Group is to be classified to obtain according to the user characteristic data of video user by user's coalignment；User's coalignment obtains
The second video user of viewing request is sent in the group, and is obtained from second video user according to preset matching rule and
The target video user that first video user matches；Finally user's coalignment by target video user and this first
Video user forms common viewing group.
In the present embodiment, which is first grouped to raw each video user according to user characteristic data
At different groups, matching point then is carried out to the user for initiating viewing request further according to preset matching rule in group
Group, to realize that viewing group is social frequently, reaches preferable viewing body to guarantee can there is topics common between user
It tests.
Optionally, the method which is grouped generation group particularly for video user is as follows: the use
Family coalignment obtains the user characteristic data of each video user in Video Applications, and the Video Applications are the video user
Video supplier；Each video user is classified as N number of group according to the user characteristic data by user's coalignment.
Optionally, the user characteristic data includes but is not limited to user's viewing behavioral data, user tag, Yong Hunian
Age, user's gender, area belonging to user.
Optionally, which is classified as N number of group for each video user according to the user characteristic data
Group includes: user's coalignment using clustering algorithm according to the user characteristic data by each video user real-time grading
For N number of group.
Optionally, the clustering algorithm includes but is not limited to K-Means algorithm, K-Medians algorithm.
Optionally, the preset matching rule includes but is not limited to wherein at least one: anisotropic user is mutually matched, same to city
User is mutually matched or is mutually matched with age bracket user.
Second aspect, the embodiment of the present application provides a kind of user's coalignment, specific as follows:
In a kind of possible implementation, which includes:
Receiving module, for receiving the viewing request of the first video user transmission；
Module is obtained, for obtaining group belonging to first video user, the group is according to user characteristic data
Classified to obtain；Obtain the second video user that viewing request is sent in the group；
Matching module is used for being obtained from second video user according to preset matching rule with first video
The target video user that family matches, the Video Applications are the video supplier of the video user；By the target video
User and first video user form common viewing group.
Optionally, the acquisition module, is also used to obtain the user characteristic data of each video user in Video Applications；
Described device further includes processing module, for being classified as each video user according to the user characteristic data
N number of group.
Optionally, the user characteristic data includes but is not limited to user's viewing behavioral data, user tag, Yong Hunian
Age, user's gender, area belonging to user.
Optionally, the processing module, being specifically used for will be described each according to the user characteristic data using clustering algorithm
Video user real-time grading is N number of group.
Optionally, the clustering algorithm includes but is not limited to K-Means algorithm or K-Medians algorithm.
Optionally, the preset matching rule includes but is not limited to wherein at least one: anisotropic user is mutually matched, same to city
User is mutually matched or is mutually matched with age bracket user.
In alternatively possible implementation, which includes processor and memory, wherein the memory
In have a computer-readable program, the processor is by running the program in the memory, with any of the above-described for completing
Method described in.
The third aspect, the embodiment of the present application provide a kind of computer readable storage medium, and the computer storage medium is deposited
Computer instruction is contained, the computer instruction is for executing method described in any of the above embodiments.
Fourth aspect, the embodiment of the present application provide a kind of computer program product comprising instruction, when its on computers
When operation, so that computer executes method described in any of the above embodiments.
As can be seen from the above technical solutions, the embodiment of the present application have the advantage that user's coalignment elder generation according to
User characteristic data is grouped each video user to generate different groups, then further according to preset in group
Matched packet is carried out to the user for initiating viewing request with rule, to guarantee there can be topics common between user, thus real
Existing viewing group is social frequently, reaches preferable viewing experience.
Detailed description of the invention
Fig. 1 is one embodiment schematic diagram of user matching method in the embodiment of the present application；
Fig. 2 is the schematic diagram that user's matching is the viewing interface after common viewing group in the embodiment of the present application；
Fig. 3 is one embodiment schematic diagram of user's coalignment in the embodiment of the present application；
Fig. 4 is another embodiment schematic diagram of user's coalignment in the embodiment of the present application.
Specific embodiment
The embodiment of the present application provides a kind of user matching method and device, for promoting social depth, and improves viewing
Experience.
The description and claims of this application and term " first ", " second ", " third ", " in above-mentioned attached drawing
The (if present)s such as four " are to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should manage
The data that solution uses in this way are interchangeable under appropriate circumstances, so that the embodiments described herein can be in addition to illustrating herein
Or the sequence other than the content of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that
Cover it is non-exclusive include, for example, containing the process, method, system, product or equipment of a series of steps or units need not limit
In step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, produce
The other step or units of product or equipment inherently.
Start to provide a kind of new video-see mode in current video application (APP), i.e., more people watch same portion simultaneously
Video is chatted when seeing.User can by way of random fit, form group with stranger together with watch video.At random
Matched mode directly affects successful match rate and organizes interior the activity of the user.Current random fit system is mostly relatively simple
Directly.Have plenty of completely random and matches stranger to a group；Has plenty of the priority match opposite sex or with city user etc..These
Matching system there is a problem of common, i.e., matching dimensionality is relatively simple, and matching logic is fairly simple.Pass through these random fit systems
The user being matched to together that unites often lacks topics common, and no words can be chatted, terribly cold clear in viewing group, some dismission soon
?.Do not achieve the purpose that strange user's depth is facilitated to interact.
In order to solve this problem, the embodiment of the present application provides a kind of user matching method, specifically includes: user matches dress
It sets and receives the viewing request that the first video user is sent；Then user's coalignment obtains group belonging to first video user
Group, the group are to be classified to determine according to the user characteristic data of video user by user's coalignment；User's coalignment
The second video user for sending viewing request in the group is obtained, and is obtained from second video user according to preset matching rule
Take the target video user to match with first video user；Finally user's coalignment by target video user and is somebody's turn to do
First video user forms common viewing group.
It is understood that user's coalignment can be terminal, chip in the embodiment of the present application；It is also possible to software
Program, concrete condition is herein without limitation.
Referring specifically to shown in Fig. 1, one embodiment of user matching method includes: in the embodiment of the present application
101, user's coalignment receives the viewing request that the first video user is initiated；
User's coalignment can be the video software programs including user's matching feature, be also possible to carry the journey
The chip of sequence be also possible to include the function a terminal.Being with user's coalignment in the embodiment of the present application includes use
It is illustrated for the video software programs of family matching feature.When first video user uses video software, first view
Frequency user sends viewing to user's coalignment by user's matching feature button in the video software or other modes and asks
It asks, wherein the viewing is requested for requesting common viewing.For example first video user is watching film A using video software
When, want to be watched together with other users, then first video user can initiate the viewing request of film A.
102, user's coalignment obtains group belonging to first video user；
User's coalignment obtains first video after receiving the viewing request of first video user transmission
Group information belonging to user.In the present embodiment, group belonging to first video user is user's coalignment according to view
The user characteristic data of each video user carries out the group of classification generation in frequency application.Wherein, which can be video
Software, then video user is registration user or the registered members in video software, and viewing group is exactly that video software provides
The common viewing personnel of a certain portion's film or the videos such as TV play or variety.For example video software can be iqiyi.com, depending on
Frequency user is exactly the registration user or member of iqiyi.com, and viewing group is exactly a certain portion provided in iqiyi.com video database
The common viewing personnel of the videos such as film or TV play or variety.Wherein, the user characteristic data including but not limited to
Family viewing behavioral data (for example usually 7 points to 10 points viewing videos, weekend can watch variety show, usually more hobbies at night
Watch documentary film etc.), user tag, age of user, user's gender, the information such as area belonging to user.For example user tag can be with
It is user from the label word (such as literature and art youth, rock and roll fan) set, is also possible to other good friends to the mark of the user setting
Sign word (such as close friend, mild, opinion is original).And specific user's coalignment uses video according to the user characteristic data
The concrete mode that family is classified can be such that user's coalignment will be each according to the user characteristic data using clustering algorithm
Video user real-time grading is N number of group.Optionally, which includes but is not limited to K-Means algorithm, K-Medians
Algorithm.User's coalignment can also can be used after collecting the user characteristic data of video user in video simultaneously
Cluster Classification is carried out in the state that family is offline can also carry out Cluster Classification in the state that the video user is online.Due to video
User base number is very big, and it is more long that full dose polymerize whole station user characteristic data time-consuming, and polymerization result is not in obvious change every time
Change, thus user's coalignment can choose off-line analysis processing it is proper.Real-time calculation processing more accurately matches,
Matching efficiency is promoted with this, it is time-consuming to reduce matching.Meanwhile in the use process of the video user, the user of the video user
Characteristic may change, therefore the iteration of Cluster Classification updates duration and can first preset, and update within such as one day one
Or Monday update.User's coalignment can also be according to the relevant parameter of setting, and automatic iteration of modifying updates duration.Such as
Change rate is more than upper limit preset threshold, then shortens iteration and update duration；Change rate is lower than lower limit preset threshold, then increases iteration more
New duration.
103, user's coalignment obtains the second video user that viewing request is sent in the group；
User's coalignment obtains the second video user that viewing request is equally initiated in the group.It can be understood that
Second video user is also to have initiated film A common viewing request.
104, user's coalignment is obtained from second video user and first video according to preset matching rule
The target video user that user matches；
User's coalignment is after getting while initiating the second video user of common viewing request, according to default
Matching rule obtains the target video user to match with first video user from second video user.The present embodiment
In, which includes but is not limited to wherein at least one: anisotropic user is mutually matched, be mutually matched with city user or
With age bracket, user is mutually matched.
105, target video user and first video user are formed common viewing group by user's coalignment.
User's coalignment uses target video user and first video after determining target video user
Family is divided into common viewing group.At this moment target video user and the one kind at the viewing interface of first video user are exemplary
Mode, can as shown in Figure 2 (by taking the interface of computer end as an example), and top half is video playing picture, and lower half portion is to use
Discussion interface between family.Other certain interface forms can also be with, for example when discussing that interface does not have new information, can hide,
Pop-up when having new information；For example it is video playing interface, discussion interface etc. of the right between user, tool that interface, which is divided into the left side,
Body situation is herein without limitation.It is understood that above-mentioned function also may be implemented in mobile terminal.
In the present embodiment, which is first grouped to raw each video user according to user characteristic data
At different groups, matching point then is carried out to the user for initiating viewing request further according to preset matching rule in group
Group, to realize that viewing group is social frequently, reaches preferable viewing body to guarantee can there is topics common between user
It tests.
Described above is user matching methods in the embodiment of the present application, below to user's coalignment in the embodiment of the present application
It is described.Referring specifically to shown in Fig. 3, one embodiment of user's coalignment includes: in the embodiment of the present application
Receiving module 301, for receiving the viewing request of the first video user transmission；
Module 302 is obtained, for obtaining group belonging to first video user, the group is according to user characteristics number
According to being classified to obtain；Obtain the second video user that viewing request is sent in the group；
Matching module 303, for being obtained from second video user according to preset matching rule and first view
The target video user that frequency user matches；The target video user and the common viewing of first video user composition is small
Group.
Optionally, the acquisition module 301, is also used to obtain the user characteristic data of each video user in Video Applications,
The Video Applications are the video supplier of the video user；
Described device further includes processing module 304, for being divided each video user according to the user characteristic data
Class is N number of group.
Optionally, the user characteristic data includes but is not limited to user's viewing behavioral data, user tag, Yong Hunian
Age, user's gender, area belonging to user.
Optionally, the processing module 304, being specifically used for will be described according to the user characteristic data using clustering algorithm
Each video user real-time grading is N number of group.
Optionally, the clustering algorithm includes but is not limited to K-Means algorithm or K-Medians algorithm.
Optionally, the preset matching rule includes but is not limited to wherein at least one: anisotropic user is mutually matched, same to city
User is mutually matched or is mutually matched with age bracket user.
Referring specifically to shown in Fig. 4, another embodiment of user's coalignment includes: in the embodiment of the present application
Transceiver 401, processor 402, bus 403；
The transceiver 401 is connect with the processor 402 by the bus 403；
The bus 403 can be Peripheral Component Interconnect standard (peripheral component interconnect,
PCI) bus or expanding the industrial standard structure (extended industry standard architecture, EISA) bus
Deng.The bus can be divided into address bus, data/address bus, control bus etc..For convenient for indicating, only with a thick line table in Fig. 4
Show, it is not intended that an only bus or a type of bus.
Processor 402 can be central processing unit (central processing unit, CPU), network processing unit
The combination of (network processor, NP) or CPU and NP.
Processor 402 can further include hardware chip.Above-mentioned hardware chip can be specific integrated circuit
(application-specific integrated circuit, ASIC), programmable logic device (programmable
Logic device, PLD) or combinations thereof.Above-mentioned PLD can be Complex Programmable Logic Devices (complex
Programmable logic device, CPLD), field programmable gate array (field-programmable gate
Array, FPGA), Universal Array Logic (generic array logic, GAL) or any combination thereof.
Shown in Figure 4, which can also include memory 404.The memory 404 may include volatile
Property memory (volatile memory), such as random access memory (random-access memory, RAM)；Memory
It also may include nonvolatile memory (non-volatile memory), such as flash memory (flash memory), firmly
Disk (hard disk drive, HDD) or solid state hard disk (solid-state drive, SSD)；Memory 404 can also include
The combination of the memory of mentioned kind.
Optionally, memory 404 can be also used for storage program instruction, and processor 402 calls to be stored in the memory 404
Program instruction, the one or more steps or in which optional embodiment in above-mentioned multiple embodiments can be executed, realize
The function of user's coalignment in the above method.
The transceiver 401 executes following steps: receiving the viewing request that the first video user is sent；
The processor 402 executes following steps: obtain group belonging to first video user, the group according to
User characteristic data is classified to obtain；Obtain the second video user that viewing request is sent in the group；According to default
The target video user to match with first video user is obtained from second video user with rule；By the mesh
It marks video user and first video user forms common viewing group.
Optionally, the processor 402 also executes the following steps: the user characteristics for obtaining each video user in Video Applications
Data, the Video Applications are the video supplier of the video user；According to the user characteristic data by each video
User is classified as N number of group.
Optionally, the user characteristic data includes but is not limited to user's viewing behavioral data, user tag, Yong Hunian
Age, user's gender, area belonging to user.
Optionally, the processor 402, specifically executes following steps: using clustering algorithm according to the user characteristic data
It is N number of group by each video user real-time grading.
Optionally, the clustering algorithm includes but is not limited to K-Means algorithm or K-Medians algorithm.
Optionally, the preset matching rule includes but is not limited to wherein at least one: anisotropic user is mutually matched, same to city
User is mutually matched or is mutually matched with age bracket user.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the application
Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.
The above, above embodiments are only to illustrate the technical solution of the application, rather than its limitations；Although referring to before
Embodiment is stated the application is described in detail, those skilled in the art should understand that: it still can be to preceding
Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
It modifies or replaces, the spirit and scope of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution.
Claims (13)
1. a kind of user matching method characterized by comprising
Receive the viewing request of the first video user transmission；
Group belonging to first video user is obtained, the group is classified to obtain according to user characteristic data；
Obtain the second video user that viewing request is sent in the group；
The target to match with first video user is obtained from second video user according to preset matching rule to regard
Frequency user；
The target video user and first video user are formed into common viewing group.
2. the method according to claim 1, wherein the method also includes:
The user characteristic data of each video user in Video Applications is obtained, the Video Applications are that the video of the video user supplies
Ying Fang；
Each video user is classified as N number of group according to the user characteristic data.
3. according to the method described in claim 2, it is characterized in that, it is described according to the user characteristic data by each video
User is classified as N number of group
Using clustering algorithm according to the user characteristic data by each video user real-time grading be N number of group.
4. the method according to claim 1, wherein the user characteristic data includes but is not limited to user's viewing
Behavioral data, user tag, age of user, user's gender, area belonging to user.
5. method according to claim 1 to 4, which is characterized in that the preset matching rule includes but not
Be limited to wherein at least one: anisotropic user is mutually matched, is mutually matched with city user or is mutually matched with age bracket user.
6. a kind of user's coalignment characterized by comprising
Receiving module, for receiving the viewing request of the first video user transmission；
Module is obtained, for obtaining group belonging to first video user, the group carries out according to user characteristic data
Classification obtains；Obtain the second video user that viewing request is sent in the group；
Matching module, for being obtained and the first video user phase from second video user according to preset matching rule
Matched target video user；The target video user and first video user are formed into common viewing group.
7. device according to claim 6, which is characterized in that the acquisition module is also used to obtain in Video Applications each
The user characteristic data of video user, the Video Applications are the video supplier of the video user；
Described device further includes processing module, N number of for being classified as each video user according to the user characteristic data
Group.
8. device according to claim 7, which is characterized in that the processing module is specifically used for utilizing clustering algorithm root
According to the user characteristic data by each video user real-time grading be N number of group.
9. device according to claim 6, which is characterized in that the user characteristic data includes but is not limited to user's viewing
Behavioral data, user tag, age of user, user's gender, area belonging to user.
10. device according to any one of claims 6 to 9, which is characterized in that the preset matching rule includes but not
Be limited to wherein at least one: anisotropic user is mutually matched, is mutually matched with city user or is mutually matched with age bracket user.
11. a kind of user's coalignment, which is characterized in that including processor and memory, wherein there is meter in the memory
Calculation machine readable program, the processor is by running the program in the memory, for completing to appoint in claim 1 to 5
Method described in one.
12. a kind of computer readable storage medium, including instruction, when described instruction is run on computers, the computer
The claims 1 are executed to the method described in any one of claim 5.
13. a kind of computer program product comprising instruction, described when the computer program product is run on computers
Computer executes the claims 1 to the method described in any one of claim 5.
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| CN201910590321.4A CN110267104A (en) | 2019-07-02 | 2019-07-02 | A kind of user matching method and device |
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