CN107729560A - User's portrait building method, device and computing device based on big data - Google Patents
User's portrait building method, device and computing device based on big data Download PDFInfo
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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Abstract
The invention discloses a kind of user's portrait building method, device, computing device and computer-readable storage medium based on big data.Wherein method includes：Obtain user and operate the behavioural information of application program and the list information of the mounted application program of user terminal；Behavioural information is learnt using the first deep learning algorithm, obtains user behavior characteristic vector；The list information of the mounted application program of user terminal is learnt using the second deep learning algorithm, obtains the application features vector of the mounted application program of user terminal；Drawn a portrait based on user behavior characteristic vector and application features vector structuring user's.Technical scheme provided by the invention, user's portrait based on big data construction can fully reflect the feature of user, so as to provide the user the service of more becoming more meticulous according to user's portrait, overcome the metadata for relying only on user in the prior art, the low-quality defect for example, user caused by the structuring user's such as age, sex portrait draws a portrait.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to a kind of user's portrait building method based on big data,
Device, computing device and computer-readable storage medium.
Background technology
With the continuous development of network and information technology, all kinds of platform applications and give birth to, in order to improve the various functions of platform,
To facilitate platform to provide the user more preferable service, and the interests of protecting platform, it is to be understood that, every letter of analysis user
Breath.At present, the information of user is understood usually through the mode of structure user's portrait.
Existing user draws a portrait construction method, mainly according to the metadata of user, for example, sex, the age, occupation,
Constellation, height, body weight, shopping type, Brang Preference and/or income etc. carry out structuring user's portrait, and underuse because of interconnection
Net and caused big data, although the user's portrait constructed using a metadata can be instructed platform, not
The feature of user can be fully demonstrated, so that platform can not provide the user the service to become more meticulous, can not also be existed in user
To protecting platform interests during the behaviors such as fraud.
The content of the invention
In view of the above problems, it is proposed that the present invention so as to provide one kind overcome above mentioned problem or at least in part solve on
State the portrait of the user based on big data building method, device, computing device and the computer-readable storage medium of problem.
The building method according to an aspect of the invention, there is provided a kind of user based on big data draws a portrait, including：
Obtain user and operate the behavioural information of application program and the list information of the mounted application program of user terminal；
Behavioural information is learnt using the first deep learning algorithm, obtains user behavior characteristic vector；
The list information of the mounted application program of user terminal is learnt using the second deep learning algorithm, obtained
The application features vector of the mounted application program of user terminal；
Drawn a portrait based on user behavior characteristic vector and application features vector structuring user's.
Alternatively, behavioural information is learnt using the first deep learning algorithm, obtains user behavior characteristic vector and enter
One step includes：
The action trail of user's operation application program is obtained according to behavioural information；
The action trail of application program is operated according to user, generates behavior sequence；
Sequence Learning is carried out to behavior sequence using trained time recurrent neural network, obtains user behavior feature
Vector.
Alternatively, the list information using the second deep learning algorithm to the mounted application program of user terminal
Practise, the application features vector for obtaining the mounted application program of user terminal further comprises：
Using trained incorporation model to each being applied in the list information of the mounted application program of user terminal
Program is learnt, and obtains application features vector corresponding to each application program；
Calculation process is carried out using the application features vector of the multiple application programs of preset algorithm, has obtained user terminal
The application features vector of the application program of installation.
Alternatively, after structuring user's portrait, method also includes：Based on user's portrait sample training user interest hobby
Model；
The user's portrait input for being analysed to user is analyzed to user interest modeling hobbies, obtains the interest of the user
Hobby.
Alternatively, after structuring user's portrait, method also includes：Whether analysis user is drawn a portrait with abnormal based on user
Behavior.
Alternatively, behavioural information includes：Operation of the user to application program and the page residence time in application program.
According to another aspect of the present invention, there is provided a kind of user's portrait constructing apparatus based on big data, including：
Acquisition module, the behavioural information and the mounted application program of user terminal of application program are operated suitable for obtaining user
List information；
First study module, suitable for learning using the first deep learning algorithm to behavioural information, obtain user behavior
Characteristic vector；
Second study module, suitable for utilizing list of the second deep learning algorithm to the mounted application program of user terminal
Information is learnt, and obtains the application features vector of the mounted application program of user terminal；
User's portrait constructing module, suitable for based on user behavior characteristic vector and application features vector structuring user's picture
Picture.
Alternatively, the first study module further comprises：
Action trail generation unit, suitable for obtaining the action trail of user's operation application program according to behavioural information；
Behavior sequence generation unit, suitable for operating the action trail of application program according to user, generate behavior sequence；
First unit, suitable for carrying out sequence to behavior sequence using trained time recurrent neural network
Practise, obtain user behavior characteristic vector.
Alternatively, the second study module further comprises：
Second unit, suitable for the row using trained incorporation model to the mounted application program of user terminal
Each application program is learnt in table information, obtains application features vector corresponding to each application program；
Operation processing unit, carried out suitable for the application features vector using the multiple application programs of preset algorithm at computing
Reason, obtain the application features vector of the mounted application program of user terminal.
Alternatively, device also includes：Training module, suitable for based on user's portrait sample training user interest modeling hobbies；
Hobby analysis module, the user suitable for being analysed to user, which draws a portrait to input to user interest modeling hobbies, to be carried out
Analysis, obtains the hobby of the user.
Alternatively, device also includes：Abnormal behaviour analysis module, suitable for whether drawing a portrait analysis user with different based on user
Chang Hangwei.
Alternatively, behavioural information includes：Operation of the user to application program and the page residence time in application program.
According to another aspect of the invention, there is provided a kind of computing device, including：Processor, memory, communication interface and
Communication bus, processor, memory and communication interface complete mutual communication by communication bus；
Memory is used to deposit an at least executable instruction, and executable instruction makes computing device above-mentioned based on big data
User draws a portrait and operated corresponding to building method.
In accordance with a further aspect of the present invention, there is provided a kind of computer-readable storage medium, be stored with least one in storage medium
Executable instruction, executable instruction make computing device behaviour corresponding to user's portrait building method based on big data as described above
Make.
According to scheme provided by the invention, behavioural information and the user terminal for obtaining user's operation application program are mounted
The list information of application program, behavioural information is learnt using the first deep learning algorithm, obtain user behavior feature to
Amount, is learnt to the list information of the mounted application program of user terminal using the second deep learning algorithm, obtains user
The application features vector of the mounted application program of terminal, based on user behavior characteristic vector and application features vector
Structuring user's are drawn a portrait.Technical scheme provided by the invention, user's portrait based on big data construction can fully reflect user's
Feature, so as to easily provide the user the service of more becoming more meticulous according to user's portrait, overcome and rely only in the prior art
The metadata of user, for example, the low-quality defect of user's portrait caused by the structuring user's such as age, sex, income portrait.
Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And can be practiced according to the content of specification, and in order to allow above and other objects of the present invention, feature and advantage can
Become apparent, below especially exemplified by the embodiment of the present invention.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area
Technical staff will be clear understanding.Accompanying drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Limitation.And in whole accompanying drawing, identical part is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 shows the flow signal of user's portrait building method according to an embodiment of the invention based on big data
Figure；
Fig. 2 shows that the flow of user's portrait building method in accordance with another embodiment of the present invention based on big data is shown
It is intended to；
Fig. 3 shows that the structure journey of user's portrait constructing apparatus according to an embodiment of the invention based on big data is shown
It is intended to；
Fig. 4 shows the structure journey of user's portrait constructing apparatus in accordance with another embodiment of the present invention based on big data
Schematic diagram；
Fig. 5 shows a kind of structural representation of computing device according to an embodiment of the invention.
Embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in accompanying drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
Limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Completely it is communicated to those skilled in the art.
Fig. 1 shows the flow signal of user's portrait building method according to an embodiment of the invention based on big data
Figure.As shown in figure 1, this method comprises the following steps：
Step S100, obtain user and operate the behavioural information of application program and the row of the mounted application program of user terminal
Table information.
The behavioural information that user operates application program refers to caused behavioural information when user operates to application program,
Here behavioural information is not only additionally included in page institute's residence time of application program comprising specific operation, is operated according to user
Whether the behavior that the behavioural information of application program can analyze user is friendly, and the information that analysis user is of interest.
The list information of the mounted application program of user terminal have recorded the mounted application program of user terminal, user
The mounted application program of terminal is that a kind of side of user interest, demand etc. is embodied, according to the mounted application of user terminal
The list information of program carrys out structuring user's portrait, and platform can be facilitated to provide a user the service to become more meticulous according to user's portrait.
Step S101, behavioural information is learnt using the first deep learning algorithm, obtains user behavior characteristic vector.
After the behavioural information of user's operation application program is obtained according to step S100, the first deep learning can be utilized
Algorithm learns to behavioural information, behavioural information is learnt here primarily to obtain user behavior characteristic vector,
To represent that user operates the behavioural information of application program by vector form, it is easy to subsequent construction user to draw a portrait.
Step S102, the list information of the mounted application program of user terminal is carried out using the second deep learning algorithm
Study, obtain the application features vector of the mounted application program of user terminal.
The list information of the mounted application program of user terminal have recorded the mounted application program of user terminal, example
Such as, application program identification, version etc., although the mounted application program of user terminal is a kind of side of user interest, demand etc.
Face embody, according to the list information of the mounted application program of user terminal come structuring user's draw a portrait, can facilitate platform according to
User's portrait provides a user the service to become more meticulous, but directly list information is embodied in user's portrait, can not be to rear
It is continuous to provide the user any help of the service band that becomes more meticulous, therefore, it is also desirable to list information is handled accordingly, for example,
The list information of the mounted application program of user terminal is learnt using the second deep learning algorithm, obtains user terminal
The application features vector of mounted application program.
Step S103, drawn a portrait based on user behavior characteristic vector and application features vector structuring user's.
, can after user behavior characteristic vector and user behavior characteristic vector is obtained according to step S101 and step S102
To be drawn a portrait according to resulting user behavior characteristic vector and user behavior characteristic vector come structuring user's, user portrait can
The fully feature of reflection user, overcomes the metadata for relying only on user in the prior art, for example, age, sex, income etc.
User's portrait quality is low caused by structuring user's portrait, can not provide the user the defects of more becoming more meticulous service.
The method provided according to the above embodiment of the present invention, obtain behavioural information and user's end that user operates application program
The list information of mounted application program is held, behavioural information is learnt using the first deep learning algorithm, obtains user
Behavioural characteristic vector, the list information using the second deep learning algorithm to the mounted application program of user terminal
Practise, the application features vector of the mounted application program of user terminal is obtained, based on user behavior characteristic vector and application
Performance of program vector structuring user's are drawn a portrait.Technical scheme provided by the invention, user's portrait based on big data construction can fill
Divide the feature of reflection user, so as to easily provide the user the service of more becoming more meticulous according to user's portrait, overcome existing
The metadata of user is relied only in technology, for example, user draws a portrait caused by the structuring user's such as age, sex, income portrait
Low-quality defect.
Fig. 2 shows that the flow of user's portrait building method in accordance with another embodiment of the present invention based on big data is shown
It is intended to.As shown in Fig. 2 this method comprises the following steps：
Step S200, obtain user and operate the behavioural information of application program and the row of the mounted application program of user terminal
Table information.
Step S201, the action trail of user's operation application program is obtained according to behavioural information.
The behavioural information that user operates application program can intuitively reflect that user uses the situation of application program, according to row
Focus of the user to application program can be analyzed for information, or the operation that user carried out can be analyzed according to behavioural information
Whether it is a kind of friendly behavior.
For example, in general application program reuses the service that application program provides after requiring user's registration account,
And before user's registration, corresponding disclaimer can be shown to registered user, generally, friendly user can read accordingly
Disclaimer, know corresponding points for attention, while can also be fully understood by user's right, but non-friendly use for some
Family, it is simultaneously not concerned with having which clause in disclaimer, and is desirable to be rapidly completed registration, enters inside application program
The page, operated, such as borrow money etc..
In many cases, any exception may can not be found by analyzing single user behavior information, and if multiple users
Behavioural information combines analysis, then is very easy to find exception, and therefore, Behavior-based control trajectory creation user portrait can be lifted
The accuracy of user's portrait.
Specifically, after behavioural information is got, behavioural information is connected with straight line according to time order and function order, shape
The action trail of application program is operated into user.
Step S202, the action trail of application program is operated according to user, generate behavior sequence.
Action trail connects behavioural information with straight line according to time order and function order, therefore, can according to
Family operates the action trail of application program, generates behavior sequence.
Step S203, Sequence Learning is carried out to behavior sequence using trained time recurrent neural network, used
Family behavioural characteristic vector.
Used time recurrent neural network (LSTM) is trained based on substantial amounts of sample in the embodiment of the present invention
Obtain, the behavior sequence generated is inputted to time recurrent neural network, the time recurrent neural network is to accessed
Behavior sequence learnt, generate user behavior characteristic vector.
Step S204, using trained incorporation model in the list information of the mounted application program of user terminal
Each application program is learnt, and obtains application features vector corresponding to each application program.
Used incorporation model (Embedding) is to be trained to obtain based on substantial amounts of sample in the embodiment of the present invention
, Embedding can be understood as some complicated features beyond expression of words and be mapped with the form for being more easy to calculate come the one kind expressed,
Such as vector, specifically, the list information of the mounted application program of user terminal is inputted to incorporation model, by the insertion mould
Type answers program to learn to each, obtains application features vector corresponding to each application program.
Step S205, calculation process is carried out using the application features vector of the multiple application programs of preset algorithm, is obtained
The application features vector of the mounted application program of user terminal.
Specifically, the application features vector of multiple application programs can be averaging, calculating is multiple to apply journey
The average value of the application features vector of sequence, obtain the application features vector of the mounted application program of user terminal.
The present invention is not limited step S201- steps S203, step S204- steps S205 execution sequence, step
S201- steps S203 can be performed before or after step S204- steps S205, can also perform step S201, step simultaneously
Rapid S204, step S207.
Step S206, drawn a portrait based on user behavior characteristic vector and application features vector structuring user's.
, can after user behavior characteristic vector and user behavior characteristic vector is obtained according to step S203 and step S205
To be drawn a portrait according to resulting user behavior characteristic vector and user behavior characteristic vector come structuring user's, user portrait can
The fully feature of reflection user, overcomes the metadata for relying only on user in the prior art, for example, age, sex, income etc.
User's portrait quality is low caused by structuring user's portrait, can not provide the user the defects of more becoming more meticulous service.
The user constructed using user provided by the invention portrait construction method is drawn a portrait, and can be applied to various flat
Platform, platform can be made to be better understood by user, so as to provide the user the service more to become more meticulous, to improve the Experience Degree of user,
For example, can apply to gaming platform, gaming platform can be made to know the game interested to user, so as to targetedly to
User pushes game advertisement；Apply also for finance company so that the finance company draws a portrait according to user understands whether user deposits
In abnormal behaviour, user's reference record is understood, corresponding service is provided user according to reference record, for example, do not made loans to it,
Or lending amount of money etc. is determined according to record.Here it is merely illustrative of, without any restriction effect.
In alternative embodiment of the present invention, after structuring user's portrait, method also includes：Based on user's portrait sample
Training user's hobby model, the user's portrait input for being analysed to user are analyzed to user interest modeling hobbies, obtained
To the hobby of the user.
Specifically, the user constructed by the use of user provided by the present invention portrait building method is drawn a portrait as sample
Row training, obtain input and drawn a portrait for user, export as the user interest modeling hobbies of user interest hobby, when needing to analyze certain use
The hobby at family, the user that the user is constructed using user provided by the invention portrait building method is drawn a portrait, then by institute's structure
The user made draws a portrait input to the user interest modeling hobbies of training, analyzes to obtain the user using the user interest modeling hobbies
Hobby.After the hobby of user is analyzed, it can be pushed accordingly to user according to the hobby of user
Information, for example, video messaging, game message etc., will not enumerate explanation here.
In alternative embodiment of the present invention, after structuring user's portrait, method also includes：Drawn a portrait and analyzed based on user
Whether user has abnormal behaviour.
For example, with the development of network technology, the application program much on debt-credit is proposed at present, for example, xx
Receipt, user can application program carry out debit operation, then to user make loans before, the affiliated company of application program needs to carry out
Risk assessment, credit of the user etc. is assessed, in the prior art, the reference record for being all based on third party's offer is judged,
However, the air control many times based on third party's data faces many problems：1st, the data cover rate of third party's data is limited, very
Multiple target user can not cover；2nd, reference cost is higher, and looks into third party's data and generally require user's mandate, Consumer's Experience
Difference.It is to be drawn a portrait based on big data come structuring user's using user provided in an embodiment of the present invention portrait building method, makes full use of
User operates the behavioural information of application program and the list information of the mounted application program of user terminal, avoids user from forging letter
Breath, the user's portrait constructed more truly reflect user profile, and whether analysis user is drawn a portrait with abnormal based on user
Behavior, finance company can be instructed.
The method provided according to embodiments of the present invention, the behavioural information and user terminal of acquisition user's operation application program are
The list information of the application program of installation, the action trail of user's operation application program is obtained according to behavioural information, according to user
The action trail of application program is operated, behavior sequence is generated, using trained time recurrent neural network to behavior sequence
Sequence Learning is carried out, obtains user behavior characteristic vector, is answered using trained incorporation model is mounted to user terminal
Learnt with each application program in the list information of program, obtain application features corresponding to each application program to
Amount, calculation process is carried out using the application features vector of the multiple application programs of preset algorithm, user terminal is obtained and has installed
Application program application features vector, drawn based on user behavior characteristic vector and application features vector structuring user's
Picture.Technical scheme provided by the invention, user's portrait based on big data construction can fully reflect the feature of user, so as to
It is enough that the service of more becoming more meticulous easily is provided the user according to user's portrait, and drawn a portrait according to user and determine user with the presence or absence of different
Chang Hangwei etc., the metadata for relying only on user in the prior art is overcome, for example, the structuring user's such as age, sex, income are drawn
The low-quality defect of user's portrait as caused by.
Fig. 3 shows that the structure journey of user's portrait constructing apparatus according to an embodiment of the invention based on big data is shown
It is intended to.As shown in figure 3, the device includes：Acquisition module 300, the first study module 310, the second study module 320, Yong Huhua
As constructing module 330.
Acquisition module 300, behavioural information and the mounted application of user terminal of application program are operated suitable for obtaining user
The list information of program.
First study module 310, suitable for learning using the first deep learning algorithm to behavioural information, obtain user's row
It is characterized vector.
Second study module 320, suitable for utilizing the second deep learning algorithm to the mounted application program of user terminal
List information is learnt, and obtains the application features vector of the mounted application program of user terminal.
User's portrait constructing module 330, suitable for being used based on user behavior characteristic vector and application features vector construction
Draw a portrait at family.
The device provided according to embodiments of the present invention, the behavioural information and user terminal of acquisition user's operation application program are
The list information of the application program of installation, behavioural information is learnt using the first deep learning algorithm, obtains user behavior
Characteristic vector, the list information of the mounted application program of user terminal is learnt using the second deep learning algorithm, obtained
It is special based on user behavior characteristic vector and application program to the application features vector of the mounted application program of user terminal
Levy vectorial structuring user's portrait.Technical scheme provided by the invention, user's portrait based on big data construction can fully reflect
The feature of user, so as to easily provide the user the service of more becoming more meticulous according to user's portrait, overcome in the prior art
The metadata of user is relied only on, for example, user's portrait quality is low caused by the structuring user's such as age, sex, income portrait
The defects of.
Fig. 4 shows the structure journey of user's portrait constructing apparatus in accordance with another embodiment of the present invention based on big data
Schematic diagram.As shown in figure 4, the device includes：Acquisition module 400, the first study module 410, the second study module 420, user
Portrait constructing module 430.
Acquisition module 400, behavioural information and the mounted application of user terminal of application program are operated suitable for obtaining user
The list information of program.
Wherein, behavioural information includes：Operation of the user to application program and the page residence time in application program.
First study module 410 further comprises：Action trail generation unit 411, suitable for being used according to behavioural information
Family operates the action trail of application program.
Behavior sequence generation unit 412, suitable for operating the action trail of application program according to user, generate behavior sequence.
First unit 413, suitable for carrying out sequence to behavior sequence using trained time recurrent neural network
Study, obtains user behavior characteristic vector.
Second study module 420 further comprises：Second unit 421, suitable for utilizing trained incorporation model
Each application program in the list information of the mounted application program of user terminal is learnt, obtains each application program pair
The application features vector answered.
Operation processing unit 422, suitable for being transported using the application features vector of the multiple application programs of preset algorithm
Calculation is handled, and obtains the application features vector of the mounted application program of user terminal.
User's portrait constructing module 430, suitable for being used based on user behavior characteristic vector and application features vector construction
Draw a portrait at family.
In alternative embodiment of the present invention, the device also includes：Training module 440, suitable for based on user's portrait sample
Training user's hobby model；
Hobby analysis module 450, the user suitable for being analysed to user, which draws a portrait, to be inputted to user interest modeling hobbies
Analyzed, obtain the hobby of the user.
In alternative embodiment of the present invention, the device also includes：Abnormal behaviour analysis module 460, suitable for based on user
Whether portrait analysis user has abnormal behaviour.
The device provided according to embodiments of the present invention, the behavioural information and user terminal of acquisition user's operation application program are
The list information of the application program of installation, the action trail of user's operation application program is obtained according to behavioural information, according to user
The action trail of application program is operated, behavior sequence is generated, using trained time recurrent neural network to behavior sequence
Sequence Learning is carried out, obtains user behavior characteristic vector, is answered using trained incorporation model is mounted to user terminal
Learnt with each application program in the list information of program, obtain application features corresponding to each application program to
Amount, calculation process is carried out using the application features vector of the multiple application programs of preset algorithm, user terminal is obtained and has installed
Application program application features vector, drawn based on user behavior characteristic vector and application features vector structuring user's
Picture.Technical scheme provided by the invention, user's portrait based on big data construction can fully reflect the feature of user, so as to
It is enough that the service of more becoming more meticulous easily is provided the user according to user's portrait, and drawn a portrait according to user and determine user with the presence or absence of different
Chang Hangwei etc., the metadata for relying only on user in the prior art is overcome, for example, the structuring user's such as age, sex, income are drawn
The low-quality defect of user's portrait as caused by.
The embodiment of the present application additionally provides a kind of nonvolatile computer storage media, the computer-readable storage medium storage
Have an at least executable instruction, the computer executable instructions can perform in above-mentioned any means embodiment based on big data
User's portrait building method.
Fig. 5 shows a kind of structural representation of computing device according to an embodiment of the invention, of the invention specific real
Specific implementation of the example not to computing device is applied to limit.
As shown in figure 5, the computing device can include：Processor (processor) 502, communication interface
(Communications Interface) 504, memory (memory) 506 and communication bus 508.
Wherein：
Processor 502, communication interface 504 and memory 506 complete mutual communication by communication bus 508.
Communication interface 504, for being communicated with the network element of miscellaneous equipment such as client or other servers etc..
Processor 502, for configuration processor 510, it can specifically perform above-mentioned user's portrait construction side based on big data
Correlation step in method embodiment.
Specifically, program 510 can include program code, and the program code includes computer-managed instruction.
Processor 502 is probably central processor CPU, or specific integrated circuit ASIC (Application
Specific Integrated Circuit), or it is arranged to implement the integrated electricity of one or more of the embodiment of the present invention
Road.The one or more processors that computing device includes, can be same type of processor, such as one or more CPU；Also may be used
To be different types of processor, such as one or more CPU and one or more ASIC.
Memory 506, for depositing program 510.Memory 506 may include high-speed RAM memory, it is also possible to also include
Nonvolatile memory (non-volatile memory), for example, at least a magnetic disk storage.
Program 510 specifically can be used for so that processor 502 perform in above-mentioned any means embodiment based on big data
User draw a portrait building method.The specific implementation of each step may refer to above-mentioned user's portrait based on big data in program 510
Corresponding description in corresponding steps and unit in constructed embodiment, will not be described here.Those skilled in the art can be clear
Recognize to Chu, for convenience and simplicity of description, the equipment of foregoing description and the specific work process of module, may be referred to foregoing
Corresponding process description in embodiment of the method, will not be repeated here.
Algorithm and display be not inherently related to any certain computer, virtual system or miscellaneous equipment provided herein.
Various general-purpose systems can also be used together with teaching based on this.As described above, required by constructing this kind of system
Structure be obvious.In addition, the present invention is not also directed to any certain programmed language.It should be understood that it can utilize various
Programming language realizes the content of invention described herein, and the description done above to language-specific is to disclose this hair
Bright preferred forms.
In the specification that this place provides, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect,
Above in the description to the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor
The application claims of shield features more more than the feature being expressly recited in each claim.It is more precisely, such as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself
Separate embodiments all as the present invention.
Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit requires, summary and accompanying drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation
Replace.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included some features rather than further feature, but the combination of the feature of different embodiments means in of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
One of meaning mode can use in any combination.
The all parts embodiment of the present invention can be realized with hardware, or to be run on one or more processor
Software module realize, or realized with combinations thereof.It will be understood by those of skill in the art that it can use in practice
Microprocessor or digital signal processor (DSP) are drawn a portrait structure to realize the user according to embodiments of the present invention based on big data
The some or all functions of some or all parts in manufacturing apparatus.The present invention is also implemented as being used to perform institute here
The some or all equipment or program of device of the method for description are (for example, computer program and computer program production
Product).Such program for realizing the present invention can store on a computer-readable medium, or can have one or more
The form of signal.Such signal can be downloaded from internet website and obtained, and either be provided or on carrier signal to appoint
What other forms provides.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of some different elements and being come by means of properly programmed computer real
It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
Claims (10)
- The building method 1. a kind of user based on big data draws a portrait, including：Obtain user and operate the behavioural information of application program and the list information of the mounted application program of user terminal；The behavioural information is learnt using the first deep learning algorithm, obtains user behavior characteristic vector；The list information of the mounted application program of the user terminal is learnt using the second deep learning algorithm, obtained The application features vector of the mounted application program of user terminal；Drawn a portrait based on the user behavior characteristic vector and the application features vector structuring user's.
- 2. the method according to claim 11, wherein, it is described that the behavioural information is carried out using the first deep learning algorithm Study, obtains user behavior characteristic vector and further comprises：The action trail of user's operation application program is obtained according to the behavioural information；The action trail of application program is operated according to user, generates behavior sequence；Sequence Learning is carried out to the behavior sequence using trained time recurrent neural network, obtains user behavior feature Vector.
- 3. method according to claim 1 or 2, wherein, it is described to utilize the second deep learning algorithm to the user terminal The list information of mounted application program is learnt, and obtains the application features of the mounted application program of user terminal Vector further comprises：Using trained incorporation model to each being applied in the list information of the mounted application program of the user terminal Program is learnt, and obtains application features vector corresponding to each application program；Calculation process is carried out using the application features vector of the multiple application programs of preset algorithm, user terminal is obtained and has installed Application program application features vector.
- 4. according to the method described in claim any one of 1-3, wherein, after structuring user's portrait, methods described also includes： Based on user portrait sample training user interest modeling hobbies；The user's portrait input for being analysed to user is analyzed to user interest modeling hobbies, obtains the interest love of the user It is good.
- 5. according to the method described in claim any one of 1-3, wherein, after structuring user's portrait, methods described also includes： Drawn a portrait based on the user and analyze whether user has abnormal behaviour.
- 6. according to the method described in claim any one of 1-5, wherein, the behavioural information includes：User is to application program Operation and the page residence time in application program.
- The constructing apparatus 7. a kind of user based on big data draws a portrait, including：Acquisition module, the behavioural information of application program and the row of the mounted application program of user terminal are operated suitable for obtaining user Table information；First study module, suitable for learning using the first deep learning algorithm to the behavioural information, obtain user behavior Characteristic vector；Second study module, suitable for utilizing list of the second deep learning algorithm to the mounted application program of the user terminal Information is learnt, and obtains the application features vector of the mounted application program of user terminal；User's portrait constructing module, suitable for being used based on the user behavior characteristic vector and application features vector construction Draw a portrait at family.
- 8. device according to claim 7, wherein, first study module further comprises：Action trail generation unit, suitable for obtaining the action trail of user's operation application program according to the behavioural information；Behavior sequence generation unit, suitable for operating the action trail of application program according to user, generate behavior sequence；First unit, suitable for carrying out sequence to the behavior sequence using trained time recurrent neural network Practise, obtain user behavior characteristic vector.
- 9. a kind of computing device, including：Processor, memory, communication interface and communication bus, the processor, the storage Device and the communication interface complete mutual communication by the communication bus；The memory is used to deposit an at least executable instruction, and the executable instruction makes the computing device such as right will Ask the user based on big data any one of 1-6 to draw a portrait corresponding to building method to operate.
- 10. a kind of computer-readable storage medium, an at least executable instruction, the executable instruction are stored with the storage medium Make user based on big data of the computing device as any one of claim 1-6 draw a portrait to grasp corresponding to building method Make.
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| CN109102341A (en) * | 2018-08-27 | 2018-12-28 | 寿带鸟信息科技（苏州）有限公司 | A kind of old man's portrait method for the service of supporting parents |
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Application publication date: 20180223 |