CN107798118A - 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
- Publication number
- CN107798118A CN107798118A CN201711092698.4A CN201711092698A CN107798118A CN 107798118 A CN107798118 A CN 107798118A CN 201711092698 A CN201711092698 A CN 201711092698A CN 107798118 A CN107798118 A CN 107798118A
- Authority
- CN
- China
- Prior art keywords
- user
- portrait
- information
- application program
- vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Remote Sensing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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's geographical location information and the list information of the mounted application program of user terminal；Position point sequence is generated according to user's geographical location information；Position point sequence is learnt using the first deep learning algorithm, obtains user's geographic location feature vector corresponding to user；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's geographic location feature vector sum application features vector structuring user's, 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.
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's geographical location information and the list information of the mounted application program of user terminal；
Position point sequence is generated according to user's geographical location information；
Position point sequence is learnt using the first deep learning algorithm, it is special to obtain user geographical position corresponding to user
Sign 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's geographic location feature vector sum application features vector structuring user's.
Alternatively, position point sequence is generated according to user's geographical location information to further comprise：
User's geographical location information is combined processing, generation positional information track；
Location point peripheral information is obtained based on the location point on positional information track, generates position point sequence；
Position point sequence is learnt using the first deep learning algorithm, it is special to obtain user geographical position corresponding to user
Sign vector further comprises：
Sequence Learning is carried out to position point sequence using trained time recurrent neural network, obtained corresponding to user
User's geographic location 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, method also includes：Location consistency characteristic vector is generated according to user's geographical location information；
Further comprised based on user's geographic location feature vector sum application features vector structuring user's portrait：
Checking treatment is carried out to user's geographic location feature vector according to location consistency characteristic vector；
Drawn according to user's geographic location feature vector sum application features vector structuring user's after checking treatment
Picture.
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, user's geographical location information includes the one or more in following information：IP information, WIFI information, base
Stand information, GPS information.
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, suitable for obtaining the list letter of user's geographical location information and the mounted application program of user terminal
Breath；
First generation module, suitable for generating position point sequence according to user's geographical location information；
First study module, suitable for learning using the first deep learning algorithm to position point sequence, obtain user couple
The user's geographic location feature vector answered；
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 being used based on user's geographic location feature vector sum application features vector construction
Draw a portrait at family.
Alternatively, the first generation module further comprises：
Combined treatment unit, suitable for user's geographical location information is combined into processing, generation positional information track；
First generation unit, suitable for obtaining location point peripheral information based on the location point on positional information track, generate position
Put point sequence；
First study module is further adapted for：Position point sequence is carried out using trained time recurrent neural network
Sequence Learning, obtain user's geographic location feature vector corresponding to user.
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：Second generation module, suitable for generating location consistency according to user's geographical location information
Characteristic vector；
User's portrait constructing module further comprises：
Checking treatment unit, suitable for being verified according to location consistency characteristic vector to user's geographic location feature vector
Processing；
User's portrait structural unit, suitable for according to user's geographic location feature vector sum application journey after checking treatment
Sequence characteristics vector structuring user's are drawn a portrait.
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, user's geographical location information includes the one or more in following information：IP information, WIFI information, base
Stand information, GPS information.
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, user's geographical location information and the mounted application program of user terminal are obtained
List information；Position point sequence is generated according to user's geographical location information；Using the first deep learning algorithm to position point sequence
Learnt, obtain user's geographic location feature vector corresponding to user；Using the second deep learning algorithm to user terminal
The list information of the application program of installation is learnt, obtain the application features of the mounted application program of user terminal to
Amount；Drawn a portrait based on user's geographic location feature vector sum application features vector structuring user's.Technical side provided by the invention
Case, user's portrait based on big data construction can fully reflect the feature of user, so as to easily be drawn a portrait according to user
The service of more becoming more meticulous is provided the user, overcomes the metadata for relying only on user in the prior art, for example, age, sex, receipts
The user caused by structuring user's portrait such as enter to draw a portrait low-quality defect.
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's geographical location information and the list information of the mounted application program of user terminal.
Wherein, user's geographical location information and the list information of the mounted application program of user terminal can be that user is whole
What end reported, alternatively, user's geographical location information in the present embodiment can include the one or more in following information：IP
Information, WIFI information, base station information, GPS information, the current geographical position of user can be determined based on above- mentioned information, for example, base
The city where IP address can determine user, can determine user the country one belongs to, based on GPS information based on base station information
Street information etc. can be determined, for example, it is 118.119.85.36 to get certain IP address, according to accessed IP
Address lookup IP inquiry tables, it may be determined that specifically geographical position is Sichuan Province Ziyang City, is merely illustrative of does not have here
Any restriction effect.The geographical position of user can be obtained according to above-mentioned four kinds of information, is no longer illustrated one by one here.
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, position point sequence is generated according to user's geographical location information.
After user's geographical location information is got, position can be generated according to acquired user's geographical location information
Point sequence is put, in the position point sequence, the order of location point is particularly important, is the row that carries out according to time order and function order
Sequence, for example, the position point sequence generated by user's geographical location information in obtaining one day is：Beijing → Shanghai → tri-
Asia → Beijing, it is merely illustrative of here certainly without any restriction effect.
Step S102, position point sequence is learnt using the first deep learning algorithm, obtains user corresponding to user
Geographic location feature vector.
After position point sequence is obtained according to step S101, the first deep learning algorithm can be utilized to position point sequence
Learnt, position point sequence is learnt here primarily to obtain user's geographic location feature vector, with pass through to
Amount form represents user's geographical location information, is easy to subsequent construction user to draw a portrait.
Step S103, 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 S104, drawn a portrait based on user's geographic location feature vector sum application features vector structuring user's.
User's geographic location feature vector sum application features vector is being obtained according to step S102 and step S103
Afterwards, can be drawn a portrait according to resulting user's geographic location feature vector sum application features vector come structuring user's, the use
Family, which is drawn a portrait, can fully reflect the feature of user, overcome the metadata for relying only on user in the prior art, for example, the age,
User's portrait quality is low caused by the structuring user's such as sex, income portrait, can not provide the user lacking for the service that more becomes more meticulous
Fall into.
The method provided according to embodiments of the present invention, obtain user's geographical location information and the mounted application of user terminal
The list information of program；Position point sequence is generated according to user's geographical location information；Using the first deep learning algorithm to position
Point sequence is learnt, and obtains user's geographic location feature vector corresponding to user；Using the second deep learning algorithm to user
The list information of the mounted application program of terminal is learnt, and obtains the application program of the mounted application program of user terminal
Characteristic vector；Drawn a portrait based on user's geographic location feature vector sum application features vector structuring user's.It is provided by the invention
Technical scheme, user's portrait based on big data construction can fully reflect the feature of user, so as to easily according to
Family portrait provides the user the service of more becoming more meticulous, and overcomes the metadata for relying only on user in the prior art, for example, the age,
The low-quality defect of user's portrait caused by the structuring user's such as sex, income portrait.
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's geographical location information and the list information of the mounted application program of user terminal.
Step S201, user's geographical location information is combined processing, generation positional information track.
User's geographical location information can intuitively reflect customer location situation of change, be a kind of hand for analyzing user behavior
Section, any exception may can not be found by analyzing single user's geographical location information, and if multiple user's geographical location information
Analysis is combined, then is very easy to find exception, for example, xx xx days month xx, user A user's geographical location information morning
When be Beijing, noon is Shanghai, and the lower period of the day from 11 a.m. to 1 p.m will be Sanya, is changed into Beijing again during evening, for normal situation, a user is little
It may be shuttled within the time in four cities, for this kind of situation, if being only according to unique user geographical location information
It can not find to go wrong, and forming position information track, then problem is very easy to find, therefore, based on positional information track
Structuring user's portrait can lift the accuracy of user's portrait.
Specifically, after geographical location information is got, the geographical position according to residing for geographical location information determines user,
Each geographical position is considered as a location point, connects location point with straight line according to time order and function order, forming position
Information track.
Step S202, location point peripheral information is obtained based on the location point on positional information track, generates position point sequence.
Positional information track is made up of location point, and location point week is inquired about according to the location point on positional information track
The information enclosed, and location point peripheral information is obtained, such as Airport information, company information, mall information etc., around location point
Information and geographical location information generation position point sequence.
Step S203, Sequence Learning is carried out to position point sequence using trained time recurrent neural network, obtained
User's geographic location feature vector corresponding to user.
Used time recurrent neural network (LSTM) is trained based on substantial amounts of sample in the embodiment of the present invention
Obtain, by the location point sequence inputting generated to time recurrent neural network, the time recurrent neural network is to acquired
To position point sequence learnt, generation user geographic location feature 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.
Step S206, location consistency characteristic vector is generated according to user's geographical location information.
User's geographical location information in the present embodiment can include the one or more in following information：IP information,
WIFI information, base station information, GPS information, but some information users can forge, for example, IP information, user can be pseudo-
IP address is made, user geographical position will be inaccurate so according to determined by IP information, and identified user geographical position is not
The accurate user's portrait finally constructed that may result in is inaccurate, so as to influence to provide a user the flat of service using user's portrait
The service that platform is provided, therefore, after user's geographical location information is got, it is also necessary to generated according to user's geographical location information
Location consistency characteristic vector, uniformity characteristic vector are used for the uniformity for representing each geographical position, can be used for user
Reason position feature vector is verified.
The present invention is not limited step S201- steps S203, step S204- steps S205, step S206 execution sequence
Fixed, step S201- steps S203 can be performed before or after step S204- steps S205, step S206, can also be simultaneously
Perform step S201, step S204, step S206.
Step S207, checking treatment is carried out to user's geographic location feature vector according to location consistency characteristic vector, obtained
User's geographic location feature vector after to checking treatment.
Specifically, user's geographic location feature vector can be verified according to location consistency characteristic vector, verified
User's geographic location feature vector afterwards reflects real user geographical position.
Step S208, used according to user's geographic location feature vector sum application features vector construction after checking treatment
Draw a portrait at family.
User's geographic location feature vector sum application by checking treatment is being obtained according to step S207 and step S205
After performance of program vector, can according to user's geographic location feature vector sum application features after checking treatment to
Amount carrys out structuring user's portrait, and the user, which draws a portrait, can fully reflect the feature of user, overcome and rely only on user in the prior art
A metadata, for example, the structuring user's such as age, sex, income portrait caused by user draw a portrait quality it is low, can not be user
The defects of more becoming more meticulous service is provided.
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
The list information of user's geographical location information and the mounted application program of user terminal, avoids user's spurious information, is constructed
User portrait more truly reflect user profile, based on user draw a portrait analysis user whether there is abnormal behaviour, can
Finance company is instructed.
The method provided according to embodiments of the present invention, obtain user's geographical location information and the mounted application of user terminal
The list information of program, user's geographical location information is combined processing, generation positional information track, based on positional information rail
Location point on mark obtains location point peripheral information, generates position point sequence, utilizes trained time recurrent neural network
Sequence Learning is carried out to position point sequence, obtains corresponding to user user's geographic location feature vector, using trained embedding
Enter model to learn each application program in the list information of the mounted application program of user terminal, obtain each application
Application features vector corresponding to program, computing is carried out using the application features vector of the multiple application programs of preset algorithm
Processing, the application features vector of the mounted application program of user terminal is obtained, is generated according to user's geographical location information
Location consistency characteristic vector, checking treatment is carried out to user's geographic location feature vector according to location consistency characteristic vector,
Drawn a portrait according to user's geographic location feature vector sum application features vector structuring user's after checking treatment.The present invention
The technical scheme of offer, user's portrait based on big data construction can fully reflect the feature of user, so as to easily
The service of more becoming more meticulous is provided the user according to user's portrait, and is drawn a portrait according to user and determines that user whether there is abnormal behaviour
Deng, overcome the metadata for relying only on user in the prior art, for example, the structuring user's such as age, sex, income portrait and lead
The low-quality defect of user's portrait of cause.
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 generation module 310, the first study module 320, second are learned
Practise module 330, user's portrait constructing module 340.
Acquisition module 300, suitable for obtaining the list of user's geographical location information and the mounted application program of user terminal
Information.
First generation module 310, suitable for generating position point sequence according to user's geographical location information.
First study module 320, suitable for learning using the first deep learning algorithm to position point sequence, obtain user
Corresponding user's geographic location feature vector.
Second study module 330, 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 340, suitable for based on user's geographic location feature vector sum application features vector structure
Make user's portrait.
The device provided according to embodiments of the present invention, obtain user's geographical location information and the mounted application of user terminal
The list information of program；Position point sequence is generated according to user's geographical location information；Using the first deep learning algorithm to position
Point sequence is learnt, and obtains user's geographic location feature vector corresponding to user；Using the second deep learning algorithm to user
The list information of the mounted application program of terminal is learnt, and obtains the application program of the mounted application program of user terminal
Characteristic vector；Drawn a portrait based on user's geographic location feature vector sum application features vector structuring user's.It is provided by the invention
Technical scheme, user's portrait based on big data construction can fully reflect the feature of user, so as to easily according to
Family portrait provides the user the service of more becoming more meticulous, and overcomes the metadata for relying only on user in the prior art, for example, the age,
The low-quality defect of user's portrait caused by the structuring user's such as sex, income portrait.
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 generation module 410, the first study module 420, second
Study module 430, the second generation module 440, user's portrait constructing module 450.
Acquisition module 400, suitable for obtaining the list of user's geographical location information and the mounted application program of user terminal
Information.
Wherein, user's geographical location information includes the one or more in following information：IP information, WIFI information, base station
Information, GPS information.
First generation module 410 includes：Combined treatment unit 411, suitable for user's geographical location information is combined into place
Reason, generation positional information track.
First generation unit 412, suitable for obtaining location point peripheral information, generation based on the location point on positional information track
Position point sequence.
First study module 420, suitable for carrying out sequence to position point sequence using trained time recurrent neural network
Row study, obtain user's geographic location feature vector corresponding to user.
Second study module 430 includes：Second unit 431, suitable for utilizing trained incorporation model to user
Each application program is learnt in the list information of the mounted application program of terminal, and obtaining should corresponding to each application program
With performance of program vector.
Operation processing unit 432, 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.
Second generation module 440, suitable for generating location consistency characteristic vector according to user's geographical location information.
User's portrait constructing module 450 includes：Checking treatment unit 451, suitable for according to location consistency characteristic vector pair
User's geographic location feature vector carries out checking treatment.
User's portrait structural unit 452, suitable for being answered according to user's geographic location feature vector sum after checking treatment
Drawn a portrait with performance of program vector structuring user's.
In alternative embodiment of the present invention, the device also includes：Training module 460, suitable for based on user's portrait sample
Training user's hobby model；
Hobby analysis module 470, 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 480, suitable for based on user
Whether portrait analysis user has abnormal behaviour.
The device provided according to embodiments of the present invention, obtain user's geographical location information and the mounted application of user terminal
The list information of program, user's geographical location information is combined processing, generation positional information track, based on positional information rail
Location point on mark obtains location point peripheral information, generates position point sequence, utilizes trained time recurrent neural network
Sequence Learning is carried out to position point sequence, obtains corresponding to user user's geographic location feature vector, using trained embedding
Enter model to learn each application program in the list information of the mounted application program of user terminal, obtain each application
Application features vector corresponding to program, computing is carried out using the application features vector of the multiple application programs of preset algorithm
Processing, the application features vector of the mounted application program of user terminal is obtained, is generated according to user's geographical location information
Location consistency characteristic vector, checking treatment is carried out to user's geographic location feature vector according to location consistency characteristic vector,
Drawn a portrait according to user's geographic location feature vector sum application features vector structuring user's after checking treatment.The present invention
The technical scheme of offer, user's portrait based on big data construction can fully reflect the feature of user, so as to easily
The service of more becoming more meticulous is provided the user according to user's portrait, and is drawn a portrait according to user and determines that user whether there is abnormal behaviour
Deng, overcome the metadata for relying only on user in the prior art, for example, the structuring user's such as age, sex, income portrait and lead
The low-quality defect of user's portrait of cause.
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's geographical location information and the list information of the mounted application program of user terminal；Position point sequence is generated according to user's geographical location information；The position point sequence is learnt using the first deep learning algorithm, it is special to obtain user geographical position corresponding to user Sign 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 application features vector structuring user's described in user's geographic location feature vector sum.
- 2. the method according to claim 11, wherein, it is described to enter one according to user's geographical location information generation position point sequence Step includes：User's geographical location information is combined processing, generation positional information track；Location point peripheral information is obtained based on the location point on positional information track, generates position point sequence；It is described that the position point sequence is learnt using the first deep learning algorithm, obtain user's geography position corresponding to user Characteristic vector is put to further comprise：Sequence Learning is carried out to the position point sequence using trained time recurrent neural network, obtained corresponding to user User's geographic location 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, methods described also includes：According to user's geography position Confidence breath generation location consistency characteristic vector；It is described to be further comprised based on user's geographic location feature vector sum application features vector structuring user's portrait：Checking treatment is carried out to user's geographic location feature vector according to the location consistency characteristic vector；Drawn a portrait according to user's geographic location feature vector sum application features vector structuring user's after checking treatment.
- 5. according to the method described in claim any one of 1-4, 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.
- 6. according to the method described in claim any one of 1-4, wherein, after structuring user's portrait, methods described also includes： Drawn a portrait based on the user and analyze whether user has abnormal behaviour.
- 7. according to the method described in claim any one of 1-6, wherein, user's geographical location information is included in following information One or more：IP information, WIFI information, base station information, GPS information.
- The constructing apparatus 8. a kind of user based on big data draws a portrait, including：Acquisition module, suitable for obtaining the list information of user's geographical location information and the mounted application program of user terminal；First generation module, suitable for generating position point sequence according to user's geographical location information；First study module, suitable for learning using the first deep learning algorithm to the position point sequence, obtain user couple The user's geographic location feature vector answered；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 based on application features vector structure described in user's geographic location feature vector sum Make user's portrait.
- 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 1-7 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 1-7 draw a portrait to grasp corresponding to building method Make.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711092698.4A CN107798118A (en) | 2017-11-08 | 2017-11-08 | User's portrait building method, device and computing device based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711092698.4A CN107798118A (en) | 2017-11-08 | 2017-11-08 | User's portrait building method, device and computing device based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107798118A true CN107798118A (en) | 2018-03-13 |
Family
ID=61549101
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711092698.4A Pending CN107798118A (en) | 2017-11-08 | 2017-11-08 | User's portrait building method, device and computing device based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107798118A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109460511A (en) * | 2018-10-25 | 2019-03-12 | 百度在线网络技术（北京）有限公司 | A kind of method, apparatus, electronic equipment and storage medium obtaining user's portrait |
| CN109933727A (en) * | 2019-03-22 | 2019-06-25 | 北京博明信德科技有限公司 | User's portrait generation method and system, user's portrait application method and system |
| CN110197315A (en) * | 2018-04-08 | 2019-09-03 | 腾讯科技（深圳）有限公司 | Methods of risk assessment, device and its storage medium |
| CN111210336A (en) * | 2019-12-16 | 2020-05-29 | 北京淇瑀信息科技有限公司 | User risk model generation method and device and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503015A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of method for building user's portrait |
| CN106651603A (en) * | 2016-12-29 | 2017-05-10 | 平安科技（深圳）有限公司 | Risk evaluation method and apparatus based on position service |
| CN106933991A (en) * | 2017-02-24 | 2017-07-07 | 陈晶 | A kind of depth analysis towards intelligent terminal and user's portrait system and method |
| US20170286845A1 (en) * | 2016-04-01 | 2017-10-05 | International Business Machines Corporation | Automatic extraction of user mobility behaviors and interaction preferences using spatio-temporal data |
-
2017
- 2017-11-08 CN CN201711092698.4A patent/CN107798118A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503015A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of method for building user's portrait |
| US20170286845A1 (en) * | 2016-04-01 | 2017-10-05 | International Business Machines Corporation | Automatic extraction of user mobility behaviors and interaction preferences using spatio-temporal data |
| CN106651603A (en) * | 2016-12-29 | 2017-05-10 | 平安科技（深圳）有限公司 | Risk evaluation method and apparatus based on position service |
| CN106933991A (en) * | 2017-02-24 | 2017-07-07 | 陈晶 | A kind of depth analysis towards intelligent terminal and user's portrait system and method |
Non-Patent Citations (1)
| Title |
|---|
| 黄文彬等: "数据驱动的移动用户行为研究框架与方法分析", 《情报科学》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110197315A (en) * | 2018-04-08 | 2019-09-03 | 腾讯科技（深圳）有限公司 | Methods of risk assessment, device and its storage medium |
| CN110197315B (en) * | 2018-04-08 | 2024-03-22 | 腾讯科技（深圳）有限公司 | Risk assessment method, apparatus and storage medium thereof |
| CN109460511A (en) * | 2018-10-25 | 2019-03-12 | 百度在线网络技术（北京）有限公司 | A kind of method, apparatus, electronic equipment and storage medium obtaining user's portrait |
| CN109933727A (en) * | 2019-03-22 | 2019-06-25 | 北京博明信德科技有限公司 | User's portrait generation method and system, user's portrait application method and system |
| CN111210336A (en) * | 2019-12-16 | 2020-05-29 | 北京淇瑀信息科技有限公司 | User risk model generation method and device and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107807997A (en) | User's portrait building method, device and computing device based on big data | |
| CN107729560A (en) | User's portrait building method, device and computing device based on big data | |
| CN107862053A (en) | User's portrait building method, device and computing device based on customer relationship | |
| Mahajan et al. | Diffusion of new products: Empirical generalizations and managerial uses | |
| Tang | Is the tourism‐led growth hypothesis valid for malaysia? a view from disaggregated tourism markets | |
| CN107798118A (en) | User's portrait building method, device and computing device based on big data | |
| CN107330731B (en) | Method and device for identifying click abnormity of advertisement space | |
| Sudirjo et al. | The Application of Extended Expectation-Confirmation Model to Identify Influencing Factors Digital Loyalty for Mobile-Based Travel Platform | |
| WO2019055065A1 (en) | Evaluating models that rely on aggregate historical data | |
| CN106919625A (en) | A kind of internet customer attribute recognition methods and device | |
| CN104718547A (en) | Customer data analysis system | |
| CN106844781A (en) | The method and device of data processing | |
| CN109902213B (en) | Real-time bus service line recommendation method and device and electronic equipment | |
| CN110084634B (en) | Advertisement delivery optimization method, advertisement delivery optimization device, computer equipment and storage medium | |
| CN109325845A (en) | A kind of financial product intelligent recommendation method and system | |
| CN114663198A (en) | Product recommendation method, device and equipment based on user portrait and storage medium | |
| CN110659961A (en) | Method and device for identifying off-line commercial tenant | |
| CN109191181B (en) | Digital signage advertisement audience and crowd classification method based on neural network and Huff model | |
| CN110189163A (en) | Evaluation method, device, electronic equipment and the storage medium of promotional content | |
| CN107767152A (en) | Product purchase intention analysis method and server | |
| CN110543474A (en) | User behavior analysis method and device based on full-buried point and potential factor model | |
| CN103617146B (en) | A kind of machine learning method and device based on hardware resource consumption | |
| CN113886721B (en) | Personalized interest point recommendation method and device, computer equipment and storage medium | |
| CN109978594B (en) | Order processing method, device and medium | |
| CN110335061A (en) | Trade mode portrait method for building up, device, medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180313 |