CN107862053A - User's portrait building method, device and computing device based on customer relationship - Google Patents
User's portrait building method, device and computing device based on customer relationship Download PDFInfo
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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 customer relationship.Wherein method includes：Customer relationship information is obtained, according to customer relationship information structuring customer relationship network；Sampling processing is carried out to customer relationship network, generates customer relationship sequence；Customer relationship sequence is learnt using deep learning algorithm, obtains user's binary feature vector corresponding to each user；Drawn a portrait using user's binary feature vector structuring user's.Based on technical scheme provided by the invention, user's portrait based on customer relationship information structuring 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 the metadata for relying only on user in the prior art, for example, the low-quality defect of user's portrait caused by the structuring user's such as age, sex, income portrait.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to a kind of user's portrait construction side based on customer relationship
Method, 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 customer relationship 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 customer relationship draws a portrait, including：
Customer relationship information is obtained, according to customer relationship information structuring customer relationship network；
Sampling processing is carried out to customer relationship network, generates customer relationship sequence；
Customer relationship sequence is learnt using deep learning algorithm, obtains user's binary feature corresponding to each user
Vector；
Drawn a portrait using user's binary feature vector structuring user's.
Alternatively, sampling processing is carried out to customer relationship network, generation customer relationship sequence further comprises：
Cohesion weighted value in customer relationship network between user is determined based on user social contact frequency information；
Random walk sampling processing is carried out to customer relationship network based on cohesion weighted value, generates customer relationship sequence.
Alternatively, customer relationship sequence is learnt using deep learning algorithm, obtains user corresponding to each user
Binary feature vector further comprises：
Using trained sequence training network, Sequence Learning is carried out to customer relationship sequence, obtains each user couple
The user's binary feature vector answered.
Alternatively, method also includes：Obtain the metadata of user one, according to the metadata of user one generate user's unitary feature to
Amount；
Further comprised using user's binary feature vector structuring user's portrait：
Drawn a portrait using user's binary feature vector sum user's unitary characteristic vector structuring user's.
Alternatively, user's unitary data include：Sex, age, occupation, constellation, height, body weight, shopping type, brand are inclined
Good and/or income.
Alternatively, after structuring user's portrait, method also includes：Drawn a portrait based on user and utilize default Similarity Algorithm point
Analyse the similitude between user.
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, customer relationship information includes：User's short message, telex network network information and/or user social contact should
Use information.
Alternatively, user social contact frequency information includes the one or more of following information：Short message quantity, the duration of call, lead to
Talk about number, social networking application a-c cycle.
According to another aspect of the present invention, there is provided a kind of user's portrait constructing apparatus based on customer relationship, including：
Customer relationship net structure module, suitable for obtaining customer relationship information, closed according to customer relationship information structuring user
It is network；
Sampling processing module, suitable for carrying out sampling processing to customer relationship network, generate customer relationship sequence；
Study module, suitable for learning using deep learning algorithm to customer relationship sequence, it is corresponding to obtain each user
User's binary feature vector；
User's portrait constructing module, suitable for being drawn a portrait using user's binary feature vector structuring user's.
Alternatively, sampling processing module further comprises：
Determining unit, suitable for determining the cohesion power in customer relationship network between user based on user social contact frequency information
Weight values；
Sample processing unit, suitable for carrying out random walk sampling processing to customer relationship network based on cohesion weighted value,
Generate customer relationship sequence.
Alternatively, study module is further adapted for：Using trained sequence training network, customer relationship sequence is entered
Row Sequence Learning, obtain user's binary feature vector corresponding to each user.
Alternatively, device also includes：Acquisition module, suitable for obtaining the metadata of user one；
Generation module, suitable for generating user's unitary characteristic vector according to the metadata of user one；
User's portrait constructing module is further adapted for：Constructed using user's binary feature vector sum user's unitary characteristic vector
User draws a portrait.
Alternatively, user's unitary data include：Sex, age, occupation, constellation, height, body weight, shopping type, brand are inclined
Good and/or income.
Alternatively, device also includes：Similarity analysis module, default Similarity Algorithm point is utilized suitable for being drawn a portrait based on user
Analyse the similitude between user.
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, customer relationship information includes：User's short message, telex network network information and/or user social contact should
Use information.
Alternatively, user social contact frequency information includes the one or more of following information：Short message quantity, the duration of call, lead to
Talk about number, social networking application a-c cycle.
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 extremely
A few executable instruction, executable instruction make computing device above-mentioned corresponding to user's portrait building method based on customer relationship
Operation.
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 customer relationship as described above
Make.
According to scheme provided by the invention, customer relationship information is obtained, according to customer relationship information structuring customer relationship net
Network；Sampling processing is carried out to customer relationship network, generates customer relationship sequence；Using deep learning algorithm to customer relationship sequence
Learnt, obtain user's binary feature vector corresponding to each user；Drawn a portrait using user's binary feature vector structuring user's.
User portrait of the technical scheme provided by the invention based on customer relationship information structuring can fully reflect the feature of user, so as to
The service of more becoming more meticulous easily can be provided the user according to user's portrait, overcome the unitary for relying only on user in the prior art
Data, 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 that the flow of user's portrait building method according to an embodiment of the invention based on customer relationship is shown
It is intended to；
Fig. 2 shows the flow of user's portrait building method in accordance with another embodiment of the present invention based on customer relationship
Schematic diagram；
Fig. 3 shows the structure journey of user's portrait constructing apparatus according to an embodiment of the invention based on customer relationship
Schematic diagram；
Fig. 4 shows the structure of user's portrait constructing apparatus in accordance with another embodiment of the present invention based on customer relationship
Journey 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 that the flow of user's portrait building method according to an embodiment of the invention based on customer relationship is shown
It is intended to.As shown in figure 1, this method comprises the following steps：
Step S100, customer relationship information is obtained, according to customer relationship information structuring customer relationship network.
In embodiments of the present invention, customer relationship information is binary information, is two user mutuals and caused information, is used
Family relational network be with the form of figure represent user between the network of personal connections that contacts, wherein, the customer relationship information includes：With
Family short message, telex network network information and/or user social contact application message.
Specifically, after customer relationship information is got, networking is carried out to the customer relationship information, obtained by node
With the customer relationship network of side composition, user is expressed as in the customer relationship nodes, side represents that two users have relation,
For example, two users had short message communication record, message registration, social networking application communication record etc., in this way, can be by two nodes
Connected with straight line, the side formed between node.
Step S101, sampling processing is carried out to customer relationship network, generates customer relationship sequence.
Although obtaining customer relationship network according to step S100, customer relationship network can not be directly used in construction and use
Family is drawn a portrait, its be only with the form of figure represent user between the network of personal connections that contacts, therefore, it is also desirable to enter to customer relationship network
Row processing, such as sampling processing can be carried out to customer relationship network, to generate customer relationship sequence.
Step S102, customer relationship sequence is learnt using deep learning algorithm, obtain using corresponding to each user
Family binary feature vector.
After customer relationship sequence is obtained according to step S101, deep learning algorithm can be utilized to customer relationship sequence
Learnt, customer relationship sequence is learnt here primarily to obtain user's binary feature corresponding to each user to
Amount, to represent user by vector form, it is easy to subsequent construction user to draw a portrait.
Step S103, drawn a portrait using user's binary feature vector structuring user's.
After user's binary feature vector is obtained according to step S102, can according to resulting user's binary feature to
Amount comes structuring user's portrait, a dimension of user's binary feature vector as structuring user's portrait so that user portrait energy
The feature of enough fully reflection users, overcomes the metadata for relying only on user in the prior art, for example, age, sex, income
User's portrait quality is low caused by being drawn a portrait Deng structuring user's, 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, customer relationship information is obtained, according to customer relationship information structuring
Customer relationship network；Sampling processing is carried out to customer relationship network, generates customer relationship sequence；Using deep learning algorithm to
Family relational sequence is learnt, and obtains user's binary feature vector corresponding to each user；Utilize user's binary feature vector structure
Make user's portrait.User portrait of the present embodiment based on customer relationship information structuring can fully reflect the feature of user, so as to
The service of more becoming more meticulous easily can be provided the user according to user's portrait, overcome the unitary for relying only on user in the prior art
Data, for example, the low-quality defect of user's portrait caused by the structuring user's such as age, sex, income portrait.
Fig. 2 shows the flow of user's portrait building method in accordance with another embodiment of the present invention based on customer relationship
Schematic diagram.As shown in Fig. 2 this method comprises the following steps：
Step S200, customer relationship information is obtained, according to customer relationship information structuring customer relationship network.
In embodiments of the present invention, customer relationship information is binary information, is two user mutuals and caused information, is used
Family relational network be with the form of figure represent user between the network of personal connections that contacts, wherein, the customer relationship information includes：With
Family short message, telex network network information and/or user social contact application message.
Specifically, after customer relationship information is got, to the customer relationship information carry out networking, obtain node and
The customer relationship network on side, user being expressed as in the customer relationship nodes, side represents that two users have relation, for example,
Two users had short message communication record, message registration, social networking application communication record etc., in this way, can be by two nodes with directly
Line connects, the side formed between node.
Step S201, the cohesion weight in customer relationship network between user is determined based on user social contact frequency information
Value.
The customer relationship network constructed in step S200 is the network for having weight, and the weighted value is used to weigh customer relationship
Cohesion in network between user, weighted value is higher, and two users are more intimate, and therefore, the cohesion between user can be used
In similitude for judging user etc..
In embodiments of the present invention, weight can determine, user social contact frequency information according to user social contact frequency information
The frequency of user's contact is embodied, wherein, user social contact frequency information specifically includes the one or more of following information：Short message number
Amount, the duration of call, talk times, social networking application a-c cycle.Assert two user's messaging communications quantity is higher, the duration of call
More long, talk times are more, and social networking application a-c cycle is bigger, and the cohesion weighted value between user is higher, conversely, user it
Between cohesion weighted value it is lower.
Step S202, random walk sampling processing is carried out to customer relationship network based on cohesion weighted value, generates user
Relational sequence.
Random walk sampling processing refers to some initial point from customer relationship network, from the neighbor node of present node
In randomly select next-hop node of the node as present node, random walk reaches the neighbor node, and to node
Relevant information is sampled.
Specifically, after the cohesion weighted value between user is obtained according to step S201, cohesion can be based on and weighed
Weight values carry out random walk sampling processing to customer relationship network, and random walk number can be set according to being actually needed,
Customer relationship sequence is generated, the customer relationship sequence embodies the sequencing of random walk.
Step S203, using trained sequence training network, Sequence Learning is carried out to customer relationship sequence, obtained every
User's binary feature vector corresponding to individual user.
Used sequence training network is trained to obtain based on substantial amounts of sample in the embodiment of the present invention, by institute
The customer relationship sequence inputting of generation to sequence training network, sequence training network enters to accessed customer relationship sequence
Row Sequence Learning, each user is represented with the higher vector of dimension with unified, that is, obtains user's binary corresponding to each user
Characteristic vector, realize the expression into characteristic vector by customer relationship network abstraction.
Step S204, the metadata of user one is obtained, user's unitary characteristic vector is generated according to the metadata of user one.
The metadata of user one is only the data for characterizing the user, can be user's registration with the metadata of other users user one
The information that is inputted during application program or generated according to user using behavioural information during application program, for example,
Shopping type, Brang Preference etc., can also get the metadata of user one by other approach.Wherein, user's unitary packet
Include：Sex, age, occupation, constellation, height, body weight, shopping type, Brang Preference and/or income.Here it is merely illustrative of,
Without any restriction effect.After the metadata of user one is got, it is special user's unitary can be generated according to the metadata of user one
Sign vector.
Step S205, drawn a portrait using user's binary feature vector sum user's unitary characteristic vector structuring user's.
, can after user's binary feature vector sum user's unitary characteristic vector is obtained according to step S203 and step S204
To be drawn a portrait according to resulting user's geographic location feature vector sum application features vector come structuring user's, user portrait
The feature of user can fully be reflected, combination user's binary feature vector sum user's unitary characteristic vector of the embodiment of the present invention carrys out structure
User's portrait is made, the metadata for relying only on user in the prior art is overcome, for example, the structuring user's such as age, sex, income
User's portrait quality is low caused by 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：Drawn a portrait and utilized based on user
Similitude between default Similarity Algorithm analysis user.
The user's portrait constructed using the present invention is more comprehensive, therefore platform can draw a portrait according to user and analyze two use
The similitude at family, specifically, the similitude between user can be analyzed using Euclidean distance algorithm.
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 customer relationship come structuring user's using user provided in an embodiment of the present invention portrait building method, Neng Goufen
Whether analysis user just has fraud, for example, certain several users contact is especially frequent, and in one day again with a lot of other use
Family contacts, for example, one day user different from 50 contacts, then probably belongs to fraudulent groups, for user here, then may be used
To assert user behavior exception, drawn a portrait based on user and analyze whether user has abnormal behaviour, finance company can be referred to
Lead.
Judged in addition, may also be combined with blacklist mechanism, for example, determining that certain user will take advantage of based on blacklist mechanism
Swindleness behavior, then after building method structuring user's portrait of being drawn a portrait using user provided in an embodiment of the present invention, determine certain
Several users are similar, then it can be assumed that other several users also have fraud.
The method provided according to the above embodiment of the present invention, customer relationship information is obtained, according to customer relationship information structuring
Customer relationship network, the cohesion weighted value in customer relationship network between user, base are determined based on user social contact frequency information
Random walk sampling processing is carried out to customer relationship network in cohesion weighted value, generates customer relationship sequence, using by instructing
Experienced sequence training network, to customer relationship sequence carry out Sequence Learning, obtain user's binary feature corresponding to each user to
Amount, the metadata of user one is obtained, user's unitary characteristic vector is generated according to the metadata of user one, utilize user's binary feature vector
Drawn a portrait with user's unitary characteristic vector structuring user's.Based on technical scheme provided by the invention, based on customer relationship information structuring
User's portrait can fully reflect the feature of user, constructed with reference to user's binary feature vector sum user's unitary characteristic vector
User draws a portrait, and easily can 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.
Fig. 3 shows the structure journey of user's portrait constructing apparatus according to an embodiment of the invention based on customer relationship
Schematic diagram.As shown in figure 3, the device includes：Customer relationship net structure module 300, sampling processing module 310, study module
320th, user's portrait constructing module 330.
Customer relationship net structure module 300, suitable for obtaining customer relationship information, according to customer relationship information structuring user
Relational network.
Sampling processing module 310, suitable for carrying out sampling processing to customer relationship network, generate customer relationship sequence.
Study module 320, suitable for learning using deep learning algorithm to customer relationship sequence, obtain each user couple
The user's binary feature vector answered.
User's portrait constructing module 330, suitable for being drawn a portrait using user's binary feature vector structuring user's.
The device provided according to the above embodiment of the present invention, customer relationship information is obtained, according to customer relationship information structuring
Customer relationship network；Sampling processing is carried out to customer relationship network, generates customer relationship sequence；Using deep learning algorithm to
Family relational sequence is learnt, and obtains user's binary feature vector corresponding to each user；Utilize user's binary feature vector structure
Make user's portrait.Based on technical scheme provided by the invention, user's portrait based on customer relationship information structuring can be fully anti-
The feature of user is reflected, so as to easily provide the user the service of more becoming more meticulous according to user's portrait, overcomes prior art
In rely only on the metadata of user, for example, user draws a portrait quality caused by the structuring user's such as age, sex, income portrait
The defects of low.
Fig. 4 shows the structure of user's portrait constructing apparatus in accordance with another embodiment of the present invention based on customer relationship
Journey schematic diagram.As shown in figure 4, the device includes：Customer relationship net structure module 400, sampling processing module 410, study mould
Block 420, acquisition module 430, generation module 440, user's portrait constructing module 450.
Customer relationship net structure module 400, suitable for obtaining customer relationship information, according to customer relationship information structuring user
Relational network.
Wherein, customer relationship information includes：User's short message, telex network network information and/or user social contact application
Information.
Sampling processing module 410 further comprises：Determining unit 411, suitable for determining to use based on user social contact frequency information
Cohesion weighted value in the relational network of family between user；
Wherein, user social contact frequency information includes the one or more of following information：Short message quantity, the duration of call, call
Number, social networking application a-c cycle.
Sample processing unit 412, suitable for being carried out based on cohesion weighted value to customer relationship network at random walk sampling
Reason, generate customer relationship sequence.
Study module 420 is further adapted for：Using trained sequence training network, sequence is carried out to customer relationship sequence
Row study, obtain user's binary feature vector corresponding to each user.
Acquisition module 430, suitable for obtaining the metadata of user one.
Wherein, user's unitary data include：Sex, age, occupation, constellation, height, body weight, shopping type, Brang Preference
And/or income.
Generation module 440, suitable for generating user's unitary characteristic vector according to the metadata of user one.
User's portrait constructing module 450 is further adapted for：Utilize user's binary feature vector sum user's unitary characteristic vector
Structuring user's are drawn a portrait.
In alternative embodiment of the present invention, the device also includes：Similarity analysis module 460, suitable for being drawn based on user
As analyzing the similitude between user using default Similarity Algorithm.
In alternative embodiment of the present invention, the device also includes：Training module 470, suitable for based on user's portrait sample
Training user's hobby model；
Hobby analysis module 480, 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 490, suitable for based on user
Whether portrait analysis user has abnormal behaviour.
The device provided according to the above embodiment of the present invention, customer relationship information is obtained, according to customer relationship information structuring
Customer relationship network, the cohesion weighted value in customer relationship network between user, base are determined based on user social contact frequency information
Random walk sampling processing is carried out to customer relationship network in cohesion weighted value, generates customer relationship sequence, using by instructing
Experienced sequence training network, to customer relationship sequence carry out Sequence Learning, obtain user's binary feature corresponding to each user to
Amount, the metadata of user one is obtained, user's unitary characteristic vector is generated according to the metadata of user one, utilize user's binary feature vector
Drawn a portrait with user's unitary characteristic vector structuring user's.Based on technical scheme provided by the invention, based on customer relationship information structuring
User's portrait can fully reflect the feature of user, constructed with reference to user's binary feature vector sum user's unitary characteristic vector
User draws a portrait, and easily can 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.
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 customer relationship
User draw a 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 based on customer relationship
Correlation step in embodiment of the method.
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 performs being closed based on user in above-mentioned any means embodiment
User's portrait building method of system.The specific implementation of each step may refer to the above-mentioned user based on customer relationship in program 510
Corresponding description in corresponding steps and unit in constructed embodiment of drawing a portrait, will not be described here.Those skilled in the art can
To be well understood, for convenience and simplicity of description, the equipment of foregoing description and the specific work process of module, may be referred to
Corresponding process description in preceding method embodiment, 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) realize user's portrait according to embodiments of the present invention based on customer relationship
The some or all functions of some or all parts in constructing apparatus.The present invention is also implemented as being used to perform here
The some or all equipment or program of device of described method 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 customer relationship draws a portrait, including：Customer relationship information is obtained, according to the customer relationship information structuring customer relationship network；Sampling processing is carried out to the customer relationship network, generates customer relationship sequence；The customer relationship sequence is learnt using deep learning algorithm, obtains user's binary feature corresponding to each user Vector；Drawn a portrait using user's binary feature vector structuring user's.
- 2. according to the method for claim 1, wherein, described that sampling processing is carried out to customer relationship network, generation user is closed It is that sequence further comprises：Cohesion weighted value in the customer relationship network between user is determined based on user social contact frequency information；Random walk sampling processing is carried out to the customer relationship network based on the cohesion weighted value, generates customer relationship sequence Row.
- 3. method according to claim 1 or 2, wherein, it is described to utilize deep learning algorithm to the customer relationship sequence Learnt, obtain user's binary feature vector corresponding to each user and further comprise：Using trained sequence training network, Sequence Learning is carried out to the customer relationship sequence, obtains each user couple The user's binary feature vector answered.
- 4. according to the method described in claim any one of 1-3, wherein, methods described also includes：Obtain the metadata of user one, root User's unitary characteristic vector is generated according to the metadata of user one；It is described to be further comprised using user's binary feature vector structuring user's portrait：Drawn a portrait using user's unitary characteristic vector structuring user's described in user's binary feature vector sum.
- 5. according to the method for claim 4, wherein, user's unitary data include：Sex, the age, occupation, constellation, Height, body weight, shopping type, Brang Preference and/or income.
- 6. according to the method described in claim any one of 1-5, wherein, after structuring user's portrait, methods described also includes： Drawn a portrait based on the user and utilize the similitude between default Similarity Algorithm analysis user.
- 7. according to the method described in claim any one of 1-5, 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.
- The constructing apparatus 8. a kind of user based on customer relationship draws a portrait, including：Customer relationship net structure module, suitable for obtaining customer relationship information, closed according to the customer relationship information structuring user It is network；Sampling processing module, suitable for carrying out sampling processing to the customer relationship network, generate customer relationship sequence；Study module, suitable for learning using deep learning algorithm to the customer relationship sequence, it is corresponding to obtain each user User's binary feature vector；User's portrait constructing module, suitable for being drawn a portrait using user's binary feature vector structuring user's.
- 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 customer relationship 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 customer relationship of the computing device as any one of claim 1-7 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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| CN112465565A (en) * | 2020-12-11 | 2021-03-09 | 加和（北京）信息科技有限公司 | User portrait prediction method and device based on machine learning |
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| CN112559301A (en) * | 2019-09-10 | 2021-03-26 | 网易（杭州）网络有限公司 | Service processing method, storage medium, processor and electronic device |
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| CN108718444A (en) * | 2018-04-17 | 2018-10-30 | 南京邮电大学 | A kind of D2D communication pattern selection methods based on social activity perception |
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| CN112559301B (en) * | 2019-09-10 | 2022-05-27 | 网易（杭州）网络有限公司 | Service processing method, storage medium, processor and electronic device |
| WO2021068608A1 (en) * | 2019-10-11 | 2021-04-15 | 深圳壹账通智能科技有限公司 | Method and apparatus for extracting user portrait, and computer device and storage medium |
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| CN113115200B (en) * | 2019-12-24 | 2023-04-18 | 中国移动通信集团浙江有限公司 | User relationship identification method and device and computing equipment |
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| CN112465565A (en) * | 2020-12-11 | 2021-03-09 | 加和（北京）信息科技有限公司 | User portrait prediction method and device based on machine learning |
| CN112465565B (en) * | 2020-12-11 | 2023-09-26 | 加和（北京）信息科技有限公司 | User portrait prediction method and device based on machine learning |
| CN115221954A (en) * | 2022-07-12 | 2022-10-21 | 中国电信股份有限公司 | User portrait method, device, electronic equipment and storage medium |
| CN115221954B (en) * | 2022-07-12 | 2023-10-31 | 中国电信股份有限公司 | User portrait method, device, electronic equipment and storage medium |
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