CN105809557A - Method and device for mining genders of users in social network - Google Patents
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Abstract
Embodiments of the invention provide a method and device for mining genders of users in a social network. The method comprises the following steps: obtaining behavior feature information of the current user in the social network; carrying out probability estimation on the gender of the current user according to the behavior feature information of the current user in the social network by utilizing a user gender model which is established on the basis of behavior feature information of a sample user, and outputting a prediction result; determining the gender of the current user according to the prediction result. The method has the following benefits: the genders of the users are mined by utilizing an automatic classification method on the basis of social network behavior information such as names and nicknames of the users; and automatic classification is carried out by using structured and semi-structured features, so that the method has the characteristics of being simple in recognition method, free of noise interference and high in recognition correctness when being compared with the traditional audio and picture recognition-based method.
Description
Technical field
The present invention relates to networking technology area, particularly relate to and a kind of excavate the method and apparatus of user's sex in social networks.
Background technology
For excavating social network user sex, having scholar to propose to carry out speaker's sex based on voice at present and know method for distinguishing, the method uses the mode that speech sound signal processes, and captures the sound characteristic of speaker, thereby judges that the sex of speaker is sex.At least there is following several respects shortcoming in current method: 1, needs to pre-build the vocal print storehouse of jumbo masculinity and femininity, and in the Internet social networks, it is extremely difficult for obtaining a large number of users acoustic information.2, recognition effect can be subject to environment and the state impact of speaker, if speaker speaks in noisy environment, then the method cannot be accurately judged to the sex of user.The shortcoming of above-mentioned two aspects can cause that the method is not suitable for the Internet user in social networks is carried out sex identification.
Second method carries out user sex determination in social networks by the mode of picture recognition, and namely computer first detects in image whether there is face, if it is present provide the information such as position coordinates, area size, is separated by face；The face detected extracts sex characteristics, further according to the face feature identified, face gender to be identified is judged.The quality of the whether accurate heavy dependence user face picture of the method, if picture blur is unclear, then recognition effect can be deteriorated.And in social networks, the head portrait that user uploads is all often process through overcompression, there is the situation of distortion；Under many circumstances, the picture that user even uploads not is my true picture, thus results in the method complete failure.
In realizing process of the present invention, inventor have found that in prior art, at least there are the following problems: how the Internet user in social networks carrying out sex targetedly and excavates judgement, this is the technical problem that those skilled in the art is urgently to be resolved hurrily.
Summary of the invention
The embodiment of the present invention provides a kind of and excavates the method and apparatus of user's sex in social networks, to excavate the sex of the user in social networks.
On the one hand, embodiments providing user's property method for distinguishing in a kind of excavation social networks, described method includes:
Obtain active user's behavior characteristic information in social networks；
Utilizing the user's sex model set up based on the behavior characteristic information of sample of users, according to described active user behavior characteristic information in social networks, the sex of described active user is carried out probability Estimation, output predicts the outcome；
The sex of described active user is determined according to described predicting the outcome.
On the other hand, embodiments providing and a kind of excavate the device of user's sex in social networks, described device includes:
Information acquisition unit, for obtaining active user's behavior characteristic information in social networks；
Sex model unit, for utilizing user's sex model that the behavior characteristic information based on sample of users sets up, according to described active user behavior characteristic information in social networks, carries out probability Estimation to the sex of described active user, and output predicts the outcome；
Sex excavates unit, predicts the outcome and determine the sex of described active user described in basis.
Technique scheme has the advantages that this programme behavioural information based on social networkies such as the name of user, the pet names, the method using classification automatically, the sex of digging user, automatically classify owing to employing structuring and semi-structured feature, thus compare with traditional method based on audio frequency, picture recognition, there is recognition methods simple, not by noise jamming, the feature that recognition accuracy is high.Additionally this programme only relies on user's behavioural information in social networks, and selected characteristic dimension is few, and classification speed is fast, has stronger practicality.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, the accompanying drawing used required in embodiment or description of the prior art will be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is that the embodiment of the present invention is a kind of excavates the method flow diagram of user's sex in social networks；
Fig. 2 is a kind of apparatus structure schematic diagram excavating user's sex in social networks of the embodiment of the present invention；
Fig. 3 is that cellular construction schematic diagram set up by embodiment of the present invention sex model.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, broadly fall into the scope of protection of the invention.
As it is shown in figure 1, excavate the method flow diagram of user's sex in social networks for the embodiment of the present invention is a kind of, described method includes:
101, active user's behavior characteristic information in social networks is obtained；
102, utilizing the user's sex model set up based on the behavior characteristic information of sample of users, according to described active user behavior characteristic information in social networks, the sex of described active user is carried out probability Estimation, output predicts the outcome；
103, according to described in predict the outcome and determine the sex of described active user.
Preferably, the described behavior characteristic information according to described active user in social networks, the sex of described active user is carried out probability Estimation, specifically includes: described active user behavior characteristic information in social networks is carried out feature pretreatment, obtains the characteristic vector of active user；Characteristic vector according to active user, calculates described active user probability under each sex respectively, using the sex of maximum probability as the output that predicts the outcome.
Preferably, described method also includes: the behavior characteristic information according to the sample of users of the known sex of the predetermined quantity chosen, and carries out feature pretreatment；To each characteristic vector obtained after feature pretreatment, carry out sample training, set up user's sex model；Wherein, the behavior characteristic information of the sample of users of the known sex of the predetermined quantity chosen described in is obtained by questionnaire survey.
Preferably, described behavior characteristic information includes: personalized template information that registration fill message, user uses in social networks, certainly fill out label information；Wherein, described registration fill message includes: name, the pet name；Described user's sex model includes: Naive Bayes Classifier.
Preferably, the described method that behavior characteristic information is carried out feature pretreatment, including: call participle program, to name, the pet name, certainly fill out label information and carry out word segmentation processing；After carrying out word segmentation processing, name and the pet name are carried out feature extraction, remove surname, only reserved name.
Corresponding to said method embodiment, as in figure 2 it is shown, be a kind of apparatus structure schematic diagram excavating user's sex in social networks of the embodiment of the present invention, described device includes:
Information acquisition unit 21, for obtaining active user's behavior characteristic information in social networks；
Sex model unit 22, for utilizing user's sex model that the behavior characteristic information based on sample of users sets up, according to described active user behavior characteristic information in social networks, carries out probability Estimation to the sex of described active user, and output predicts the outcome；
Sex excavates unit 23, predicts the outcome and determine the sex of described active user described in basis.
Preferably, described device also includes: feature pretreatment unit 20, for user's behavior characteristic information in social networks is carried out feature pretreatment, obtains the characteristic vector of user；Described sex model unit, specifically for the characteristic vector according to active user, calculates described active user probability under each sex respectively, using the sex of maximum probability as the output that predicts the outcome.
Preferably, described device also includes: unit 24 set up by sex model, for the behavior characteristic information of the sample of users of the known sex according to the predetermined quantity chosen, carries out feature pretreatment；To each characteristic vector obtained after feature pretreatment, carry out sample training, set up user's sex model；Wherein, the behavior characteristic information of the sample of users of the known sex of the predetermined quantity chosen described in is obtained by questionnaire survey.
Preferably, described behavior characteristic information includes: personalized template information that registration fill message, user uses in social networks, certainly fill out label information；Wherein, described registration fill message includes: name, the pet name；Described user's sex model includes: Naive Bayes Classifier.
Preferably, as it is shown on figure 3, be embodiment of the present invention one feature pretreatment unit structural representation, described feature pretreatment unit 20 includes:
Word segmentation processing module 201, for behavior characteristic information, calls participle program, to name, the pet name, certainly fills out label information and carries out word segmentation processing；
Feature extraction module 202, for, after carrying out word segmentation processing, name and the pet name carrying out feature extraction, removes surname, only reserved name.
Below by way of application example, embodiment of the present invention technique scheme is described in detail:
In social networks, identify user's sex fairly heavy want, in some cases, it is possible to the sex according to user, to its recommend related content；Him is recommended to be likely to people interested；Recommend to be likely to article interested.Such as can recommend female article (such as skirt, high-heel shoes etc.) in conjunction with other information such as age to women.
The behavioural informations such as the label that template that when application example of the present invention is intended to by obtaining user and registering, the network pet name filled in, personal homepage use, individual fill in, extract identifying sex useful feature, use bayes method to set up Gender Classification model again, newer user is carried out gender prediction.
The Computational frame of application example of the present invention is as follows:
Step 1: choose classification useful feature, and carry out feature pretreatment.
This step first to build training sample database, namely first chooses the user of certain known sex, extracts the pet name filled in when user registers, template number that name, personal homepage use, certainly fills out label.Call participle program, to the pet name, name, certainly fill out label and carry out word segmentation processing.Owing to the surname in name and the pet name is difficult to characterize sex, thus during feature extraction, only reserved name, and ignore surname.
Step 2: to the sample after characteristic processing, be trained, obtain training pattern.For vector to be predicted, according to model, calculate and output predicts the outcome
Can using multiple model that sample is trained, the model that application example of the present invention selects here is naive Bayesian.Naive Bayesian computing formula is as follows:
Wherein x is input vector, and k is 1 or 2, represents man or female respectively.
Conditional probability has been done conditional independence assumption by naive Bayesian, and conditional independence assumption is:
Wherein X(i)Represent the i-th dimension feature of input vector.
Formula (2) is substituted into formula (1), and then, Naive Bayes Classifier can be expressed as:
Due in formula (3), for all of class, denominator is all identical, so,
Namely for given input x, using the class maximum for the posterior probability output as the class of x.
Example:
Step 1: obtain training sample, and carry out feature pretreatment.Training sample can be obtained by questionnaire survey, as shown in table 1 below (for purposes of illustration only, only selected part data):
Table 1
Step 2: have a user, name: Chen Anbang；The pet name: GordenChen；The template used: blue；Label from filling out: football,
The Bayesian Estimation of conditional probability is:
Wherein SjRepresent X(j)Value number.
After the name of input vector, the pet name have been divided word and removed surname, what obtain by name stablizes the country, Gorden.4 final dimensional feature vectors stablize the country according to formula (6) i.e. X={ is old, Gorden, blue, football }, according to formula (6)
According to formula (5)
P(X(1)=stablize the country .X(2)=Gorden, X(3)=blue, X(4)=football | Y=c1)=(1/7) * (3/17) * (1/2) * (3/7)=0.0054P (X(1)=stablize the country .X(2)=Gorden, X(3)=blue, X(4)=football | Y=c2)=(1/21) * (1/17) * (1/12) * (1/14)=0.000054
According to formula (4), it is judged that this user belongs to classification 1, i.e. male.
The beneficial effect that application example technical scheme of the present invention is brought: this programme is based on social networks behaviors such as the name of user, the pet names, the method using classification automatically, the sex of digging user, automatically classify owing to employing structuring and semi-structured feature, thus compare with traditional method based on audio frequency, picture recognition, there is recognition methods simple, not by noise jamming, the feature that recognition accuracy is high.Additionally this programme only relies on log-on message and the behavioral data of user, and selected characteristic dimension is few, and classification speed is fast, has stronger practicality.
Should be understood that the particular order of the step in disclosed process or level are the examples of illustrative methods.Based on design preference, it should be appreciated that the particular order of the step in process or level can be rearranged when without departing from the protection domain of the disclosure.Appended claim to a method gives the key element of various step with exemplary order, and is not limited to described particular order or level.
In above-mentioned detailed description, various features are combined in single embodiment together, to simplify the disclosure.Should not be construed to reflect such intention by this open method, i.e. the embodiment of theme required for protection needs feature more more than the feature clearly stated in each claim.On the contrary, as the following claims reflect, the present invention is in the state fewer than whole features of disclosed single embodiment.Therefore, appending claims is hereby expressly incorporated in detailed description, and wherein each claim is alone as the preferred embodiment that the present invention is independent.
For making any technical staff in this area be capable of or use the present invention, above disclosed embodiment is described.To those skilled in the art；The various amendment modes of these embodiments will be apparent from, and generic principles defined herein can also be applicable to other embodiments on the basis without departing from the spirit and scope of the disclosure.Therefore, the disclosure is not limited to embodiments set forth herein, but consistent with the widest scope of principle disclosed in the present application and novel features.
Described above includes the citing of one or more embodiment.Certainly, all possible combination describing parts or method in order to describe above-described embodiment is impossible, but it will be appreciated by one of ordinary skill in the art that each embodiment can do further combinations and permutations.Therefore, embodiment described herein is intended to all such changes, modifications and variations of falling in the protection domain of appended claims.Additionally, with regard in description or claims use term " comprising ", the mode that contains of this word is similar to term " including ", just as " including, " in the claims be used as link word explain such.Additionally, any one term being used in the description of claims " or " it is to represent " non-exclusionism or ".
Those skilled in the art are it will also be appreciated that the various illustrative components, blocks (illustrativelogicalblock) listed of the embodiment of the present invention, unit, and step can pass through electronic hardware, computer software, or both combinations realize.For clearly showing that the replaceability (interchangeability) of hardware and software, above-mentioned various illustrative components (illustrativecomponents), unit and step have universally described their function.Such function is to realize depending on the designing requirement of specific application and whole system by hardware or software.Those skilled in the art can for every kind of specific application, it is possible to use various methods realize described function, but this realization is understood not to exceed the scope of embodiment of the present invention protection.
Various illustrative logical block described in the embodiment of the present invention, or unit is such as through general processor, digital signal processor, special IC (ASIC), field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the design of any of the above described combination realize or operate described function.General processor can be microprocessor, and alternatively, this general processor can also be any traditional processor, controller, microcontroller or state machine.Processor can also be realized by the combination of calculation element, for instance digital signal processor and microprocessor, multi-microprocessor, one or more microprocessors one Digital Signal Processor Core of associating, or any other like configuration realizes.
Method described in the embodiment of the present invention or the step of algorithm can be directly embedded into hardware, processor performs software module or the combination of both.Software module can be stored in RAM memory, flash memory, ROM memory, eprom memory, eeprom memory, depositor, hard disk, moveable magnetic disc, CD-ROM or this area in other any form of storage medium.Exemplarily, storage medium can be connected with processor, so that processor can read information from storage medium, it is possible to deposit write information to storage medium.Alternatively, storage medium can also be integrated in processor.Processor and storage medium can be arranged in ASIC, and ASIC can be arranged in user terminal.Alternatively, processor and storage medium can also be arranged in the different parts in user terminal.
In one or more exemplary designs, the above-mentioned functions described by the embodiment of the present invention can realize in the combination in any of hardware, software, firmware or this three.If realized in software, these functions can store and on the medium of computer-readable, or be transmitted on the medium of computer-readable with one or more instructions or code form.Computer readable medium includes computer storage medium and is easy to so that allowing computer program transfer to the telecommunication media in other place from a place.Storage medium can be that any general or special computer can the useable medium of access.Such as, such computer readable media can include but not limited to RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage device, or other any may be used for carrying or storage with instruction or data structure and other can be read the medium of program code of form by general or special computer or general or special processor.In addition, any connection can be properly termed computer readable medium, such as, if software is by a coaxial cable, fiber optic cables, twisted-pair feeder, Digital Subscriber Line (DSL) or being also contained in defined computer readable medium with wireless way for transmittings such as such as infrared, wireless and microwaves from a web-site, server or other remote resource.Described video disc (disk) and disk (disc) include Zip disk, radium-shine dish, CD, DVD, floppy disk and Blu-ray Disc, and disk is generally with magnetic duplication data, and video disc generally carries out optical reproduction data with laser.Combinations of the above can also be included in computer readable medium.
Above-described detailed description of the invention; the purpose of the present invention, technical scheme and beneficial effect have been further described; it is it should be understood that; the foregoing is only the specific embodiment of the present invention; the protection domain being not intended to limit the present invention; all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (10)
1. one kind is excavated user's property method for distinguishing in social networks, it is characterised in that described method includes:
Obtain active user's behavior characteristic information in social networks；
Utilizing the user's sex model set up based on the behavior characteristic information of sample of users, according to described active user behavior characteristic information in social networks, the sex of described active user is carried out probability Estimation, output predicts the outcome；
The sex of described active user is determined according to described predicting the outcome.
2. method as claimed in claim 1, it is characterised in that the described behavior characteristic information according to described active user in social networks, carries out probability Estimation to the sex of described active user, specifically includes:
Described active user behavior characteristic information in social networks is carried out feature pretreatment, obtains the characteristic vector of active user；
Characteristic vector according to active user, calculates described active user probability under each sex respectively, using the sex of maximum probability as the output that predicts the outcome.
3. method as claimed in claim 1, it is characterised in that described method also includes:
The behavior characteristic information of the sample of users of the known sex according to the predetermined quantity chosen, carries out feature pretreatment；
To each characteristic vector obtained after feature pretreatment, carry out sample training, set up user's sex model；Wherein, the behavior characteristic information of the sample of users of the known sex of the predetermined quantity chosen described in is obtained by questionnaire survey.
4. method as described in Claims 2 or 3, it is characterised in that described behavior characteristic information includes: personalized template information that registration fill message, user uses in social networks, certainly fill out label information；Wherein, described registration fill message includes: name, the pet name；Described user's sex model includes: Naive Bayes Classifier.
5. method as claimed in claim 4, it is characterised in that the described method that behavior characteristic information is carried out feature pretreatment, including:
Call participle program, to name, the pet name, certainly fill out label information and carry out word segmentation processing；
After carrying out word segmentation processing, name and the pet name are carried out feature extraction, remove surname, only reserved name.
6. one kind is excavated the device of user's sex in social networks, it is characterised in that described device includes:
Information acquisition unit, for obtaining active user's behavior characteristic information in social networks；
Sex model unit, for utilizing user's sex model that the behavior characteristic information based on sample of users sets up, according to described active user behavior characteristic information in social networks, carries out probability Estimation to the sex of described active user, and output predicts the outcome；
Sex excavates unit, predicts the outcome and determine the sex of described active user described in basis.
7. device as claimed in claim 6, it is characterised in that described device also includes:
Feature pretreatment unit, for user's behavior characteristic information in social networks is carried out feature pretreatment, obtains the characteristic vector of user；
Described sex model unit, specifically for the characteristic vector according to active user, calculates described active user probability under each sex respectively, using the sex of maximum probability as the output that predicts the outcome.
8. device as claimed in claim 6, it is characterised in that described device also includes:
Unit set up by sex model, for the behavior characteristic information of the sample of users of the known sex according to the predetermined quantity chosen, carries out feature pretreatment；To each characteristic vector obtained after feature pretreatment, carry out sample training, set up user's sex model；Wherein, the behavior characteristic information of the sample of users of the known sex of the predetermined quantity chosen described in is obtained by questionnaire survey.
9. device as described in claim 7 or 8, it is characterised in that described behavior characteristic information includes: personalized template information that registration fill message, user uses in social networks, certainly fill out label information；Wherein, described registration fill message includes: name, the pet name；Described user's sex model includes: Naive Bayes Classifier.
10. device as claimed in claim 9, it is characterised in that described feature pretreatment unit includes:
Word segmentation processing module, for behavior characteristic information, calls participle program, to name, the pet name, certainly fills out label information and carries out word segmentation processing；
Feature extraction module, for, after carrying out word segmentation processing, name and the pet name carrying out feature extraction, removes surname, only reserved name.
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| CN109271957A (en) * | 2018-09-30 | 2019-01-25 | 厦门市巨龙信息科技有限公司 | Face gender identification method and device |
| CN110032640A (en) * | 2019-01-07 | 2019-07-19 | 阿里巴巴集团控股有限公司 | The determination method and device of service plan |
| CN110100276A (en) * | 2016-12-22 | 2019-08-06 | 大众汽车有限公司 | The voice output sound of voice operating system |
| CN111143441A (en) * | 2019-12-30 | 2020-05-12 | 北京每日优鲜电子商务有限公司 | Gender determination method, device, equipment and storage medium |
| CN111199208A (en) * | 2019-12-31 | 2020-05-26 | 上海昌投网络科技有限公司 | Head portrait gender identification method and system based on deep learning framework |
| CN113535885A (en) * | 2021-09-09 | 2021-10-22 | 北京轻松筹信息技术有限公司 | Age prediction method and device based on user nickname and electronic equipment |
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