CN104951544A - User data processing method and system and method and system for providing user data - Google Patents
User data processing method and system and method and system for providing user data Download PDFInfo
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- CN104951544A CN104951544A CN201510347820.2A CN201510347820A CN104951544A CN 104951544 A CN104951544 A CN 104951544A CN 201510347820 A CN201510347820 A CN 201510347820A CN 104951544 A CN104951544 A CN 104951544A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- 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
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
Abstract
The embodiment of the invention provides a user data processing method and system and a method and system for providing user data. The user data processing method based on user network behaviors includes the steps that network behavior data of a user are obtained; the network behavior data of the user are recognized through at least one classification model used for recognizing the attribute of the user, and at least one piece of attribute information of the user is obtained; the attribute information, obtained through recognition, of the user is added into user model data of the user. According to the technical scheme, the attribute information of the user is obtained and provided based on the user network behaviors.
Description
Technical field
The present invention relates to the information processing technology, particularly relate to a kind of user data disposal route, the supplying method of user data and system.
Background technology
Be devoted to the demand meeting government, enterprise monitors for network public-opinion, need to utilize whole network data (comprising microblogging, mhkc, news, forum etc.) to be concerned about that event is monitored in real time to focus incident, user.In public sentiment monitoring, except public sentiment event itself, the demographic data that event is correlated with also is the information that demander is paid close attention to.Along with the privacy aware of people is instantly strong all the more, user less and less initiatively can fill in personal information and open on network, therefore how to obtain the data of user exactly, just becomes a great problem in public sentiment monitoring.
Summary of the invention
Embodiments of the invention provide supplying method and the system of a kind of user data disposal route, user data, to obtain the attribute information of user based on the network behavior of user.
For achieving the above object, The embodiment provides a kind of user data disposal route based on user network behavior, comprising: the network behavior data obtaining user; Respectively by least one for identifying that the network behavior data of the disaggregated model of user property to described user identify, obtain at least one customer attribute information of described user; To identify that at least one customer attribute information of the described user obtained adds in the user model data of described user.
Further, the user model data of described user comprise identification information and the customer attribute information described at least one of user.
Alternatively, described method also comprises: extract keyword, according to the Keyword Selection extracted for identifying the described disaggregated model of user property from the network behavior data of described user.
Further, described customer attribute information comprises at least one in the middle of age, sex, region, interest, affiliated industry.
Embodiments of the invention additionally provide a kind of supplying method of user data, comprising: the network behavior data obtaining user; From user ID mapping table, obtain second user ID of described user according to the first user mark of described user, wherein, described user ID mapping table associated record has multiple user ID of user; Obtain the user model data of described user according to described second user ID, described user model data comprise at least one customer attribute information of described user.
Alternatively, described method also comprises: the first user mark obtaining described user from described network behavior data.
Further, multiple user ID of described user ID mapping table associated record comprise less than at least two marks: the register account number of at least one user, MAC Address, called subscriber's identifier CUID and international mobile equipment identification number IMEI.
Embodiments of the invention additionally provide a kind of user data disposal system based on user network behavior, comprising: data acquisition module, for obtaining the network behavior data of user; Data identification module, for respectively by least one for identifying that the network behavior data of the disaggregated model of user property to described user identify, obtain at least one customer attribute information of described user; Information adds module, for identifying that at least one customer attribute information of the described user obtained adds in the user model data of described user.
Further, the user model data of described user comprise identification information and the customer attribute information described at least one of user.
Alternatively, described system also comprises: module chosen by model, extracts keyword, according to the Keyword Selection extracted for identifying the described disaggregated model of user property for the network behavior data from described user.
Further, described customer attribute information comprises at least one in the middle of age, sex, region, interest, affiliated industry.
What embodiments of the invention additionally provided a kind of user data provides system, comprising: data acquisition module, for obtaining the network behavior data of user; User ID acquisition module, for obtaining second user ID of described user from user ID mapping table according to the first user mark of described user, wherein, described user ID mapping table associated record has multiple user ID of user; User model data acquisition module, for obtaining the user model data of described user according to described second user ID, described user model data comprise at least one customer attribute information of described user.
Alternatively, described user ID acquisition module also for obtaining the first user mark of described user from described network behavior data.
Further, multiple user ID of described user ID mapping table associated record comprise less than at least two marks: the register account number of at least one user, MAC Address, called subscriber's identifier CUID and international mobile equipment identification number IMEI.
The embodiment of the present invention provide based on the user data process of user network behavior, the supplying method of user data and system, utilize the disaggregated model of identifiable design user property to identify user network behavioral data, obtain the user model data comprising at least one customer attribute information; By preset user ID mapping table, user ID in the network behavior data of the new user obtained is identified, find the user ID with this user ID with mapping relations, to obtain the user model data corresponding with the user ID of this mapping further.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the user data disposal route embodiment based on user network behavior provided by the invention;
Fig. 2 is the method flow diagram of a supplying method embodiment of user data provided by the invention;
Fig. 3 is the structural representation of the user data disposal system embodiment based on user network behavior provided by the invention;
Fig. 4 is the structural representation of another embodiment of user data disposal system based on user network behavior provided by the invention;
The structural representation that a system embodiment is provided that Fig. 5 is user data provided by the invention.
Embodiment
Basic inventive concept of the present invention is, is first identified by the user property that the disaggregated model trained is corresponding to the network behavior data of user, obtains the user model data that comprise at least one customer attribute information corresponding with this user; Then utilize the user ID mapping table of these user model data and multiple user ID corresponding to the same user that establishes in advance, in the whole network system, the customer attribute information of the network behavior data of the new user obtained is identified.
Embodiment one
Fig. 1 is the method flow diagram of the user data disposal route embodiment based on user network behavior provided by the invention, and the executive agent of the method can for having the server of data processing function.
With reference to Fig. 1, in step S110, obtain the network behavior data of user.
The network behavior data of described user for user is using the related data produced in the behavior of internet operation, particularly, can comprise the data such as daily record, cookies.Such as, user search " association ", then this keyword can as network behavior data.Similar, the web site url that user clicks after Baidu's search, and the ad click on the website of Baidu's cooperation, can as the network behavior data of user.These network behavior data produce along with the network behavior of user, and are preserved by web browser or the webserver.
In step S120, respectively by least one for identifying that the network behavior data of the disaggregated model of user property to user identify, obtain at least one customer attribute information of user.
Described disaggregated model is the model for identifying customer attribute information pre-set.Wherein, customer attribute information can comprise the information of at least one comprised in the middle of age, sex, region, interest, affiliated industry of user.And for the customer attribute information of user under different dimensions, different disaggregated models can be adopted to identify respectively.Often kind of disaggregated model all can comprise the keyword identifying relative users attribute information, and utilize these keywords to mate with the network behavior data of user, then identifiable design goes out the most possible user property of this user.
Such as, for the identification of the customer attribute information under " sex " dimension, the keyword adopted in the disaggregated model being identified as women can comprise especially for the keyword of the special behavior of women, as " cosmetics ", " beauty treatment ", " mother and baby " etc.; Similar, for the identification of the customer attribute information under " region " dimension, this province/incity major part geographic name can be included in being identified as the keyword adopted in the disaggregated model in certain province/city.The rest may be inferred, can be obtained at least one customer attribute information of user by the keyword under at least one disaggregated model.
In step S130, will identify that at least one customer attribute information of the user obtained adds in the user model data of user.
After getting at least one customer attribute information of user, corresponding customer attribute information can be added in the corresponding property location in the user model data of this user.Intelligible, after a large amount of network behavior data of same user are identified, just can obtain the customer attribute information of various dimensions corresponding to this user.Table 1 shows the user model data obtained by method described in the present embodiment.
Table 1 user model data
| User ID | ID type | Age | Sex | Region | Interest | Industry | Other |
| 123456 | Baidu | Less than 18 | Man | Beijing | IT electronics | Internet | … |
| abcd | Baidu | 18-25 | Female | Guangdong | Animation | Medical treatment | … |
| my123 | Baidu | 18-25 | Man | Shanghai | Photography | Sell | … |
| … | … | … | … | … | … | … | … |
As shown in table 1, can comprise in user model data: the identification information of user, as user ID and ID type, and at least one above-mentioned customer attribute information.
Alternatively, in step 120, respectively by least one for identifying that the network behavior data of the disaggregated model of user property to user identify, before obtaining at least one customer attribute information of user, can also according to the network behavior data selection of the user obtained for identifying the disaggregated model of user property.As the network behavior data from user extract keyword, according to the Keyword Selection extracted for identifying the disaggregated model of user property.
Particularly, due to the network behavior data property of there are differences of user obtained at every turn, some data data that are longer, that have are shorter, then how many numbers of corresponding extractible keyword differs, thus cause the number correspondence of adopted disaggregated model also the amount doesn't matter; Or, network behavior data for the user obtained also tentatively can be judged its Data Source by the information extracting keyword wherein, be contained field or summary content, and the characteristic information that so can reflect for these keywords selects the disaggregated model of coupling; Certainly, when also can identify for specifying the user property identified, the specific disaggregated model of these customer attribute informations of identifiable design can be chosen.
Such as, when the network behavior data of user obtained are extracted in the keyword obtained comprise as the keyword such as " cosmetics ", " beauty treatment ", " mother and baby " time, the disaggregated model of identification " sex " attribute can be chosen; Similar, when extracting the keyword comprising geography information, can selective recognition " region " attributive classification model.
According to above several embody rule scene, according to the network behavior data of user, such as can select appropriate disaggregated model from the information of the keyword wherein extracted, thus the customer attribute information of the network behavior data obtaining user is carried out fast and identified targetedly.
The user data disposal route based on user network behavior that the embodiment of the present invention provides, by at least one for identifying that the network behavior data of the disaggregated model of user property to user identify, obtain at least one customer attribute information of user, thus formed at least one user's and comprise the user model data of at least one customer attribute information.Utilize these user model data can carry out further audience analysis to the user carrying out network behavior.
Embodiment two
Fig. 2 is the method flow diagram of a supplying method embodiment of user data provided by the invention, and the executive agent of the method can for having the server of data processing function.Method design embodiment illustrated in fig. 2, be utilize embodiment illustrated in fig. 1 in user model data, by the user ID mapping table comprising multiple user ID of the interrelated record of at least one user pre-set, from user model data, directly obtain the customer attribute information for the network behavior data of user.
With reference to Fig. 2, in step S210, obtain the network behavior data of user.The step content of step S210 is similar to the step content of abovementioned steps S110.
Particularly, the network behavior data of the user that this step obtains can be the network behavior data that user produces arbitrarily in the whole network system, as the network behavior data that Baidu's mhkc, microblogging, forum produce.
In step S220, from user ID mapping table, obtain second user ID of user according to the first user mark of user, wherein, user ID mapping table associated record has multiple user ID of user.
Described user ID can be the mark with identity attribute that user is marked when carrying out network behavior, such as in account, microblog account corresponding in microblogging that Baidu's mhkc is corresponding, or in forum, deliver the publisher's account etc. corresponding to forum.User ID in similar aforementioned table 1 is also a kind of concrete form of user ID.So learn, each user corresponding can comprise multiple multi-form user ID according to carried out network behavior.Induction-arrangement is carried out to multiple user ID of each user, the user ID mapping table shown in similar table 2 can be obtained.As shown in table 2, the user ID corresponding to each user can comprise the register account number of user at different web sites, as Baidu ID, microblogging ID; The MAC Address (not marking in table) of the terminal used when carrying out network behavior, mobile terminal are as called subscriber's identifier CUID of mobile phone and international mobile equipment identification number IMEI etc.By the information such as daily record or cookies of user's generation when carrying out network behavior, these information can be obtained, and the incidence relation between information, thus obtain user ID mapping table as shown in table 2.
Table 2 user ID mapping table
Described first user mark can be user ID corresponding to the network behavior data of the user obtained in step S210 in embodiment.The second described user ID, in the user ID mapping table shown in table 2, can identify other user ID of the same user of indication with described first user.
By the first user mark corresponding to the network behavior data that obtain user, other second user ID that just can obtain corresponding to this user by question blank 2.
Alternatively, the first user mark obtaining user corresponding to above-mentioned network behavior data can obtain from the user data that system has recorded, or obtain after passing through to identify these network behavior data.At this, obtain manner that first user identifies is not limited.
In step S230, obtain the user model data of user according to the second user ID, described user model data comprise at least one customer attribute information of user.
In the user ID mapping table shown in above-mentioned table 2, comprise the user ID comprised in aforementioned user model data.This user ID can for identify identical mark with first user in user ID mapping table, also can be the second user ID, no matter and be which kind of mark, can according to the user ID of any one form, and the incidence relation of any one user ID and this mark obtains and its respective user model data.The customer attribute information of these user model data then also user corresponding to applicable sign any one user ID above-mentioned.
Therefore, utilize above-mentioned user ID mapping table can project to the user ID of another kind of form from a kind of user ID of form by least one customer attribute information of same user, thus eliminate the network behavior data repeated by user, formed without the user model data under the user ID of form, accelerate the process that the customer attribute information of relative users is provided according to the network behavior data of user, improve the identification efficiency of customer attribute information.
The supplying method of the user data that the embodiment of the present invention provides, according to the user model data that above-described embodiment produces, and the associated record built in advance has the user ID mapping table of multiple user ID of user, the user ID corresponding to the network behavior data of the user obtained identifies, and relationship maps is carried out to this user ID, obtain the customer attribute information of relative users, thus Quick supplies the customer attribute information for the user of heterogeneous networks behavioral data in the whole network system.
Embodiment three
Fig. 3 is the structural representation of the user data disposal system embodiment based on user network behavior provided by the invention.System shown in Figure 3 can be used for performing the method step of embodiment as shown in Figure 1.
With reference to Fig. 3, should specifically comprise the first data acquisition module 310, data identification module 320 and information based on the user data disposal system of user network behavior and add module 330.
First data acquisition module 310 is for obtaining the network behavior data of user; Data identification module 320 for respectively by least one for identifying that the network behavior data of the disaggregated model of user property to user identify, obtain at least one customer attribute information of user; Information adds module 330 for identifying that at least one customer attribute information of the user obtained adds in the user model data of user.
Further, the user model data of above-mentioned user can comprise identification information and at least one customer attribute information of user.
Alternatively, as shown in Figure 4, the above-mentioned user data disposal system based on user network behavior also can comprise: model is chosen module 340 and extracted keyword, according to the Keyword Selection extracted for identifying the disaggregated model of user property for the network behavior data from user.
Further, above-mentioned customer attribute information can comprise at least one in the middle of age, sex, region, interest, affiliated industry.
The user data disposal system based on user network behavior that the embodiment of the present invention provides, by at least one for identifying that the network behavior data of the disaggregated model of user property to user identify, obtain at least one customer attribute information of user, thus formed at least one user's and comprise the user model data of at least one customer attribute information.Utilize these user model data can carry out further audience analysis to the user carrying out network behavior.
Embodiment four
The structural representation providing a system embodiment that Fig. 5 is user data provided by the invention, system shown in Figure 5 can be used for performing the method step of embodiment as shown in Figure 2.
With reference to Fig. 5, the system that provides of this user data comprises the second data acquisition module 510, user ID acquisition module 520 and user model data acquisition module 530.
Second data acquisition module 510 is for obtaining the network behavior data of user; User ID acquisition module 520, for obtaining second user ID of user from user ID mapping table according to the first user mark of user, wherein, user ID mapping table associated record has multiple user ID of user; User model data acquisition module 530 is for obtaining the user model data of user according to the second user ID, described user model data comprise at least one customer attribute information of user.
Alternatively, above-mentioned user ID acquisition module 520 also can be used for the first user mark obtaining user from network behavior data.
Further, multiple user ID of above-mentioned user ID mapping table associated record comprise less than at least two marks: the register account number of at least one user, MAC Address, called subscriber's identifier CUID and international mobile equipment identification number IMEI.
The user data that the embodiment of the present invention provides system is provided, according to the user model data that above-described embodiment produces, and the associated record built in advance has the user ID mapping table of multiple user ID of user, the user ID corresponding to the network behavior data of the user obtained identifies, and relationship maps is carried out to this user ID, obtain the customer attribute information of relative users, thus Quick supplies the customer attribute information for the user of heterogeneous networks behavioral data in the whole network system.
Above-mentioned can at hardware according to method and apparatus of the present invention, realize in firmware, or be implemented as and can be stored in recording medium (such as CD ROM, RAM, floppy disk, hard disk or magneto-optic disk) in software or computer code, or be implemented and will be stored in the computer code in local recording medium by the original storage of web download in remote logging medium or nonvolatile machine readable media, thus method described here can be stored in use multi-purpose computer, such software process on the recording medium of application specific processor or able to programme or specialized hardware (such as ASIC or FPGA).Be appreciated that, computing machine, processor, microprocessor controller or programmable hardware comprise and can store or receive the memory module of software or computer code (such as, RAM, ROM, flash memory etc.), when described software or computer code by computing machine, processor or hardware access and perform time, realize disposal route described here.In addition, when the code for realizing the process shown in this accessed by multi-purpose computer, multi-purpose computer is converted to the special purpose computer for performing the process shown in this by the execution of code.
The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; change can be expected easily or replace, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of described claim.
Claims (14)
1., based on a user data disposal route for user network behavior, it is characterized in that, comprising:
Obtain the network behavior data of user;
Respectively by least one for identifying that the network behavior data of the disaggregated model of user property to described user identify, obtain at least one customer attribute information of described user;
To identify that at least one customer attribute information of the described user obtained adds in the user model data of described user.
2. method according to claim 1, is characterized in that, the user model data of described user comprise identification information and the customer attribute information described at least one of user.
3. method according to claim 2, is characterized in that, described method also comprises:
Keyword is extracted, according to the Keyword Selection extracted for identifying the described disaggregated model of user property from the network behavior data of described user.
4. the method according to any one of claims 1 to 3, is characterized in that, described customer attribute information comprises at least one in the middle of age, sex, region, interest, affiliated industry.
5. a supplying method for user data, is characterized in that, comprising:
Obtain the network behavior data of user;
From user ID mapping table, obtain second user ID of described user according to the first user mark of described user, wherein, described user ID mapping table associated record has multiple user ID of user;
Obtain the user model data of described user according to described second user ID, described user model data comprise at least one customer attribute information of described user.
6. method according to claim 5, is characterized in that, described method also comprises:
The first user mark of described user is obtained from described network behavior data.
7. method according to claim 6, it is characterized in that, multiple user ID of described user ID mapping table associated record comprise less than at least two marks: the register account number of at least one user, MAC Address, called subscriber's identifier CUID and international mobile equipment identification number IMEI.
8., based on a user data disposal system for user network behavior, it is characterized in that, comprising:
Data acquisition module, for obtaining the network behavior data of user;
Data identification module, for respectively by least one for identifying that the network behavior data of the disaggregated model of user property to described user identify, obtain at least one customer attribute information of described user;
Information adds module, for identifying that at least one customer attribute information of the described user obtained adds in the user model data of described user.
9. system according to claim 8, is characterized in that, the user model data of described user comprise identification information and the customer attribute information described at least one of user.
10. system according to claim 9, is characterized in that, described system also comprises:
Module chosen by model, extracts keyword for the network behavior data from described user, according to
The Keyword Selection extracted is for identifying the described disaggregated model of user property.
System according to any one of 11. according to Claim 8 ~ 10, is characterized in that, described customer attribute information comprises at least one in the middle of age, sex, region, interest, affiliated industry.
12. 1 kinds of user data system is provided, it is characterized in that, comprising:
Data acquisition module, for obtaining the network behavior data of user;
User ID acquisition module, for obtaining second user ID of described user from user ID mapping table according to the first user mark of described user, wherein, described user ID mapping table associated record has multiple user ID of user;
User model data acquisition module, for obtaining the user model data of described user according to described second user ID, described user model data comprise at least one customer attribute information of described user.
13. systems according to claim 12, is characterized in that, described user ID acquisition module also for obtaining the first user mark of described user from described network behavior data.
14. systems according to claim 13, it is characterized in that, multiple user ID of described user ID mapping table associated record comprise less than at least two marks: the register account number of at least one user, MAC Address, called subscriber's identifier CUID and international mobile equipment identification number IMEI.
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| CN201510347820.2A CN104951544A (en) | 2015-06-19 | 2015-06-19 | User data processing method and system and method and system for providing user data |
| PCT/CN2015/097772 WO2016201933A1 (en) | 2015-06-19 | 2015-12-17 | User data processing method, providing method, system and computer device |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105553999A (en) * | 2015-12-23 | 2016-05-04 | 北京奇虎科技有限公司 | Application program user behavior analysis and security control method and corresponding device |
| WO2016201933A1 (en) * | 2015-06-19 | 2016-12-22 | 百度在线网络技术（北京）有限公司 | User data processing method, providing method, system and computer device |
| CN106488493A (en) * | 2015-08-24 | 2017-03-08 | 阿里巴巴集团控股有限公司 | The method and apparatus of the network hotspot type of identifying user and electronic equipment |
| CN106897729A (en) * | 2016-06-28 | 2017-06-27 | 阿里巴巴集团控股有限公司 | Information identifying method, model training method, device and processing equipment |
| CN106998262A (en) * | 2016-10-10 | 2017-08-01 | 深圳汇网天下科技有限公司 | A kind of System and method for for recognizing Internet user |
| CN107682344A (en) * | 2017-10-18 | 2018-02-09 | 南京邮数通信息科技有限公司 | A kind of ID collection of illustrative plates method for building up based on DPI data interconnection net identifications |
| CN107786389A (en) * | 2017-10-16 | 2018-03-09 | 上海理工大学 | A kind of spreading network information device and method thereof |
| CN107818472A (en) * | 2016-09-13 | 2018-03-20 | 腾讯科技（北京）有限公司 | A kind of information processing method and server |
| CN108038714A (en) * | 2017-11-29 | 2018-05-15 | 链家网（北京）科技有限公司 | Advertisement promotion processing method and processing device |
| US20210035126A1 (en) * | 2015-02-13 | 2021-02-04 | Baidu Online Network Technology (Beijing) Co., Ltd. | Data processing method, system and computer device based on electronic payment behaviors |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111353001B (en) * | 2018-12-24 | 2023-08-18 | 杭州海康威视数字技术股份有限公司 | Method and device for classifying users |
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| CN111242171B (en) * | 2019-12-31 | 2023-10-31 | 中移（杭州）信息技术有限公司 | Model training and diagnosis prediction method and device for network faults and electronic equipment |
| CN111340112B (en) * | 2020-02-26 | 2023-09-26 | 腾讯科技（深圳）有限公司 | Classification method, classification device and classification server |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957834A (en) * | 2010-08-12 | 2011-01-26 | 百度在线网络技术（北京）有限公司 | Content recommending method and device based on user characteristics |
| CN102368788A (en) * | 2011-12-09 | 2012-03-07 | 中国电信股份有限公司 | Information pushing method and apparatus thereof |
| CN103530540A (en) * | 2013-09-27 | 2014-01-22 | 西安交通大学 | User identity attribute detection method based on man-machine interaction behavior characteristics |
| CN103778555A (en) * | 2014-01-21 | 2014-05-07 | 北京集奥聚合科技有限公司 | User attribute mining method and system based on user tags |
| CN104090888A (en) * | 2013-12-10 | 2014-10-08 | 深圳市腾讯计算机系统有限公司 | Method and device for analyzing user behavior data |
| CN104298719A (en) * | 2014-09-23 | 2015-01-21 | 新浪网技术(中国)有限公司 | Method and system for conducting user category classification and advertisement putting based on social behavior |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103731284B (en) * | 2012-10-11 | 2016-12-21 | 腾讯科技（深圳）有限公司 | Associate the method and system of multiple network account |
| JP6445517B2 (en) * | 2013-03-13 | 2018-12-26 | グーグル エルエルシー | Improved user experience for unrecognized and new users |
| CN104951544A (en) * | 2015-06-19 | 2015-09-30 | 百度在线网络技术（北京）有限公司 | User data processing method and system and method and system for providing user data |
-
2015
- 2015-06-19 CN CN201510347820.2A patent/CN104951544A/en active Pending
- 2015-12-17 WO PCT/CN2015/097772 patent/WO2016201933A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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