CN103761296A - Method and system for analyzing network behaviors of mobile terminal users - Google Patents
Method and system for analyzing network behaviors of mobile terminal users Download PDFInfo
- Publication number
- CN103761296A CN103761296A CN201410024921.1A CN201410024921A CN103761296A CN 103761296 A CN103761296 A CN 103761296A CN 201410024921 A CN201410024921 A CN 201410024921A CN 103761296 A CN103761296 A CN 103761296A
- Authority
- CN
- China
- Prior art keywords
- data
- user
- mobile internet
- algorithm
- mobile
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
- G06F16/337—Profile generation, learning or modification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Telephonic Communication Services (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention relates to the technical field of mobile internet, and discloses a method and a system for analyzing network behaviors of mobile terminals users. The method includes collecting access logs and signaling log data of the users to the mobile internet through a collection device, cleaning the collected data to form an effective data set, classifying and analyzing the data set through a matched mobile internet knowledge base, analyzing users' behaviors by loading a user behavior analysis algorithm to acquire analysis results of users' behaviors, and finally displaying the analysis results to the users. According to the collected network requests sent from mobile terminals and the signaling data, the users can be recognized accurately; by means of building the mobile internet knowledge base, users' behavior preferences can be found accurately; therefore, problems such as high data noises, small data sample and inaccurate prediction in the prior art are solved.
Description
Technical Field
The invention relates to the technical field of mobile internet, in particular to the field of analysis of network behavior data of mobile terminal users.
Background
The user network behavior analysis means that under the condition of obtaining the relevant data of the network operation behavior of the user, the relevant data is subjected to statistical analysis, so that the group composition and the respective preference of the network user are judged and found. Thereby providing basis for subsequent related operations.
The existing user network behavior analysis system and method generally comprise an information acquisition module, an information storage module, an information mining and counting module and a system display module. The information acquisition module is used for acquiring user network behavior data and summarizing and uploading the acquired data to the information storage module; the information storage module is used for storing the uploaded data acquired by the information acquisition module, summarizing the data and outputting the summarized data to an original database; the information mining statistical module is used for regularly extracting data from the original database, performing statistics, mining and analysis, specifically comprising ranking statistics, user behavior classification, user clustering and the like, and outputting an analysis result to the statistical database; and the system display output module is used for acquiring data from the statistical database and displaying the result of the network behavior analysis of the user.
The existing user network behavior analysis scheme has the following three defects:
1. data source. The existing solutions analyze the user's behavior based on the network request of the fixed network. The fixed network environment is that network requests on a computer are used for analyzing user behaviors, and the network requests sent out are complicated and have more data noises due to a plurality of software on the computer.
2. And (4) data analysis. Most of data adopted by the existing user behavior analysis system is data of partial media or data of small samples, so that the behavior of a user cannot be accurately judged.
3. Limited aspects of the device. Under the condition that a user does not use a computer and only has a mobile terminal such as a mobile phone, the prior art has no method for analyzing the network behavior of the user.
Disclosure of Invention
The application provides a method and a system for analyzing network behaviors of a mobile terminal user. The method comprises the steps that accurate user identification is carried out by collecting data of a user mobile internet, namely, a mobile terminal such as a mobile phone of the user or an iPad sends out a network request and combining mobile signaling data; by analyzing and mining the network request, monitoring applications do not need to be installed on the mobile terminal of the user, and the information is encrypted, so that the privacy of the user can be better protected; by aiming at the characteristics of a network request sent by a mobile application installed on a mobile terminal, a knowledge base of the mobile internet is established, so that the intention of a user can be judged more accurately, and the behavior preference of the user can be found. Thereby solving the problems in the prior art described above.
According to one aspect of the invention, a method for analyzing network behavior of a mobile terminal user is provided. The method comprises the following steps: the method comprises the steps of data acquisition, data preprocessing, user analysis and mining and data display; the method comprises the steps of data acquisition, wherein data of the mobile internet of a user are acquired through acquisition equipment; the data of the user mobile internet comprises one or more of access log data and signaling log data of the mobile internet; a data preprocessing step, namely performing data cleaning and washing on the acquired data to form an effective data set for user behavior analysis, storing the data set and outputting the data set to a user analysis mining module; a user analysis and mining step, namely classifying, sorting and analyzing the cleaned data set by matching with a mobile internet knowledge base, loading an algorithm in a user behavior analysis algorithm base, and analyzing the behavior of the user to obtain a behavior analysis result of the user; the mobile internet knowledge base is a knowledge base for classifying network requests sent by mobile applications, and can accurately identify the type of each mobile network request; and a data display step, displaying the obtained user behavior analysis result to the user.
According to another aspect of the invention, a mobile terminal user network behavior analysis system is provided. The system comprises: the system comprises a data acquisition module, a data preprocessing block, a user analysis mining module and a data display module; the data acquisition module is used for acquiring data of the mobile internet of the user through acquisition equipment; the data of the user mobile internet comprises one or more of access log data and signaling log data of the mobile internet; the data preprocessing module is used for performing data cleaning and washing on the acquired data to form an effective data set for user behavior analysis, storing the data set and outputting the data set to the user analysis mining module; the user analysis and mining module comprises a mobile internet knowledge base and a user behavior analysis algorithm base, and is used for classifying, sorting and analyzing the cleaned data set by matching the mobile internet knowledge base, loading the algorithm in the user behavior analysis algorithm base, and analyzing the behavior of the user to obtain a behavior analysis result of the user; the mobile internet knowledge base is a knowledge base for classifying network requests sent by mobile applications, and can accurately identify the type of each mobile network request; and the data display module is used for displaying the obtained user behavior analysis result to the user.
Compared with the prior art, the method has the advantages that the user behavior is analyzed by combining the data of the mobile application used by the user on the mobile terminal, and the method is more accurate and perfect than the method only suitable for analyzing the access log of the browser. The invention arranges the network requests sent by the mobile terminal application to form a mobile network knowledge base. Meanwhile, the invention supports the analysis and mining of mass user data. The method has wide application, is not limited to information recommendation and advertisement publishing, and can be applied to various fields.
Drawings
FIG. 1 is a schematic diagram of a method for analyzing network behavior of a mobile terminal user according to the present invention;
FIG. 2 is a schematic diagram of a network behavior analysis system for a mobile terminal user according to the present invention.
Detailed Description
The technical problems to be solved by the present invention, the technical means to be adopted by the present invention, and the technical effects to be achieved by the present invention will be fully described below by describing embodiments of the present invention in detail with reference to the accompanying drawings and examples. It should be noted that the embodiments and features of the embodiments may be combined with each other, and the technical solutions are within the scope of the present invention, as long as they do not constitute a conflict.
Example 1
Fig. 1 is a schematic diagram of a method for analyzing network behavior of a mobile terminal user according to the present invention, and the following describes the steps of the method in detail with reference to fig. 1.
The user network behavior analysis method comprises the following steps: the method comprises the steps of data acquisition, data preprocessing, user analysis and mining and data display; wherein,
a data acquisition step, wherein data of the mobile internet of the user is acquired through acquisition equipment; the data of the user mobile internet comprises one or more of access log data and signaling log data of the mobile internet;
a data preprocessing step, namely performing data cleaning and washing on the acquired data to form an effective data set for user behavior analysis, storing the data set and outputting the data set to a user analysis mining module;
a user analysis and mining step, namely classifying, sorting and analyzing the cleaned data set by matching with a mobile internet knowledge base, loading an algorithm in a user behavior analysis algorithm base, and analyzing the behavior of the user to obtain a behavior analysis result of the user; the mobile internet knowledge base is a knowledge base for classifying network requests sent by mobile applications, and can accurately identify the type of each mobile network request;
and a data display step, displaying the obtained user behavior analysis result to the user.
Preferably, in the data acquisition step, data acquisition is completed by a proprietary data acquisition device linking with an acquirer developed by a program, and the acquirer is responsible for analyzing and storing the access log and the signaling log data.
Preferably, the data preprocessing step associates the access log data with the signaling log data, performs user identification, identifies a user _ id of the user, and filters a garbage log, such as some picture logs and logs unrelated to user behavior.
Preferably, the mobile internet knowledge base is obtained through web crawlers and manual arrangement. The mobile internet knowledge base comprises network request data of mobile applications which are mainstream in the market, and is gradually increased and improved.
Preferably, the algorithm in the user behavior analysis algorithm library includes one or more of an SVM algorithm and a clustering algorithm.
Preferably, the obtaining of the behavior analysis result of the user refers to generating corresponding user behavior tags, including a user attribute tag, an interest tag, a product tag, a usage habit tag, and the like.
Preferably, the analyzing the user behavior in the user analysis mining module includes analyzing one or more of user attributes, user behavior preferences, user product preferences, and user usage habits. Wherein the user attributes include one or more of gender, age group, presence or absence of children, income level; the user interest preferences include one or more of sports, leisure, culture; the product preference of the user comprises one or more items of clothing, jewelry, mobile phone numbers and household appliances; the using habits of the user comprise one or more of the internet surfing time and the internet surfing time period.
Example 2
Fig. 2 is a schematic diagram of the network behavior analysis system of the mobile terminal user according to the present invention, and the following describes the components of the system in detail with reference to fig. 2.
The mobile terminal user network behavior analysis system comprises a data acquisition module, a data preprocessing block, a user analysis mining module and a data display module; wherein,
the data acquisition module is used for acquiring data of the mobile internet of the user through acquisition equipment; the data of the user mobile internet comprises one or more of access log data and signaling log data of the mobile internet;
the data preprocessing module is used for performing data cleaning and washing on the acquired data to form an effective data set for user behavior analysis, storing the data set and outputting the data set to the user analysis mining module;
the user analysis and mining module comprises a mobile internet knowledge base and a user behavior analysis algorithm base, and is used for classifying, sorting and analyzing the cleaned data set by matching the mobile internet knowledge base, loading the algorithm in the user behavior analysis algorithm base, and analyzing the behavior of the user to obtain a behavior analysis result of the user; the mobile internet knowledge base is a knowledge base for classifying network requests sent by mobile applications, and can accurately identify the type of each mobile network request;
and the data display module is used for displaying the obtained user behavior analysis result to the user.
Preferably, the data acquisition module comprises a collector, the collector is responsible for analyzing and storing the access log and the signaling log data, and the data acquisition module completes data acquisition by a proprietary data acquisition device linked with the collector developed by a program.
Preferably, the data preprocessing module associates the access log data with the signaling log data, performs user identification, identifies a user _ id of the user, and filters a garbage log, such as some picture logs and logs unrelated to user behavior.
Preferably, the mobile internet knowledge base is obtained through web crawlers and manual arrangement. The mobile internet knowledge base comprises network request data of mobile applications which are mainstream in the market, and is gradually increased and improved.
Preferably, the algorithm in the user behavior analysis algorithm library includes one or more of an SVM algorithm and a clustering algorithm.
Preferably, the obtaining of the behavior analysis result of the user refers to generating corresponding user behavior tags, including a user attribute tag, an interest tag, a product tag, a usage habit tag, and the like.
Preferably, the analyzing the user behavior in the user analysis mining module includes analyzing one or more of user attributes, user behavior preferences, user product preferences, and user usage habits. Wherein the user attributes include one or more of gender, age group, presence or absence of children, income level; the user interest preferences include one or more of sports, leisure, culture; the product preference of the user comprises one or more items of clothing, jewelry, mobile phone numbers and household appliances; the using habits of the user comprise one or more of the internet surfing time and the internet surfing time period. Other dimensions can be derived on the basis of the basic dimensions, and more detailed user insights are provided.
Example 3
The mobile terminal-based user behavior analysis can be used for accurately recommending the mobile advertisement. The method comprises the following specific steps: an advertiser of maternal and infant products wants to advertise maternal and infant products to mothers on a mobile application. At this time, through log analysis of a mobile terminal of a user, it is found that a message sent by a mobile phone terminal of the user calls an API under a domain name' bbpapp. Meanwhile, the user also often accesses api.m.taobao.com, which indicates that the user often calls the API of the Taobao network, installs a mobile application of the Taobao network on a mobile phone, and can detect that the user often searches keywords such as 'toy', 'child raising' and the like in the Taobao network, and has a transaction record, which indicates that the user is a strongly related user of a mother and infant product. Therefore, when the user accesses the internet by using the mobile application, the advertiser can put mobile advertisements of mother-infant products on the user. This can achieve better desired results.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A user network behavior analysis method is characterized in that: the method comprises the following steps: the method comprises the steps of data acquisition, data preprocessing, user analysis and mining and data display; wherein,
a data acquisition step, wherein data of the mobile internet of the user is acquired through acquisition equipment; the data of the user mobile internet comprises one or more of access log data and signaling log data of the mobile internet;
a data preprocessing step, namely performing data cleaning and washing on the acquired data to form an effective data set for user behavior analysis, storing the data set and outputting the data set to a user analysis mining module;
a user analysis and mining step, namely classifying, sorting and analyzing the cleaned data set by matching with a mobile internet knowledge base, loading an algorithm in a user behavior analysis algorithm base, and analyzing the behavior of the user to obtain a behavior analysis result of the user; the mobile internet knowledge base is a knowledge base for classifying network requests sent by mobile applications, and can accurately identify the type of each mobile network request;
and a data display step, displaying the obtained user behavior analysis result to the user.
2. The assay of claim 1, wherein: in the data acquisition step, data acquisition is completed by a proprietary data acquisition device linking with an acquisition unit developed by a program, and the acquisition unit is responsible for analyzing and storing the access log and the signaling log data.
3. The assay of claim 1, wherein: and the data preprocessing step corresponds the access log data and the signaling log data, identifies the user _ id of the user and filters the junk log.
4. The assay of claim 1, wherein: the mobile internet knowledge base is obtained through web crawlers and manual sorting.
5. The assay of claim 1, wherein: the algorithm in the user behavior analysis algorithm library comprises one or more of SVM algorithm and clustering algorithm.
6. A mobile terminal user network behavior analysis system is characterized in that: the system comprises: the system comprises a data acquisition module, a data preprocessing block, a user analysis mining module and a data display module; wherein,
the data acquisition module is used for acquiring data of the mobile internet of the user through acquisition equipment; the data of the user mobile internet comprises one or more of access log data and signaling log data of the mobile internet;
the data preprocessing module is used for performing data cleaning and washing on the acquired data to form an effective data set for user behavior analysis, storing the data set and outputting the data set to the user analysis mining module;
the user analysis and mining module comprises a mobile internet knowledge base and a user behavior analysis algorithm base, and is used for classifying, sorting and analyzing the cleaned data set by matching the mobile internet knowledge base, loading the algorithm in the user behavior analysis algorithm base, and analyzing the behavior of the user to obtain a behavior analysis result of the user; the mobile internet knowledge base is a knowledge base for classifying network requests sent by mobile applications, and can accurately identify the type of each mobile network request;
and the data display module is used for displaying the obtained user behavior analysis result to the user.
7. The analytical system of claim 6, wherein: the data acquisition module comprises an acquisition unit which is responsible for analyzing and storing the access log and the signaling log data, and the data acquisition module finishes data acquisition by linking a proprietary data acquisition device with the acquisition unit developed by a program.
8. The analytical system of claim 6, wherein: and the data preprocessing module corresponds the access log data and the signaling log data, identifies the user _ id of the user and filters the junk log.
9. The analytical system of claim 6, wherein: the mobile internet knowledge base is obtained through web crawlers and manual sorting.
10. The analytical system of claim 6, wherein: the algorithm in the user behavior analysis algorithm library comprises one or more of SVM algorithm and clustering algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410024921.1A CN103761296A (en) | 2014-01-20 | 2014-01-20 | Method and system for analyzing network behaviors of mobile terminal users |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410024921.1A CN103761296A (en) | 2014-01-20 | 2014-01-20 | Method and system for analyzing network behaviors of mobile terminal users |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103761296A true CN103761296A (en) | 2014-04-30 |
Family
ID=50528533
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410024921.1A Pending CN103761296A (en) | 2014-01-20 | 2014-01-20 | Method and system for analyzing network behaviors of mobile terminal users |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103761296A (en) |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104298751A (en) * | 2014-10-14 | 2015-01-21 | 李小宜 | Method and device for obtaining internet virtual identity characteristic information |
| CN104683122A (en) * | 2015-02-12 | 2015-06-03 | 北京集奥聚合科技有限公司 | Multi-screen-linkage-based information transmitting method and system |
| CN104778273A (en) * | 2015-04-24 | 2015-07-15 | 淘金信息科技江苏有限公司 | Big data analysis system for shopping website |
| WO2016054988A1 (en) * | 2014-10-09 | 2016-04-14 | 阿里巴巴集团控股有限公司 | Method and apparatus for identifying intelligent device users |
| CN105590240A (en) * | 2015-12-30 | 2016-05-18 | 合一网络技术(北京)有限公司 | Discrete calculating method of brand advertisement effect optimization |
| CN105608604A (en) * | 2015-12-30 | 2016-05-25 | 合一网络技术(北京)有限公司 | Continuous calculation method of brand advertisement effectiveness optimization |
| CN105630658A (en) * | 2015-12-22 | 2016-06-01 | 北京奇虎科技有限公司 | Data processing method and data processing device |
| CN106022349A (en) * | 2015-03-27 | 2016-10-12 | 国际商业机器公司 | Method and system for determining device types |
| CN106446228A (en) * | 2016-10-08 | 2017-02-22 | 中国工商银行股份有限公司 | Collection analysis method and device for WEB page data |
| CN106503015A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of method for building user's portrait |
| CN106504099A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of system for building user's portrait |
| CN106649069A (en) * | 2016-12-28 | 2017-05-10 | Tcl集团股份有限公司 | User behavior statistical method and system |
| CN107609040A (en) * | 2017-08-16 | 2018-01-19 | 深信服科技股份有限公司 | Data analysing method, data processing equipment and computer-readable recording medium |
| CN107704494A (en) * | 2017-08-24 | 2018-02-16 | 上海斐讯数据通信技术有限公司 | A kind of user information collection method and system based on application software |
| CN108337290A (en) * | 2017-12-26 | 2018-07-27 | 努比亚技术有限公司 | A kind of method and apparatus of enterprise staff user's behaviors analysis |
| CN108549685A (en) * | 2018-04-08 | 2018-09-18 | 武志学 | Behavior analysis method, device, system and readable storage medium storing program for executing |
| CN109348542A (en) * | 2018-09-17 | 2019-02-15 | 深圳市三体科技有限公司 | A kind of data transmission method based on big data, storage medium and mobile terminal |
| CN109350969A (en) * | 2018-10-22 | 2019-02-19 | 镇江悦乐网络科技有限公司 | Knowledge based map game data intelligent analysis method |
| CN109496302A (en) * | 2018-05-31 | 2019-03-19 | 优视科技新加坡有限公司 | A kind of user's characteristic information collection method, device and equipment/terminal/server |
| CN109670112A (en) * | 2018-12-20 | 2019-04-23 | 北京城市网邻信息技术有限公司 | A kind of generation method, device, storage medium and the terminal of user's portrait ID |
| CN110019837A (en) * | 2017-12-22 | 2019-07-16 | 百度在线网络技术（北京）有限公司 | The generation method and device, computer equipment and readable medium of user's portrait |
| US10489715B2 (en) | 2015-03-27 | 2019-11-26 | International Business Machines Corporation | Fingerprinting and matching log streams |
| CN110507997A (en) * | 2019-08-12 | 2019-11-29 | 广州小丑鱼信息科技有限公司 | A kind of user behavior analysis method and system |
| CN111950282A (en) * | 2020-07-08 | 2020-11-17 | 国家计算机网络与信息安全管理中心 | A method and device for expanding line analysis based on network behavior characteristics |
| CN120317900A (en) * | 2025-03-31 | 2025-07-15 | 苏州瑞盈智算科技有限公司 | A distributed data processing system based on big data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101655868A (en) * | 2009-09-03 | 2010-02-24 | 中国人民解放军信息工程大学 | Network data mining method, network data transmitting method and equipment |
| WO2011029238A1 (en) * | 2009-09-11 | 2011-03-17 | 华为技术有限公司 | Advertisement delivery method, advertisement server and advertisement system |
| CN102364468A (en) * | 2011-09-29 | 2012-02-29 | 北京亿赞普网络技术有限公司 | User network behavior analysis method, device and system |
| CN103248677A (en) * | 2013-04-24 | 2013-08-14 | 深圳天源迪科信息技术股份有限公司 | Internet behavior analysis system and working method thereof |
-
2014
- 2014-01-20 CN CN201410024921.1A patent/CN103761296A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101655868A (en) * | 2009-09-03 | 2010-02-24 | 中国人民解放军信息工程大学 | Network data mining method, network data transmitting method and equipment |
| WO2011029238A1 (en) * | 2009-09-11 | 2011-03-17 | 华为技术有限公司 | Advertisement delivery method, advertisement server and advertisement system |
| CN102364468A (en) * | 2011-09-29 | 2012-02-29 | 北京亿赞普网络技术有限公司 | User network behavior analysis method, device and system |
| CN103248677A (en) * | 2013-04-24 | 2013-08-14 | 深圳天源迪科信息技术股份有限公司 | Internet behavior analysis system and working method thereof |
Non-Patent Citations (2)
| Title |
|---|
| 孙一鸣: "移动互联网用户行为监测数据系统的设计与实现", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 王璐: "移动互联网用户行为分析", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105573999B (en) * | 2014-10-09 | 2019-02-26 | 阿里巴巴集团控股有限公司 | Method and apparatus for identifying users of smart devices |
| WO2016054988A1 (en) * | 2014-10-09 | 2016-04-14 | 阿里巴巴集团控股有限公司 | Method and apparatus for identifying intelligent device users |
| CN105573999A (en) * | 2014-10-09 | 2016-05-11 | 阿里巴巴集团控股有限公司 | Method and device for recognizing intelligent equipment users |
| CN104298751A (en) * | 2014-10-14 | 2015-01-21 | 李小宜 | Method and device for obtaining internet virtual identity characteristic information |
| CN104683122A (en) * | 2015-02-12 | 2015-06-03 | 北京集奥聚合科技有限公司 | Multi-screen-linkage-based information transmitting method and system |
| CN106022349A (en) * | 2015-03-27 | 2016-10-12 | 国际商业机器公司 | Method and system for determining device types |
| CN106022349B (en) * | 2015-03-27 | 2020-01-10 | 国际商业机器公司 | Method and system for device type determination |
| US10489715B2 (en) | 2015-03-27 | 2019-11-26 | International Business Machines Corporation | Fingerprinting and matching log streams |
| US10489714B2 (en) | 2015-03-27 | 2019-11-26 | International Business Machines Corporation | Fingerprinting and matching log streams |
| CN104778273B (en) * | 2015-04-24 | 2018-10-09 | 淘金信息科技江苏有限公司 | A kind of big data analysis system for shopping website |
| CN104778273A (en) * | 2015-04-24 | 2015-07-15 | 淘金信息科技江苏有限公司 | Big data analysis system for shopping website |
| CN106503015A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of method for building user's portrait |
| CN106504099A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of system for building user's portrait |
| CN105630658A (en) * | 2015-12-22 | 2016-06-01 | 北京奇虎科技有限公司 | Data processing method and data processing device |
| CN105630658B (en) * | 2015-12-22 | 2018-10-09 | 北京奇虎科技有限公司 | The method and device of data processing |
| CN105608604A (en) * | 2015-12-30 | 2016-05-25 | 合一网络技术(北京)有限公司 | Continuous calculation method of brand advertisement effectiveness optimization |
| CN105590240A (en) * | 2015-12-30 | 2016-05-18 | 合一网络技术(北京)有限公司 | Discrete calculating method of brand advertisement effect optimization |
| CN106446228A (en) * | 2016-10-08 | 2017-02-22 | 中国工商银行股份有限公司 | Collection analysis method and device for WEB page data |
| CN106446228B (en) * | 2016-10-08 | 2020-01-10 | 中国工商银行股份有限公司 | Method and device for collecting and analyzing WEB page data |
| CN106649069B (en) * | 2016-12-28 | 2019-12-13 | Tcl集团股份有限公司 | Method and system for user behavior statistics |
| CN106649069A (en) * | 2016-12-28 | 2017-05-10 | Tcl集团股份有限公司 | User behavior statistical method and system |
| CN107609040A (en) * | 2017-08-16 | 2018-01-19 | 深信服科技股份有限公司 | Data analysing method, data processing equipment and computer-readable recording medium |
| CN107704494B (en) * | 2017-08-24 | 2021-09-14 | 深圳市来玩科技有限公司 | User information collection method and system based on application software |
| CN107704494A (en) * | 2017-08-24 | 2018-02-16 | 上海斐讯数据通信技术有限公司 | A kind of user information collection method and system based on application software |
| CN110019837A (en) * | 2017-12-22 | 2019-07-16 | 百度在线网络技术（北京）有限公司 | The generation method and device, computer equipment and readable medium of user's portrait |
| CN110019837B (en) * | 2017-12-22 | 2021-07-16 | 百度在线网络技术（北京）有限公司 | User portrait generation method and device, computer equipment and readable medium |
| CN108337290A (en) * | 2017-12-26 | 2018-07-27 | 努比亚技术有限公司 | A kind of method and apparatus of enterprise staff user's behaviors analysis |
| CN108549685A (en) * | 2018-04-08 | 2018-09-18 | 武志学 | Behavior analysis method, device, system and readable storage medium storing program for executing |
| WO2019227423A1 (en) * | 2018-05-31 | 2019-12-05 | 优视科技新加坡有限公司 | Method and apparatus for collecting user feature information, and device/terminal/server |
| CN109496302A (en) * | 2018-05-31 | 2019-03-19 | 优视科技新加坡有限公司 | A kind of user's characteristic information collection method, device and equipment/terminal/server |
| CN109348542A (en) * | 2018-09-17 | 2019-02-15 | 深圳市三体科技有限公司 | A kind of data transmission method based on big data, storage medium and mobile terminal |
| CN109350969A (en) * | 2018-10-22 | 2019-02-19 | 镇江悦乐网络科技有限公司 | Knowledge based map game data intelligent analysis method |
| CN109670112A (en) * | 2018-12-20 | 2019-04-23 | 北京城市网邻信息技术有限公司 | A kind of generation method, device, storage medium and the terminal of user's portrait ID |
| CN110507997A (en) * | 2019-08-12 | 2019-11-29 | 广州小丑鱼信息科技有限公司 | A kind of user behavior analysis method and system |
| CN111950282A (en) * | 2020-07-08 | 2020-11-17 | 国家计算机网络与信息安全管理中心 | A method and device for expanding line analysis based on network behavior characteristics |
| CN120317900A (en) * | 2025-03-31 | 2025-07-15 | 苏州瑞盈智算科技有限公司 | A distributed data processing system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103761296A (en) | Method and system for analyzing network behaviors of mobile terminal users | |
| US10505884B2 (en) | Entity classification and/or relationship identification | |
| CN103914550B (en) | Show the method and apparatus of content recommendation | |
| CN103763361B (en) | A kind of method, system and recommendation server for recommending application based on user behavior | |
| KR101419504B1 (en) | System and method providing a suited shopping information by analyzing the propensity of an user | |
| US20200118168A1 (en) | Advertising method, device and system, and computer-readable storage medium | |
| JP5615857B2 (en) | Analysis apparatus, analysis method, and analysis program | |
| US20140337328A1 (en) | System and method for retrieving and presenting concept centric information in social media networks | |
| CN108664513B (en) | Method, device and equipment for pushing keywords | |
| US20140095308A1 (en) | Advertisement distribution apparatus and advertisement distribution method | |
| CN106682144A (en) | Page display method and device | |
| CN107426328B (en) | Information pushing method and device | |
| CN107977678B (en) | Method and apparatus for outputting information | |
| TW201503019A (en) | Method and system for discovery of user unknown interests | |
| CN108256537A (en) | A kind of user gender prediction method and system | |
| CN106095867A (en) | A kind of book recommendation method based on industry analysis and device | |
| CN107451832A (en) | The method and apparatus of pushed information | |
| CN105205686A (en) | Method and system for obtaining product price information | |
| CN110674404A (en) | Link information generation method, device, system, storage medium and electronic equipment | |
| CN108629605A (en) | Real-time questionnaire method for pushing based on online customer service | |
| CN103854206A (en) | Method and device for analyzing group characteristics | |
| CN105389714B (en) | Method for identifying user characteristics from behavior data | |
| CN108628980A (en) | A kind of user's portrait method and system based on user network behavior | |
| CN111414074A (en) | Screen browsing data processing method, device, medium and electronic device | |
| CN111415183A (en) | Method and apparatus for processing access requests |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C53 | Correction of patent of invention or patent application | ||
| CB03 | Change of inventor or designer information |
Inventor after: Cui Jingjing Inventor after: Lin Jiajie Inventor after: Dan Xiaolong Inventor before: Cui Jingjing Inventor before: Lin Jiajie Inventor before: Liu Lina Inventor before: Dan Xiaolong |
|
| COR | Change of bibliographic data |
Free format text: CORRECT: INVENTOR; FROM: CUI JINGJING LIN JIAJIE LIU LINA DAN XIAOLONG TO: CUI JINGJING LIN JIAJIE DAN XIAOLONG |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140430 |