CN102736918B - A kind of in Web behavioral targeting, give user method and system for change - Google Patents
A kind of in Web behavioral targeting, give user method and system for change Download PDFInfo
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- CN102736918B CN102736918B CN201210089685.2A CN201210089685A CN102736918B CN 102736918 B CN102736918 B CN 102736918B CN 201210089685 A CN201210089685 A CN 201210089685A CN 102736918 B CN102736918 B CN 102736918B
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Abstract
The invention provides and a kind of in Web behavioral targeting, give user method and system for change, the method is for improving the discrimination power of web behavioral targeting system of users, it is characterized in that, described method comprises the steps of: step 101) make to generate factor when giving user for change by the habitual behavior pattern in user's history access behavior；Step 102) by behavioural habits model value, the user losing cookie labelling is given for change again, thus this user is mapped with the historic user being positioned, i.e. give the user losing cookie labelling for change so that web behavioral targeting system can be repositioned onto this user.Described habitual behavior pattern can be according to according in a period of time n, and user accesses the behavior number of times m of certain class website, and Type of website t calculates.
Description
Technical field
The present invention relates to web behavioral targeting, particularly when web behavioral targeting is for online advertisement, to losing mark
The method again given for change of user of note, is specifically related to a kind of give user method and system for change in Web behavioral targeting.
Background technology
In web behavioral targeting is applied, visitor is marked by most crucial technology exactly, the most conventional labelling
Method is to use js cookie, and when user accesses website again, website can be by the information pair of storage in cookie
User demarcates and identifies.
But once user deletes cookie, or after hard disc of computer is reformatted, on subscriber computer
Cookie information will be lost, then when user accesses website again, and web behavioral targeting system just cannot be to user
Carry out identification, can only be using this user as new user, then his historical behavior record and to his feature,
The analysis results such as interest just cannot be mapped with him, say, that his historical data just fails.
Therefore, the method at present user being marked, once cookie can be made to lose, user is just difficult to be looked for
Returning, all analysis results to the historical behavior of this user are the most all lost.This is one to the application of web behavioral targeting
Plant the biggest loss.
Fig. 1 is the method giving user in web behavioral targeting for change of prior art: existing method is that website visiting is used
Access website, family, website generates cookie labelling, contains unique identification ID of user in this cookie labelling
Number, in the computer of the implanted user of cookie tab file.Once the computer of user is formatted or cookie
File is deleted, and navigates to the link breakdown of website visiting user, and the most existing method cannot give user for change.
Summary of the invention
It is an object of the invention to, for overcoming in prior art once cookie to lose, user is just difficult to be retrieved,
The analysis result of all historical behaviors to this user is the most all lost thus is caused the damage to the application of web behavioral targeting
Lose, thus provide a kind of multifactor in Web behavioral targeting to give user method for change.
For achieving the above object, the invention provides and a kind of in Web behavioral targeting, give user side for change
Method, the method is for improving the discrimination power of web behavioral targeting system of users, it is characterised in that described method
Comprise the steps of:
Step 101) make to generate factor when giving user for change by the habitual behavior pattern in user's history access behavior；
Step 102) by behavioural habits model value, the user losing cookie labelling is given for change again, thus should
User is mapped with the historic user being positioned, and has i.e. given the user losing cookie labelling for change so that web
Behavioral targeting system can be repositioned onto this user.
In technique scheme, described habitual behavior pattern is:
According in a period of time n, user accesses the behavior number of times m of certain class website, Type of website t, acquired behavior mould
Type h, then habitual behavior pattern h is:
The habitual behavior pattern of user can determine: goes through
History h value h1, and current h2, if
It is that h1 is corresponding that user corresponding for so h2 also gets over the most likely property
User.
In technique scheme, described behavior number of times m, this number of times m access, equal to user, the website that certain type is t
Number of times.
Present invention also offers based on said method and a kind of in Web behavioral targeting, give custom system for change, this system
For to web behavioral targeting system is lost again the giving for change of user of cookie labelling, improving web behavioral targeting
The discrimination power of system of users, it is characterised in that described system comprises: access some users of website, online row
For orientation subsystem and acquired behavior statistical model subsystem；
Described online behavioral targeting subsystem, does unique mark for the user to the website that maiden visit type is t
ID, this ID cookie identify file record, and the access behavior of record access user, described access behavior bag
Contain: the type of access is address and the time p of access of the website of t；
Described habitual behavior pattern statistics sub system, for exporting user to t type according to online behavioral targeting subsystem
The access time p of website, calculate interval of time n=p2-p1, access record further according to user accumulative,
Draw access times m accessing t type website, the final habitual behavior pattern value calculating each access user.
In technique scheme, described habitual behavior pattern value is deposited in acquired behavior statistical model subsystem, with
Data base or document form persistence, and it is spaced p, regular update over time.
In technique scheme, described habitual behavior pattern statistics sub system comprises such as lower module further,
User habit behavior model computing module, this module accepts the input user every access record to t type website
Access time p, calculate interval of time n=p2-p1, access record according to user accumulative, draw access
Access times m of t type website, the final habitual behavior pattern value calculating user；
The memory module of user habit behavior model value, user habit behavior model computing module is calculated by this module
The habitual behavior pattern value gone out carries out persistent storage；With
User's recovery module, this module, according to habitual behavior pattern value h, goes to retrieve h value in persistent storage identical
User, and the ID of the user retrieved is exported in online behavioral targeting subsystem, by online behavioral targeting
ID is re-write in the cookie tab file of user by system.
It is an advantage of the current invention that proposition is a kind of when user loses or after deleting history cookie, again gives user for change
Method.Once user loses or deleting history cookie, utilizes the method can realize again looking for user
Return, allow the historical record of this user and analysis result be mapped with this user, compensate for the most methodical deficiency,
Enhance the effect of behavioral targeting.
Accompanying drawing explanation
Fig. 1 is the structural representation of the method giving user in web behavioral targeting for change of prior art；
Fig. 2 is the structural representation of the method giving user in web behavioral targeting for change of the present invention；
Fig. 3 is the flow chart giving user method in web behavioral targeting for change of the embodiment of the present invention.
Detailed description of the invention
With embodiment, the present invention is further described below in conjunction with the accompanying drawings.
Web behavioral targeting is by generating a cookie (tab file) on the user computer, remembers in cookie
Employ the ID at family, carry out unique certain user corresponding by ID.The most once User Format computer or user delete
Except the cookie file on computer, then in web behavioral targeting, system just cannot position this user.
Method describes: its method is by behavioural habits model algorithm, draws the behavioural habits model value h1 of certain new user a,
If behavioural habits model value h1 is equal to or approximates value h2 of the behavioural habits model of certain historic user b, then
New user a i.e. can corresponding historic user b, therefore, in web behavioral targeting, calculate even if depositing in user b
Cookie on machine is deleted, then user b still can be given for change by said method, and system is it is believed that a user
It is exactly the b user being deleted cookie.
Above-mentioned habitual behavior pattern: according in a period of time n, user accesses the behavior number of times m of certain class website,
Type of website t, habitual behavior pattern h,
The habitual behavior pattern of user may determine that: history
H value h1, and current h2, if
It is use corresponding for h1 that user corresponding for so h2 also gets over the most likely property
Family.
As shown in Figure 2: website visiting user a, even if computer formats or cookie file is deleted, this method
Still habitual behavior pattern can be calculated according to the history access record of user a according to the algorithm of habitual behavior pattern
Value, it may be assumed that according in a period of time n, user accesses the behavior number of times m of certain class website, Type of website t, custom row
For model h,
Calculate habitual behavior pattern value h1 of user a.
Once there is website visiting user b, by calculating habitual behavior pattern value h2 of user b, if h2 is equal to
H1 is then it is believed that user b i.e. loses the user a of cookie tab file, thus corresponding with user b by user a
Get up, say, that system has given user a again for change.
Embodiment
As it is shown on figure 3, specifically comprise the following steps that
Step 101) computer to each website visiting user carries out the plantation of cookie tab file, cookie
The identity number ID, this ID that deposit labelling user in tab file uniquely identify a user.
Step 102) website records access user access behavior: Type of website t of access, the time p of access,
According to time p, calculate interval of time n=p2-p1.Access record according to user accumulative, draw access t
Access times m of the Type of website, calculate habitual behavior pattern value
Step 103) once certain user a cookie labelling be deleted, i.e. label loss, then a period of time n
After, according to h value h1 of user a, calculate the h value of all labeled users, as long as finding the custom of certain user b
Behavior model value h2=h1, then it is believed that user b i.e. loses the user a of cookie tab file, thus will use
Family a and user b is mapped, say, that system has given user a again for change.
Step 104) ID in the cookie tab file of user b can be re-flagged the ID value into user a.
So far, it is possible to successfully give user a for change.
It should be noted last that, above example is only in order to illustrate technical scheme and unrestricted.Although
With reference to embodiment, the present invention is described in detail, it will be understood by those within the art that, to the present invention
Technical scheme modify or equivalent, without departure from the spirit and scope of technical solution of the present invention, it is equal
Should contain in the middle of scope of the presently claimed invention.
Claims (3)
1. giving a user method for change in Web behavioral targeting, the method is used for improving web behavioral targeting system
The system discrimination power to user, it is characterised in that described method comprises the steps of:
Step 101) using the habitual behavior pattern in user's history access behavior as the factor generated when giving user for change；
Step 102) by behavioural habits model value, the user losing cookie labelling is given for change again, thus should
User is mapped with the historic user being positioned, and has i.e. given the user losing cookie labelling for change so that web
Behavioral targeting system can be repositioned onto this user；Described habitual behavior pattern is:
According in a period of time n, user accesses the behavior number of times m of certain class website, Type of website t, acquired behavior mould
Type h, then habitual behavior pattern h is:The habitual behavior pattern of user can determine: goes through
History h value h1, and current h value h2, ifIt is h1 pair that user corresponding for so h2 also gets over the most likely property
The user answered.
The most according to claim 1 in Web behavioral targeting, give user method for change, it is characterised in that institute
State behavior number of times m, this number of times m and access the number of times of the website that certain type is t equal to user.
3. giving a custom system for change in Web behavioral targeting, this system is for web behavioral targeting system
The user of middle loss cookie labelling gives for change again, improves the discrimination power of web behavioral targeting system of users, its
Being characterised by, described system comprises: access some users of website, online behavioral targeting subsystem and acquired behavior
Statistical model subsystem；
Described online behavioral targeting subsystem, does unique mark for the user to the website that maiden visit type is t
ID, this ID cookie identify file record, and the access behavior of record access user, described access behavior bag
Contain: the type of access is address and the time p of access of the website of t；
Described acquired behavior statistical model subsystem, for exporting user to t type according to online behavioral targeting subsystem
The access time p of website, calculate interval of time n=p2-p1, access record further according to user accumulative,
Draw access times m accessing t type website, the final habitual behavior pattern value calculating each access user；Institute
State habitual behavior pattern value and deposit in acquired behavior statistical model subsystem, lasting with data base or document form
Change, and be spaced n, regular update over time；
Described acquired behavior statistical model subsystem comprises such as lower module further,
User habit behavior model computing module, this module accepts the input user every access record to t type website
Access time p, calculate interval of time n=p2-p1, access record according to user accumulative, draw access
Access times m of t type website, the final habitual behavior pattern value calculating user；
The memory module of user habit behavior model value, user habit behavior model computing module is calculated by this module
The habitual behavior pattern value gone out carries out persistent storage；With
User's recovery module, this module, according to habitual behavior pattern value h, goes to retrieve h value in persistent storage identical
User, and the ID of the user retrieved is exported in online behavioral targeting subsystem, by online behavioral targeting
ID is re-write in the cookie tab file of user by system；
Wherein, habitual behavior pattern h is:
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| CN201210089685.2A CN102736918B (en) | 2011-03-30 | 2012-03-30 | A kind of in Web behavioral targeting, give user method and system for change |
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| CN201110077636 | 2011-03-30 | ||
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| CN201210089685.2A CN102736918B (en) | 2011-03-30 | 2012-03-30 | A kind of in Web behavioral targeting, give user method and system for change |
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| CN103605738B (en) * | 2013-11-19 | 2017-03-15 | 北京国双科技有限公司 | Web page access data statistical method and device |
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| CN1758248A (en) * | 2004-10-05 | 2006-04-12 | 微软公司 | Systems, methods, and interfaces for providing personalized search and information access |
| CN101441657A (en) * | 2008-12-31 | 2009-05-27 | 阿里巴巴集团控股有限公司 | Caller intent recognition system and method and caller intent recognition platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20030167195A1 (en) * | 2002-03-01 | 2003-09-04 | Fernandes Carlos Nicholas | System and method for prioritization of website visitors to provide proactive and selective sales and customer service online |
| US20050192863A1 (en) * | 2004-02-26 | 2005-09-01 | Krishna Mohan | Web site vistor incentive program in conjunction with promotion of anonymously identifying a user and/or a group |
| US8312157B2 (en) * | 2009-07-16 | 2012-11-13 | Palo Alto Research Center Incorporated | Implicit authentication |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1758248A (en) * | 2004-10-05 | 2006-04-12 | 微软公司 | Systems, methods, and interfaces for providing personalized search and information access |
| CN101441657A (en) * | 2008-12-31 | 2009-05-27 | 阿里巴巴集团控股有限公司 | Caller intent recognition system and method and caller intent recognition platform |
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