CN102736918A - Method and system for retrieving user in Web behavior targeting - Google Patents
Method and system for retrieving user in Web behavior targeting Download PDFInfo
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- CN102736918A CN102736918A CN2012100896852A CN201210089685A CN102736918A CN 102736918 A CN102736918 A CN 102736918A CN 2012100896852 A CN2012100896852 A CN 2012100896852A CN 201210089685 A CN201210089685 A CN 201210089685A CN 102736918 A CN102736918 A CN 102736918A
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- 230000008685 targeting Effects 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 36
- 235000014510 cooky Nutrition 0.000 claims abstract description 37
- 230000003542 behavioural effect Effects 0.000 claims description 49
- 230000002650 habitual effect Effects 0.000 claims description 40
- 230000002085 persistent effect Effects 0.000 claims description 4
- 238000013179 statistical model Methods 0.000 claims description 4
- 230000002688 persistence Effects 0.000 claims description 2
- 238000011084 recovery Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 description 2
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Abstract
The invention provides a method and a system for retrieving a user in Web behavior targeting. The method is used for improving the identification of a Web behavior targeting system on the user. The method is characterized by comprising the following steps of 101) taking an acquired behavior model in a history access of the user as a factor generated in the user retrieving; and 102) re-retrieving a user losing cookie marks through an acquired behavior model value so as to match the user losing cookie marks with a located history user, namely, the user losing the cookies marks is retrieved, and the web behavior targeting system can relocate the user. Therefore, the behavior model can be used for calculating according to a behavior time m that the user accesses a certain website and website types t within a period n.
Description
Technical field
The present invention relates to the web behavioral targeting, particularly when the web behavioral targeting is used for online advertisement,, be specifically related to a kind ofly in the Web behavioral targeting, give user method and system for change the method that the user who loses mark gives for change again.
Background technology
In the web behavioral targeting was used, most crucial technology was carried out mark to the visitor with exactlying, and the labeling method of using always at present is to use js cookie, and as user once more during access websites, the website can be demarcated and discern the user through canned data among the cookie.
In case but the user deletes cookie; Perhaps to behind the hard disc of computer reformatting, the cookie information on the subscriber computer will be lost, so as user once more during access websites; Web behavioral targeting system just can't carry out identification to the user; Can only be with this user as new user, his historical behavior record and his analysis results such as characteristic, interest just can't be mapped with him that is to say that his historical data had just lost efficacy so.
Therefore, at present the user is carried out the method for mark, in case can make that cookie loses, the user just is difficult to given for change, and all are all lost the analysis result of this user's historical behavior.This uses the web behavioral targeting is a kind of very big loss.
Fig. 1 is the method for in the web behavioral targeting, giving the user for change of prior art: existing method is website visiting user capture website; The website generates the cookie mark; Contain unique identification ID number of user in this cookie mark, the cookie tab file is implanted in the user's computer.In case user's computer is formatd or cookie file is deleted, navigate to website visiting user's link fracture, therefore existing method can't be given the user for change.
Summary of the invention
The objective of the invention is to; In case cookie loses in the prior art for overcoming; The user just is difficult to given for change; Thereby all are all lost the analysis result of this user's historical behavior and cause the loss that the web behavioral targeting is used, thereby a kind of multifactor user method of giving for change in the Web behavioral targeting is provided.
Be to realize the foregoing invention purpose, the invention provides and a kind ofly in the Web behavioral targeting, give user method for change that this method is used to improve the discrimination power of web behavioral targeting system to the user, it is characterized in that described method comprises following steps:
Factor when step 101) giving the habitual behavior pattern do generation in the historical visit behavior of user for change user;
The user that step 102) will lose the cookie mark through the behavioural habits model value gives for change again; Thereby this user and the historical user who has been positioned are mapped; Promptly give the user who loses the cookie mark for change, made web behavioral targeting system can be repositioned onto this user.
In the technique scheme, described habitual behavior pattern is:
In a period of time n; The behavior number of times m of user capture type website; Type of website t; Habitual behavior pattern h; Then habitual behavior pattern h is:
user's habitual behavior pattern can be confirmed: historical h value h1; With current h2, if
also more possible property of user that h2 is corresponding so is the corresponding user of h1.
In the technique scheme, said behavior number of times m, this number of times m equal the user capture type number of times for the website of t.
Also provide a kind of based on said method the present invention and in the Web behavioral targeting, given custom system for change; This system is used for web behavioral targeting system is lost the giving for change again of user of cookie mark; Improve the discrimination power of web behavioral targeting system to the user; It is characterized in that said system comprises: some users of access websites, online behavioral targeting subsystem and acquired behavior statistical model subsystem;
Said online behavioral targeting subsystem; Being used for to the maiden visit type is that the user of the website of t does unique ID; This ID is with cookie identification document record; And record access user's visit behavior, said visit behavior comprises: the type of visit is address and the time p of visit of the website of t;
Said habitual behavior pattern statistics sub system; Be used for the access time p to the website of t type according to online behavioral targeting subsystem output user; Calculate a period of time interval n=p2-p1; According to user capture record accumulative total, draw the access times m of visit t type website again, finally calculate the habitual behavior pattern value of each calling party.
In the technique scheme, said habitual behavior pattern value is deposited in the acquired behavior statistical model subsystem, with database or document form persistence, and along with time interval p, regular update.
In the technique scheme, said habitual behavior pattern statistics sub system further comprises like lower module,
User's habitual behavior pattern computing module; This module is accepted the access time p of input user to every Visitor Logs of t type website; Calculate a period of time interval n=p2-p1; According to user capture record accumulative total, draw the access times m of visit t type website, finally calculate user's habitual behavior pattern value;
The memory module of user's habitual behavior pattern value, this module is carried out persistent storage with the habitual behavior pattern value that user's habitual behavior pattern computing module calculates; With
User's recovery module; This module is according to habitual behavior pattern value h; Go the identical user of retrieval h value in the persistent storage, and the user's who retrieves ID is outputed in the online behavioral targeting subsystem, ID is write in the cookie tab file of access customer again by online behavioral targeting subsystem.
The invention has the advantages that, propose a kind ofly behind user loss or deleting history cookie, give user's method again for change.In case user loss or deleting history cookie utilize this method can realize the giving for change again of user, let this user's historical record and analysis result and this user be mapped, remedied the deficiency of existing method, strengthened the effect of behavioral targeting.
Description of drawings
Fig. 1 is the structural representation of user's the method for in the web behavioral targeting, giving for change of prior art;
Fig. 2 is a structural representation of in the web behavioral targeting, giving user's method for change of the present invention;
Fig. 3 is the process flow diagram of in the web behavioral targeting, giving user method for change of the embodiment of the invention.
Embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is further specified.
The web behavioral targeting is that the ID of recording user among the cookie comes certain user of unique correspondence through ID through a generation cookie (tab file) on subscriber computer.In case User Format computing machine or user delete the cookie file on the computing machine so, in the web behavioral targeting, this user just can't locate in system so.Method is described: its method is through the behavioural habits model algorithm; Draw the behavioural habits model value h1 of certain new user a; If behavioural habits model value h1 equals or approximate the value h2 of the behavioural habits model of certain historical user b, so new user a promptly can corresponding historical user b, therefore; In the web behavioral targeting; Even if the cookie that deposits on the user b computing machine is deleted, user b still can give for change through said method so, and system can think that a user is exactly the b user who is deleted cookie.
Above-mentioned habitual behavior pattern: in a period of time n; The behavior number of times m of user capture type website; Type of website t; Habitual behavior pattern h;
user's habitual behavior pattern can be confirmed: historical h value h1; With current h2, if
also more possible property of user that h2 is corresponding so is the corresponding user of h1.
As shown in Figure 2: website visiting user a; Even if computing machine format or cookie file are deleted; This method still can be according to the history access record of user a, and the algorithm computation that accordings to habitual behavior pattern goes out the habitual behavior pattern value, that is: according in a period of time n; The behavior number of times m of user capture type website; Type of website t, habitual behavior pattern h,
calculates the habitual behavior pattern value h1 of user a.
In case website visiting user b is arranged; Through calculating the habitual behavior pattern value h2 of user b,, can think that then user b promptly loses the user a of cookie tab file if h2 equals h1; So just user a and user b are mapped, the system that is to say has given user a again for change.
Embodiment
As shown in Figure 3, concrete steps are following:
Step 101) to all carrying out the plantation of cookie tab file in each website visiting user's computer, deposits mark user's identify label number ID in the cookie tab file, user of this ID unique identification.
Step 102) the visit behavior of website records calling party: the Type of website t of visit, the time p of visit according to time p, calculates a period of time interval n=p2-p1.According to user capture record accumulative total; Draw the access times m of the visit t Type of website, calculate habitual behavior pattern value
Step 103) in case the cookie mark of certain user a is deleted, and promptly mark is lost, so behind a period of time n; H value h1 according to user a; Calculate the h value that all are labeled the user,, can think that so user b promptly loses the user a of cookie tab file as long as find the habitual behavior pattern value h2=h1 of certain user b; So just user a and user b are mapped, the system that is to say has given user a again for change.
Step 104) can the ID in the cookie tab file of user b be labeled as again the ID value of user a.So far, just can successfully give user a for change.
It should be noted last that above embodiment is only unrestricted in order to technical scheme of the present invention to be described.Although the present invention is specified with reference to embodiment; Those of ordinary skill in the art is to be understood that; Technical scheme of the present invention is made amendment or is equal to replacement, do not break away from the spirit and the scope of technical scheme of the present invention, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (6)
1. in the Web behavioral targeting, give user method for change for one kind, this method is used to improve the discrimination power of web behavioral targeting system to the user, it is characterized in that described method comprises following steps:
Factor when step 101) giving the habitual behavior pattern do generation in the historical visit behavior of user for change user;
The user that step 102) will lose the cookie mark through the behavioural habits model value gives for change again; Thereby this user and the historical user who has been positioned are mapped; Promptly give the user who loses the cookie mark for change, made web behavioral targeting system can be repositioned onto this user.
2. according to claim 1ly in the Web behavioral targeting, give user method for change, it is characterized in that described habitual behavior pattern is:
In a period of time n; The behavior number of times m of user capture type website; Type of website t; Habitual behavior pattern h; Then habitual behavior pattern h is:
user's habitual behavior pattern can be confirmed: historical h value h1; With current h2, if
also more possible property of user that h2 is corresponding so is the corresponding user of h1.
3. according to claim 1ly in the Web behavioral targeting, give user method for change, it is characterized in that, said behavior number of times m, this number of times m equal the user capture type number of times for the website of t.
4. in the Web behavioral targeting, give custom system for change for one kind; This system is used for web behavioral targeting system is lost the giving for change again of user of cookie mark; Improve the discrimination power of web behavioral targeting system to the user; It is characterized in that said system comprises: some users of access websites, online behavioral targeting subsystem and acquired behavior statistical model subsystem;
Said online behavioral targeting subsystem; Being used for to the maiden visit type is that the user of the website of t does unique ID; This ID is with cookie identification document record; And record access user's visit behavior, said visit behavior comprises: the type of visit is address and the time p of visit of the website of t;
Said habitual behavior pattern statistics sub system; Be used for the access time p to the website of t type according to online behavioral targeting subsystem output user; Calculate a period of time interval n=p2-p1; According to user capture record accumulative total, draw the access times m of visit t type website again, finally calculate the habitual behavior pattern value of each calling party.
5. according to claim 4ly in the Web behavioral targeting, give custom system for change; It is characterized in that said habitual behavior pattern value is deposited in the acquired behavior statistical model subsystem, with database or document form persistence; And along with time interval p, regular update.
6. in the Web behavioral targeting, give custom system according to claim 4 is said for change, it is characterized in that said habitual behavior pattern statistics sub system further comprises like lower module,
User's habitual behavior pattern computing module; This module is accepted the access time p of input user to every Visitor Logs of t type website; Calculate a period of time interval n=p2-p1; According to user capture record accumulative total, draw the access times m of visit t type website, finally calculate user's habitual behavior pattern value;
The memory module of user's habitual behavior pattern value, this module is carried out persistent storage with the habitual behavior pattern value that user's habitual behavior pattern computing module calculates; With
User's recovery module; This module is according to habitual behavior pattern value h; Go the identical user of retrieval h value in the persistent storage, and the user's who retrieves ID is outputed in the online behavioral targeting subsystem, ID is write in the cookie tab file of access customer again by online behavioral targeting subsystem.
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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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| 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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| WO2015074503A1 (en) * | 2013-11-19 | 2015-05-28 | 北京国双科技有限公司 | Statistical method and apparatus for webpage access data |
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| 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 |
| US20110016534A1 (en) * | 2009-07-16 | 2011-01-20 | Palo Alto Research Center Incorporated | Implicit authentication |
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Patent Citations (5)
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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 |
| CN1758248A (en) * | 2004-10-05 | 2006-04-12 | 微软公司 | Systems, methods, and interfaces for providing personalized search and information access |
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| WO2015074503A1 (en) * | 2013-11-19 | 2015-05-28 | 北京国双科技有限公司 | Statistical method and apparatus for webpage access data |
| US10248730B2 (en) | 2013-11-19 | 2019-04-02 | Beijing Gridsum Technology Co., Ltd. | Statistical method and apparatus for webpage access data |
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