CN102946319A - System and method for analyzing network user behavior information - Google Patents
System and method for analyzing network user behavior information Download PDFInfo
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- CN102946319A CN102946319A CN2012103742926A CN201210374292A CN102946319A CN 102946319 A CN102946319 A CN 102946319A CN 2012103742926 A CN2012103742926 A CN 2012103742926A CN 201210374292 A CN201210374292 A CN 201210374292A CN 102946319 A CN102946319 A CN 102946319A
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Abstract
The invention discloses a system and a method for analyzing network user behavior information. The system comprises a network user behavior data obtaining module, a network user behavior data preprocessing module, a network user behavior data storage module, a network user behavior data analyzing module and an analysis result display module, which are connected with each other in sequence; and the method comprises the steps of data collection, data preprocessing, data conversion, data analysis, data displaying and so on. According to the invention, the network user behavior information can be conveniently and rapidly obtained, and integrity, continuity and effectiveness of the user behavior information are guaranteed.
Description
Technical field
The invention belongs to the Analysis of Network Information field, particularly a kind of network user's behavioural information analytical system and analytical method thereof.
Background technology
Along with the development of the Internet and popularizing of computer, the network user's quantity growth is rapid, and network behavior becomes one of most important social phenomenon in the human behavior gradually.Understand the behavior with the phase-split network user in depth, could find the influencing factor of user website usage behavior, help to improve and optimize based on network information service, improve the efficient of information management and service.
In the scope of behavior information, behavior refers to as the movable of action, operation or event and entity produces under specific situation and environment in virtual or actual tissue active sequences.What the present invention mainly studied is the characteristics that have at the net environment user behavior.
Network behavior can quantitatively or qualitatively represent with the statistical nature of some characteristic quantity or the incidence relation of characteristic quantity.The user concludes the business by e-commerce website, in the operation process of these websites, accumulated the data message of a large amount of relevant client's behaviors, these behavioral datas are further studied, can find general modfel and rule in the user website usage behavior, and then find the problem that may exist in webpage and the web sites function design, thereby find the direction of website Improvement and perfection.
Document 1: Chinese patent CN 101188521 A, Ning Hui, opening great waves. a kind of method of digging user behavioral data and Website server .2008.5 disclose a kind of method and Website server of digging user behavioral data, preserve the web log file data by Website server, read described web log file data, and to described web log file data analysis, this method need not arrange separately statistical server, saves hardware resource and cost.But this method can't realize the analysis to network user's dynamic behaviour.Because the data of web log file data record have certain standard, essential record user's access time, accession page, user ID, access IP etc. can't obtain how needed information.For example, only can not obtain behavioural information the user of website registration failure by the web log file data.
Document 2: Chinese patent CN102238045 A, Xie Yongkai. a kind of wireless Internet user behavior prognoses system .2011.11 discloses a kind of prognoses system of wireless Internet user behavior, this system is by being positioned at cellphone subscriber's behavioral data acquisition module of client, collect cellphone subscriber's user behavior data of running time, and send to server, be positioned at cellphone subscriber's behavioural analysis prediction module of server end, to the user behavior modeling, the user behavior data of collecting according to the user behavior data acquisition module of client carries out user behavior analysis and prediction.This invention and the present invention have certain consistency at thought and method, but also have defective: this invention is carried out cluster analysis to the user behavior data of collecting and is obtained user grouping, set up relation between user grouping and corresponding behavior by correlation rule, so different clustering methods may make the result of grouping inconsistent, cause and analyze the inaccurate of conclusion, should invent mainly for the mobile phone wireless Internet user simultaneously, and the operation system of smart phone of supporting is limited.
Summary of the invention
The object of the present invention is to provide and a kind ofly can obtain easily and fast network user's behavioural information, and can guarantee network user's behavioural information analytical system and the analytical method of user behavior information integrity, continuity and validity.
The technical solution that realizes the object of the invention is:
A kind of network user's behavioural information analytical system, comprise the network user's behavioral data acquisition module that connects successively, network user's behavioral data pretreatment module, network user's behavioral data memory module, network user's behavioral data analysis module, the analysis result display module.
A kind of network user's behavioural information analytical method may further comprise the steps:
Step 1: the program of burying that the utilization of network user's behavioral data acquisition module is integrated in is wherein obtained website user's behavioral data, and it is recorded in the web log file data, then whether the data of collecting in the unit interval are judged unusually, at last with data synchronously to local data base; Wherein burying a program is used for the monitor user ' behavioral data by the program code of the collection user behavior data that is embedded into browser end and rear end and collects the result whether unusual program code forms;
Step 2: network user's behavioral data pretreatment module is identified the web log file data, filters out the user behavior data for the user behavior information analysis;
Step 3: by the behavior sequence analysis user behavior data that extracts is converted to towards the behavioral data in behavioural characteristic space, it is stored in network user's behavioral data memory module;
Step 4: network user's behavioral data analysis module calls the user behavior data in network user's behavioral data memory module, uses the user behavior pattern method for digging of default that user behavior data is analyzed;
Step 5: the analysis result display module the results are shown in user terminal displays device interface with the user behavior information analysis.
The present invention compared with prior art, its remarkable advantage:
1, obtains easily and fast network user's behavioural information, comprise the behavioural information of Static and dynamic, guaranteed user behavior information integrity, continuity and validity;
2, analyze each network user's forward and negative sense behavioural information, fully excavate network user's behavioural characteristic and pattern;
3, data mining algorithm calculated off-line, result of calculation is clear, objective, further sums up business rule to make things convenient for enterprise, carries out optimization and the adjustment of website structure.
Below in conjunction with accompanying drawing the present invention is described in further detail.
Description of drawings
Fig. 1 is the structural representation according to network user's behavioural information analytical system of the present invention.
Fig. 2 is that the JS page front end that the website user according to the embodiment of the invention registers the behavioural information analytical system buries a program diagram.
Fig. 3 is that a program diagram is buried in the Java rear end that the website user according to the embodiment of the invention registers the behavioural information analytical system.
Fig. 4 is that the website user according to the embodiment of the invention registers behavioural information and collects and the storage organization schematic diagram.
Fig. 5 is that the website user according to the embodiment of the invention registers the behavior sequence schematic diagram.
Fig. 6 is the flow chart of registering the behavioural information analytical system according to the website user of the embodiment of the invention.
Embodiment
For the purpose, technical scheme and the advantage that make embodiments of the invention is clearer, the below does simplicity of explanation to some terms that relate among the present invention.
Bury a program: be used for the monitor user ' behavioral data by the program code of the collection user behavior data that is embedded into browser end and rear end and collect the result whether unusual program code forms.
Basic data: be to bury the data that are used for the user behavior information analysis that a program is caught.Basic data belongs to primary data, and needing through native system is the intermediate layer data according to rule extraction.
The intermediate layer data: be from basic data according to predefined rule extraction be the combination of the used business information of native system, the intermediate layer data belong to the semi-finished product data, also need to solve analysis result through native system according to predefined algoritic module.
User: the user of access websites.
Goal behavior: refer to the behavior relevant with the performance of enterprise.
A kind of network user's behavioural information of the present invention analytical system, this system comprises the network user's behavioral data acquisition module that connects successively, network user's behavioral data pretreatment module, network user's behavioral data memory module, network user's behavioral data analysis module, the analysis result display module.
Referring to Fig. 1, shown according to the present invention a kind of structural representation of network user's behavioural information analytical system, specifically comprise with lower module.
User behavior data acquisition module 101: at webpage embedded cover JavaScript script, when user's accessed web page, trigger the statistics script and obtain visit data, the rear end java applet judges whether data structure is unusual, and both data are merged.
Website user's behavioral data pretreatment module 102: the daily record data of data-base recording is identified, screens, classified and gathers according to predefined daily record data storage rule.
Website user's behavioral data memory module 103: will store in the UEAM system data library unit through the form of the pretreated network user's behavioral data of data with standard.
Website user's behavioral data analysis module 104: the data mining algorithm according to default is analyzed daily record data, and with the user behavior analysis result store that draws in the analysis result memory cell.
Analysis result display module 105: data analysis be the results are shown in system manager's terminal display interface.
A kind of network user's behavioural information analytical method may further comprise the steps:
Step 1: the program of burying that the utilization of network user's behavioral data acquisition module is integrated in is wherein obtained website user's behavioral data, and it is recorded in the web log file data, then whether the data of collecting in the unit interval are judged unusually, at last with data synchronously to local data base; Wherein burying a program is used for the monitor user ' behavioral data by the program code of the collection user behavior data that is embedded into browser end and rear end and collects the result whether unusual program code forms;
When obtaining website user's behavioral data, utilized and buried a program, when the user logs in client browser, triggered the program code of collecting user behavior data, when user's access websites page, for the user creates a session and page number; When the user fills in relevant information as requested, collect the automatic recording user behavioral data of program code meeting of user behavior data; Simultaneously, system is provided with the minimum input of user data volume, when the user behavior data of collecting reaches the minimum input of user data volume, the page overall data packing that is recorded to is sent to Web server; Then with the user behavior data collected in the unit interval synchronously to local data base.
With the user behavior data collected in the unit interval during synchronously to local data base, whether unusual program is put judgement in storage to be used for monitor user ' behavioral data collection result by the rear end, judge whether the data and the mean value that collect exist unusually, if exist unusual, then the data of collecting are cast out, replace with mean value, otherwise directly with the data that collect synchronously to local data base; Described is by judging with the difference rate of mean value unusually, and wherein the difference rate can be set;
Step 2: network user's behavioral data pretreatment module is identified the web log file data, filters out the user behavior data for the user behavior information analysis;
The web log file data are identified, screened, namely select useful user behavior data, get rid of useless user behavior data; Following several situation can appear in network user's behavioral data of directly collecting: the page elements name that the page elements name that (1) front-end user interface shows and rear end are recorded in the daily record data is inconsistent; Such as page elements Full Name by name, and the field of logdata record is userName; (2) content that relates to the user security privacy can't be collected record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thereby cause the repeatedly duplicate record of same page elements information; After having filled in email address such as the user, change again email address, filled in so behavior for twice of the user and all can go on record, thereby caused this information of mailbox 2 records to occur.Therefore, got rid of the page elements that relates to the user security privacy in identification, when screening useful user behavior data, and made up the contrast list of the page elements that records in page elements and the daily record data, and for the phenomenon of duplicate record, the principle of taking the information with last record to be as the criterion.
Step 3: by the behavior sequence analysis user behavior data that extracts is converted to towards the behavioral data in behavioural characteristic space, it is stored in network user's behavioral data memory module;
When making up the user behavior sequence, adopted the method for forward structure behavior sequence, i.e. the method for time window; The time window of a rolling is set, according to the order of occurrence of each target, user behavior is moved to right from the left side in the behavior coordinate.
Step 4: network user's behavioral data analysis module calls the user behavior data in network user's behavioral data memory module, uses the user behavior pattern method for digging of default that user behavior data is analyzed;
When network user's behavioral data being carried out the user behavior pattern excavation, mainly adopted support to calculate and these two kinds of methods of correlation analysis;
It is by the support of calculating page elements to goal behavior that support is calculated, and excavates the behavior pattern that those probably cause the ownership goal frequent occurrence, and computing formula is:
Wherein
The representative of consumer behavior,
Representative of consumer behavioral data collection is comprising target data set
With non-target data set
Simultaneously, although low in order to capture those frequencies of occurrences, can produce to goal behavior the behavior of significant impact, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein
Refer to target data set,
Refer to non-target data set,
Target,
Non-target, here
, a threshold value is set
If,
, explanation
To target
Impact be greater than non-target
Impact; If
, then opposite;
In order to weigh the related intimate degree between the user behavior, carried out correlation analysis.
Carrying out correlation analysis is to utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between the page elements; If negative correlation item is thought in P＜0; If 0＜P＜0.2 item thinks uncorrelated; If weak positive correlation item is thought in 0.2＜P＜0.4; If medium positive correlation item is thought in 0.4＜P＜0.6; If strong positive correlation item is thought in 0.6＜P＜0.8; If P〉0.8 item think extremely strong positive correlation.
Step 5: the analysis result display module the results are shown in user terminal displays device interface with the user behavior information analysis.
1 one kinds of network users of embodiment retrieve the behavior information analysis method
Step 1: the program of burying that the utilization of network user's behavioral data acquisition module is integrated in is wherein obtained the website user and is retrieved behavioral data, wherein buries a program and is used for the monitor user ' behavioral data by the program code of the collection user behavior data that is embedded into browser end and rear end and collects the result whether unusual program code forms; As user during according to the demand retrieving information, the automatic recording user name of program code of the collection user behavior data of writing by java applet and the information of user search, if user search has arrived information needed, then the user search behavioral data packing with record sends to the website web server; If the user does not retrieve information needed, when the user leaves the page, the user search behavioral data that records is sent to the website web server; The front end java applet has been collected the user search behavioral data in a week, send request to server, start this moment the rear end by the JavaScript programming be used for the monitor user ' behavioral data collect the result whether the unusual program code warehouse-in that carries out data judge, data of gathering in this week and weekly average data are before compared, whether the difference rate that the data that judgement gathers are compared with average data exceeds predefined domain of walker, if exceed, then replace with mean value, then with data synchronously to local data base, otherwise directly with the data that collect synchronously to local data base.
Step 2: network user's behavioral data pretreatment module is identified the web log file data, filters out the user search behavioral data for the user behavior information analysis;
The web log file data are identified, screened, namely select useful user search behavioral data, get rid of useless user search behavioral data; The network user who directly collects retrieves behavioral data following several situation can occur: the page elements name that the page elements name that (1) front-end user interface shows and rear end are recorded in the daily record data is inconsistent; (2) content that relates to the user security privacy can't be collected record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thereby cause the repeatedly duplicate record of same page elements information; Inputted after the term such as the user, again term has been changed, filled in so behavior for twice of the user and all can go on record, thereby caused the situation of the retrieving information duplicate record of primary retrieval behavior.Therefore, got rid of the page elements that relates to the user security privacy in identification, when screening useful user behavior data, and made up the contrast list of the page elements that records in page elements and the daily record data, and for the phenomenon of duplicate record, the principle of taking the information with last record to be as the criterion.
Step 3: by the behavior sequence analysis user search behavioral data that extracts is converted to towards the behavioral data in behavioural characteristic space, it is stored in network user's behavioral data memory module;
When making up the user search behavior sequence, adopted the method for forward structure behavior sequence, i.e. the method for time window; The time window of a rolling is set, according to the order of occurrence of each target, the user search behavior is moved to right from the left side in the behavior coordinate.
Step 4: network user's behavioral data analysis module calls the user search behavioral data in network user's behavioral data memory module, uses the user behavior pattern method for digging of default that the user search behavioral data is analyzed;
Carry out user behavior pattern when excavating the network user being retrieved behavioral data, mainly adopted support to calculate and these two kinds of methods of correlation analysis;
It is by the support of calculating retrieval behavior to searched targets that support is calculated, and excavates the behavior pattern that those probably cause the ownership goal frequent occurrence, and computing formula is:
Wherein
Representative of consumer retrieval behavior,
Representative of consumer retrieval behavior data set is comprising target data set
With non-target data set
In like manner can draw data set
With
In cause the support of the behavior pattern of user search success and failure
With
Be respectively:
At data set
Middle support is higher, illustrates that the behavior or behavior sequence are larger on the impact of target; At data set
Middle support is lower, illustrates that the behavior or behavior sequence are larger on the impact of target.
Simultaneously, although low in order to capture those frequencies of occurrences, can produce to goal behavior the behavior of significant impact, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein
Refer to target data set,
Refer to non-target data set,
Target,
Non-target, here
, a threshold value is set
If,
, explanation
To target
Impact be greater than non-target
Impact; If
, then opposite;
In order to weigh the related intimate degree between the user search behavior, carried out correlation analysis, so that find the successful optimal path of retrieval.
Carrying out correlation analysis is to utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between the page elements; If negative correlation item is thought in P＜0; If 0＜P＜0.2 item thinks uncorrelated; If weak positive correlation item is thought in 0.2＜P＜0.4; If medium positive correlation item is thought in 0.4＜P＜0.6; If strong positive correlation item is thought in 0.6＜P＜0.8; If P〉0.8 item think extremely strong positive correlation.
Step 5: the analysis result display module the results are shown in user terminal displays device interface with the analysis of user search behavioural information.
In order to set forth more clearly the purpose, technical solutions and advantages of the present invention, the present invention will be described in detail below in conjunction with specific embodiments and the drawings.
2-6 by reference to the accompanying drawings:
2 one kinds of network user register behavioural informations of embodiment analytical method
Step 1: the program of burying that the utilization of network user's behavioral data acquisition module is integrated in is wherein obtained the website user and is registered behavioral data, wherein buries a program and is used for the monitor user ' behavioral data by the program code of the collection user behavior data that is embedded into browser end and rear end and collects the result whether unusual program code forms; When the user accesses the English enrollment page 201 of MIC, then can create a Session and produce PageId204, if the user refreshes the page, then again create PageId205, when filling in related registration information as requested, the user (comprises user name, password, the area, mailbox, Business Name etc.), the automatic recording user registration of the program code of the collection user behavior data by JavaScript programming behavioral data 206, if the user has finished the data input of predetermined amount, then with the page overall data PageInfo208 that records, packing sends to web server 209, if the user has closed the MIC enrollment page, front page overall data 208 closed in record, and data packings sent to web server 209 and it is recorded in the web log file data, the user that front end JavaScript program was collected one day registers behavioral data, to the server request of sending 301, whether unusual program code carries out the warehouse-in judgement 302 of data to start the monitor user ' behavioral data collection result that is used for who is write by java applet the rear end this moment, the data that this day gathered compare with the average data of every day before, whether the difference rate that the data that judgement gathers are compared with average data exceeds predefined domain of walker, if exceed, then replace 305 with mean value, then with data synchronously to local data base 304, otherwise directly with the data that collect synchronously to local data base 304.
Step 2: network user's behavioral data pretreatment module is identified the web log file data, and the user who filters out for the user behavior information analysis registers behavioral data;
The web log file data are identified, screened, namely select useful user behavior data, get rid of useless user behavior data; Following several situation can appear in network user's behavioral data of directly collecting: the page elements name that the page elements name that (1) front-end user interface shows and rear end are recorded in the daily record data is inconsistent; Such as page elements Full Name by name, and the field of logdata record is userName; (2) content that relates to the user security privacy can't be collected record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thereby cause the repeatedly duplicate record of same page elements information; After having filled in email address such as the user, change again email address, filled in so behavior for twice of the user and all can go on record, thereby caused this information of mailbox 2 records to occur.Therefore, got rid of the page elements that relates to the user security privacy in identification, when screening useful user behavior data, and made up the contrast list of the page elements that records in page elements and the daily record data, and for the phenomenon of duplicate record, the principle of taking the information with last record to be as the criterion.
Step 3: by the behavior sequence analysis user who extracts is registered behavioral data and convert to towards the behavioral data in behavioural characteristic space, it is stored in network user's behavioral data memory module;
Making up the method that has adopted forward to make up behavior sequence when the user registers behavior sequence, the i.e. method of time window; The time window of a rolling is set, according to the order of occurrence of each target, user's behavior of registering is moved to right from the left side in the behavior coordinate.
Step 4: the user that network user's behavioral data analysis module calls in network user's behavioral data memory module registers behavioral data, uses the user behavior pattern method for digging of default that the user is registered behavioral data analysis;
When the network user register behavioral data being carried out the user behavior pattern excavation, mainly adopted support to calculate and these two kinds of methods of correlation analysis;
It is by the support of calculating page elements to the submission behavior that support is calculated, and excavates the behavior pattern that those probably cause the ownership goal frequent occurrence, and computing formula is:
Wherein
Representative of consumer registration behavior,
Representative of consumer registration behavioral data collection is comprising target data set
With non-target data set
In like manner can draw data set
With
In cause the support of user registration success and failed behavior pattern
With
Be respectively:
At data set
Middle support is higher, illustrates that the behavior or behavior sequence are larger on the impact of target; At data set
Middle support is lower, illustrates that the behavior or behavior sequence are larger on the impact of target.
Simultaneously, although low in order to capture those frequencies of occurrences, can produce to goal behavior the behavior of significant impact, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein
Refer to target data set,
Refer to non-target data set,
Target,
Non-target, here
, a threshold value is set
If,
, explanation
To target
Impact be greater than non-target
Impact; If
, then opposite;
In order to weigh the in twos related intimate degree of element of the page, carried out correlation analysis, so that adjust page layout, optimize the interface.
Carrying out correlation analysis is to utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between the page elements; If negative correlation item is thought in P＜0; If 0＜P＜0.2 item thinks uncorrelated; If weak positive correlation item is thought in 0.2＜P＜0.4; If medium positive correlation item is thought in 0.4＜P＜0.6; If strong positive correlation item is thought in 0.6＜P＜0.8; If P〉0.8 item think extremely strong positive correlation.
Step 5: the analysis result display module the results are shown in user terminal displays device interface with what the user registered the behavioural information analysis.
Fig. 2 is that the JavaScript page front end that the website user according to the embodiment of the invention registers the behavioural information analytical system buries a program diagram.
The user accesses the English enrollment page 201 of MIC, then can create a Session and produce PageId204, if the user refreshes the page, then again create PageId205, when the user fills in related registration information as requested, the automatic recording user registration of JavaScript program behavioral data 206, if the user has finished the data input of predetermined amount, then with the page overall data PageInfo208 that records, packing sends to web server 209, if the user has closed the MIC enrollment page, front page overall data 208 closed in record, and the data packing is sent to web server 209.
Fig. 3 is that a program diagram is buried in the Java rear end that the website user according to the embodiment of the invention registers the behavior monitoring system.
Front end JavaScript buries a program and has collected a certain amount of user and register behavioral data, to the server request of sending 301, start rear end Java this moment and bury the warehouse-in judgement 302 that a program is carried out data, judge whether the data and the mean value that gather exist unusually, if exist unusual, then replace 305 with mean value, then with data synchronously to local data base 304, otherwise directly with the data that collect synchronously to local data base 304.
Referring to Fig. 4, shown that the website user according to the embodiment of the invention registers behavioural information collection and storage organization schematic diagram.
User's open any browser access MIC website 401, system judges whether the user is maiden visit MIC website 402, if the user is not maiden visit MIC, then user's registration behavioural information is not gathered; If the user is maiden visit MIC, then trigger " burying a program " 403, then implant and start JS page front end to user browser and " bury a program " 404,405 differentiate for a user behavior data amount of burying some program record, if reach predefined data volume, then the JS program will send to the website Web server with the packing of user's registration behavioral data and be stored in the local log database 406, if the data volume of record does not reach predefined data volume, then identify and judge whether the user leaves enrollment page 407, if the user has left enrollment page, judge further whether the user has closed browser 408, if user browser is closed, then as user again during open any browser 410, the packing of user's registration behavioral data sent the website Web server and be stored in the local log database 406, if the user does not leave enrollment page, although perhaps the user has left enrollment page but browser has not cut out, then the waiting system Preset Time 409, after the Preset Time, the packing of user's registration behavioral data sent the website Web server and be stored in the local log database 406;
Application server extracts web log file file 411, Java buries the rear end data in the programmed acquisition local data base and includes in the Data Warehouse for Enterprises 412 in, by analysis, after the processing, merge Java end data and JavaScript daily record data 413, and deposit the data of edit in UEAM system database 414, then system has finished the collection 415 that a time user registers behavioral data.
The embodiment of the invention has adopted the method for forward structure behavior sequence, the i.e. method of time window when identification, screening, extraction data.The goal behavior that the embodiment of the invention is paid close attention to be user registration success whether, therefore enter enrollment page from the user and leave enrollment page to the user and just consisted of a complete time window, establish target and " succeed in registration for the user
" and " registration failure
" wherein, because there are two enrollment pages in register flow path, therefore, establish two sub-goals and be respectively the page one and succeed in registration
Succeed in registration with the page two
Unsuccessful with the page one
Unsuccessful with the page two
The time window that a rolling is set comes the selection behavior, moves to right from the left side in the behavior that the occurs in coordinate of time window according to each target.No matter whether user registration success, as long as he leaves enrollment page, so before a series of behaviors of user are all said and are placed in the group.Fig. 5 is MIC user and registers the behavior sequence schematic diagram.Wherein,
Represent goal behavior, the registration behavior sequence is broken down into two sub-behavior sequences, and the sub-goal behavior is respectively
With
Represent
Individual control is filled in behavior.
Referring to Fig. 6, shown the flow chart of registering the behavior monitoring system according to the website user of the embodiment of the invention.
The system manager is by browser terminal login 601, monitoring system is returned a http response, show on keeper's browser that the user registers behavior monitoring system queries interface, wait for operation requests 603, eject prompting operation dialog box 604, need the data in the invoke user registration behavioral data memory module? if the keeper selects "No", then withdraw from monitoring system, and show the page that welcome reuses in the user browser terminal.If the user selects "Yes", at first the data in the data memory module are called in the user and register behavioral data analysis module 605, then eject the choice box 606 of data analysis algorithm types, after the keeper clicks, selection instruction Input Monitor Connector system 607 with keeper's input, the algorithm of selecting according to the keeper carries out data analysis and calculates 608, the result of data analysis is shown to terminal browser interface 609, eject prompting operation dialog box 610, whether is continuation inquired about? the keeper selects "Yes", returns 606; The keeper selects "No", then withdraws from monitoring system, and shows the page that welcome reuses at keeper's browser terminal.
For 606, the user behavior pattern method for digging that adopts in an embodiment of the present invention mainly contains two, and namely support is calculated and correlation analysis.These two kinds of methods have mainly realized the calculating of page elements to the support of submission behavior, and the analysis of the correlation between each element of the page.
In an embodiment of the present invention, support is calculated and will be excavated the behavior pattern that those probably cause the frequent occurrence of user registration success or registration failure exactly.The user has consisted of a data set in all behaviors of the English enrollment page of MIC
, it has two subsets, namely causes the user behavior data collection of user registration success
With the user behavior data collection that causes user's registration failure
At whole data set
In cause the support of the behavior pattern that goal behavior occurs
Computing formula is:
In like manner can draw data set
With
In cause the support of user registration success and failed behavior pattern
With
Be respectively:
At data set
Middle support is higher, illustrates that the behavior or behavior sequence are larger on the impact of target; At data set
Middle support is lower, illustrates that the behavior or behavior sequence are larger on the impact of target.
Simultaneously, the embodiment of the invention has also been done comparative analysis to the support that same behavior is concentrated in different pieces of information, although low in order to capture those frequencies of occurrences, can produce to goal behavior the behavior of significant impact.Data set
With
In behavior
Support contrast ratio C ontrast be:
A threshold value is set
If,
, behavior then is described
To goal behavior
Impact be greater than goal behavior
Impact; If
, behavior then is described
To goal behavior
Impact be greater than goal behavior
Impact; The embodiment of the invention arranges threshold value
, seek the behavior to different target behavior generation material impact
In order further to analyze the incidence relation between each control of enrollment page, the embodiment of the invention has been done correlation analysis to each element of MIC enrollment page.Correlation analysis refers to two or more variable elements that possess correlation are analyzed, thereby weighs the related intimate degree of two Variable Factors.
By the description of above true mode, those skilled in the art can be well understood to the present invention and can realize by the mode of software combined with hardware platform.Based on this, all or part of can the embodying with the form of software product that technical scheme of the present invention contributes to background technology, this computer software product can be stored in the storage medium, such as ROM/RAM, magnetic disc, CD etc., comprise that some instructions are in order to use a computer equipment (can be personal computer, server, perhaps the network equipment etc.) to carry out the described method of some part of each embodiment of the present invention or embodiment.
The present invention can analyze various user behavior information.
In sum, a kind of network user's behavioural information analytical system of the present invention and analytical method gather and store network user's behavioural information, the mining algorithm and the rule that arrange in advance by system draw data results, be shown to enterprise by analysis result at last and be further analyzed, thereby sum up certain business rule.Not only help the website to find timely the user behavior feature, and so that the website further perfect institutional framework, improved the reasonability of website structure.
Claims (9)
1. network user's behavioural information analytical system is characterized in that, comprises the network user's behavioral data acquisition module that connects successively, network user's behavioral data pretreatment module, network user's behavioral data memory module, network user's behavioral data analysis module, analysis result display module.
2. network user's behavioural information analytical method is characterized in that, may further comprise the steps:
Step 1: the program of burying that the utilization of network user's behavioral data acquisition module is integrated in is wherein obtained website user's behavioral data, and it is recorded in the web log file data, then whether the data of collecting in the unit interval are judged unusually, at last with data synchronously to local data base; Wherein burying a program is used for the monitor user ' behavioral data by the program code of the collection user behavior data that is embedded into browser end and rear end and collects the result whether unusual program code forms;
Step 2: network user's behavioral data pretreatment module is identified the web log file data, filters out the user behavior data for the user behavior information analysis;
Step 3: by the behavior sequence analysis user behavior data that extracts is converted to towards the behavioral data in behavioural characteristic space, it is stored in network user's behavioral data memory module;
Step 4: network user's behavioral data analysis module calls the user behavior data in network user's behavioral data memory module, uses the user behavior pattern method for digging of default that user behavior data is analyzed;
Step 5: the analysis result display module the results are shown in user terminal displays device interface with the user behavior information analysis.
3. a kind of network user's behavioural information analytical method according to claim 2, it is characterized in that, when obtaining website user's behavioral data in the step 1, utilized a program of burying, when the user logs in client browser, trigger the program code of collecting user behavior data, when user's access websites page, for the user creates a session and page number; When the user fills in relevant information as requested, collect the automatic recording user behavioral data of program code meeting of user behavior data; Simultaneously, system is provided with the minimum input of user data volume, when the user behavior data of collecting reaches the minimum input of user data volume, the page overall data packing that is recorded to is sent to Web server; Then with the user behavior data collected in the unit interval synchronously to local data base.
4. a kind of network user's behavioural information analytical method according to claim 2 is characterized in that, in the step 2 web log file data is identified, is screened, and namely selects useful user behavior data, gets rid of useless user behavior data; Following several situation can appear in network user's behavioral data of directly collecting: the first, and the page elements name that the page elements name that front-end user interface shows and rear end are recorded in the daily record data is inconsistent; The second, the content that relates to the user security privacy can't be collected record, and can produce some irrelevant gibberish; The three, one user can go on record to filling in of same page elements at every turn, thereby causes the repeatedly duplicate record of same page elements information; Therefore, got rid of the page elements that relates to the user security privacy in identification, when screening useful user behavior data, and made up the contrast list of the page elements that records in page elements and the daily record data, and for the phenomenon of duplicate record, the principle of taking the information with last record to be as the criterion.
5. a kind of network user's behavioural information analytical method according to claim 2 is characterized in that, step 3 has adopted the method for forward structure behavior sequence, the i.e. method of time window when making up the user behavior sequence; The time window of a rolling is set, according to the order of occurrence of each target, user behavior is moved to right from the left side in the behavior coordinate.
6. a kind of network user's behavioural information analytical method according to claim 2 is characterized in that, step 4 has mainly adopted support to calculate and these two kinds of methods of correlation analysis when network user's behavioral data being carried out the user behavior pattern excavation;
It is by the support of calculating page elements to goal behavior that support is calculated, and excavates the behavior pattern that those probably cause the ownership goal frequent occurrence, and computing formula is:
Wherein
The representative of consumer behavior,
Representative of consumer behavioral data collection is comprising target data set
With non-target data set
Simultaneously, although low in order to capture those frequencies of occurrences, can produce to goal behavior the behavior of significant impact, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein
Refer to target data set,
Refer to non-target data set,
Target,
Non-target, here
, a threshold value is set
If,
, explanation
To target
Impact be greater than non-target
Impact; If
, then opposite;
In order to find out the related intimate degree between user behavior, carried out correlation analysis, so that adjust page layout, optimize the interface.
7. a kind of network user's behavioural information analytical method according to claim 3, it is characterized in that, with the user behavior data collected in the unit interval during synchronously to local data base, whether unusual program is put judgement in storage to be used for monitor user ' behavioral data collection result by the rear end, judge that whether the data and the mean value that collect exist unusually, if exist unusually, then cast out the data of collecting, replace with mean value, otherwise directly with the data that collect synchronously to local data base.
8. a kind of network user's behavioural information analytical method according to claim 6 is characterized in that, carrying out correlation analysis is to utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between the page elements; If negative correlation item is thought in P＜0; If 0＜P＜0.2 item thinks uncorrelated; If weak positive correlation item is thought in 0.2＜P＜0.4; If medium positive correlation item is thought in 0.4＜P＜0.6; If strong positive correlation item is thought in 0.6＜P＜0.8; If P〉0.8 item think extremely strong positive correlation.
9. a kind of network user's behavioural information analytical method according to claim 7 is characterized in that, described is by judging with the difference rate of mean value unusually, and wherein the difference rate can be set.
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