CN106204157A - Behavior processing method evaluated by a kind of brush list based on big data collection and analysis - Google Patents
Behavior processing method evaluated by a kind of brush list based on big data collection and analysis Download PDFInfo
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- CN106204157A CN106204157A CN201610583107.2A CN201610583107A CN106204157A CN 106204157 A CN106204157 A CN 106204157A CN 201610583107 A CN201610583107 A CN 201610583107A CN 106204157 A CN106204157 A CN 106204157A
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- G06Q30/00—Commerce
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- G06Q30/0609—Buyer or seller confidence or verification
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The present invention provides a kind of brush list based on big data collection and analysis to evaluate behavior processing method, comprise the following steps: step 1, data collection and analysis, step 2, extract the shopping information of the evaluated commodity of user, by the shopping information gathering the evaluated commodity of user of multiple small-sized data bases, these information is sent to a big data base, extract the Shopping Behaviors feature of user, judge；Step 3, process, according to judged result, user and shop are processed compared with prior art, the present invention has following beneficial effect: by the evaluation of big data grabber user, and the Shopping Behaviors of user is judged, the brush single file effectively finding user and shop is, thus foundation operates steadily, shopping at network safely and efficiently runs new mechanism.
Description
Technical field
The present invention is that behavior processing method evaluated by a kind of brush list based on big data collection and analysis, belongs to data and processes skill
Art field.
Background technology
The effectiveness of the sequence informations such as inquiry directly affects final sale effect, how to ensure the effective of hint information
Property, preventing the behaviors such as brush list is the important module needing in lower single process to realize, closer to the brush list of buyer's Shopping Behaviors
It is less susceptible to be recognized by the system.The single detecting system of brush of exploitation on the basis of big data uniquely can not be judged to brush single be exactly
True order.So the renewal of the single technology of brush is always round how imitating true buyer's Shopping Behaviors progress.The most popular
Brush folk prescription method comprise the distribution of brush single path, seven days spiral brush sifter single gauges rule, brush single process major control browse duration, it is whole to browse
The page, Accounting Check etc..More specifically brush folk prescription method includes: IP prevents cheating, Netclean prevents cheating, click on ratio, come
Source statistics, unique parameters, click time favorable balance and mouse value.
The such organization man of workpeople that traditional brush forms data handling process is similar in factory on production line.Each
The part work that worker is only responsible in operation.When this part completing oneself works, operation can be transmitted to down by worker
One worker.But ought Producer above to go wrong be that Producer below then cannot perform to work accordingly.If gone out
Then need to wait that whole function checks during the situations such as the longest data base querying complete.
And existing practical situation is that the real-time throughput of order data is bigger, traditional brush forms data processes service and deposits
: processing order data slow, efficiency is low and the problem of poor availability.
Summary of the invention
The deficiency existed for prior art, it is an object of the present invention to provide a kind of brush list based on big data collection and analysis
Evaluation behavior processing method, with the problem solving to propose in above-mentioned background technology.
To achieve these goals, the present invention is to realize by the following technical solutions: a kind of based on big data acquisition
Evaluate behavior processing method with the brush list analyzed, comprise the following steps:
Step 1, data collection and analysis, by the evaluation behavioural information gathering user that multiple small-sized data bases are real-time, collection
All evaluation behavioural informations of one user are sent to a big data base by all evaluation behavioural informations of unification user
In, extracting the evaluation behavior characteristics of user, set an evaluation threshold values, it is right the evaluation behavior characteristics of user and evaluation threshold values to be carried out
Ratio, the evaluation behavior to user divides；
Step 2, extracts the shopping information of the evaluated commodity of user, by the evaluated business of collection user of multiple small-sized data bases
The shopping information of product, sends these information to a big data base, extracts the Shopping Behaviors feature of user, sentence
Disconnected；
Step 3, processes, according to judged result, processes user and shop.
Further, in step 1, user evaluate number of words, the use that behavior characteristics refers to that user evaluates for commodity
Number of times that content that family is evaluated for commodity, user evaluate for commodity, user a time period for one
Number of times that number of words that commodity are evaluated, user evaluated for commodity a time period and user are for a commodity evaluation
Time；
Described evaluation threshold values refers to content, the user that number of words that user evaluates, user evaluate for commodity for commodity
The commodity of number of times evaluate for to(for) the key word derivative words of a commodity evaluation content, user, user are a time
Number of times that number of words that section is evaluated for commodity, user evaluated for commodity a time period and user are for one
The time that part commodity are evaluated；
The evaluation behavior characteristics of user is A, and evaluation threshold values is B, and the evaluation threshold values set by employing is special to the evaluation behavior of user
Levy and mate, if A>=B, using the evaluation behavior of this user as doubtful brush single file be, and carry out next step, if A<B, should
The evaluation behavior of user is as non-brush single file and deletes.
Further, in step 2, it is judged that according to being:
Mourn in silence conversion ratio, extract user and buy the conversion ratio of mourning in silence of commodity place classification, by mourning in silence of this commodity place classification
Conversion ratio conversion ratio of comprehensively mourning in silence to the corresponding classification of shopping platform contrasts；
The shop time of staying, extract in the commodity page residence time when user buys commodity, extract according to the value of commodity and purchase
Thing platform At-the-Money plays the average residence time of the commodity page, when user is bought commodity when the commodity page stops
Between, contrast with mean residence time；
Shopping-time, extraction user buys shopping-time during commodity, when extracting the concentration shopping of this commodity place classification simultaneously
Between, user is bought shopping-time during commodity, contrasts with concentrating shopping-time；
Collection accounting, extracts user and buys the collection accounting in shop, commodity place, by the collection accounting in this shop, commodity place
Comprehensively collect accounting to the corresponding classification of shopping platform to contrast；
Evaluation content, the number of words one commodity evaluated according to user, the number of times that commodity are evaluated, a time
Number of words that section is evaluated for commodity, the number of times one commodity evaluated a time period and a commodity evaluation
Time judges；
Wireless side accounting, extracts user and buys the wireless side accounting in shop, commodity place, wireless by this shop, commodity place
End accounting classification corresponding to shopping platform comprehensive wireless end accounting contrasts.
Beneficial effects of the present invention: behavior process side evaluated by a kind of based on big data collection and analysis the brush list of the present invention
Method, by the evaluation of big data grabber user, and judges the Shopping Behaviors of user, effectively finds user and shop
Brush single file is, thus foundation operates steadily, shopping at network safely and efficiently runs new mechanism, and people-oriented, benefits the whole people for structure
People's livelihood new system for health service, open masses start an undertaking, millions of people innovation innovation drive new frame, cultivate high-end intelligence, emerging prosperity
Industry development nascent state.
Detailed description of the invention
For the technological means making the present invention realize, creation characteristic, reach purpose and be easy to understand with effect, below in conjunction with
Detailed description of the invention, is expanded on further the present invention.
The present invention provides a kind of technical scheme: behavior process side evaluated by a kind of brush list based on big data collection and analysis
Method, comprises the following steps:
Step 1, data collection and analysis, by the evaluation behavioural information gathering user that multiple small-sized data bases are real-time, collection
All evaluation behavioural informations of one user are sent to a big data base by all evaluation behavioural informations of unification user
In, extracting the evaluation behavior characteristics of user, set an evaluation threshold values, it is right the evaluation behavior characteristics of user and evaluation threshold values to be carried out
Ratio, the evaluation behavior to user divides；
Step 2, extracts the shopping information of the evaluated commodity of user, by the evaluated business of collection user of multiple small-sized data bases
The shopping information of product, sends these information to a big data base, extracts the Shopping Behaviors feature of user, sentence
Disconnected；
Step 3, processes, according to judged result, processes user and shop.
In step 1, user evaluate number of words that behavior characteristics refers to that user evaluates for commodity, user for one
Number of times, user that the content of commodity evaluation, user evaluate for commodity evaluated for commodity a time period
Number of times that number of words, user evaluated for commodity a time period and the time that user evaluates for commodity；
Evaluate number of words that threshold values refers to that user evaluates for commodity, content that user evaluates for commodity, user for
Number of times that the key word derivative words of one commodity evaluation content, user evaluate for commodity, user are a time period pair
Number of times that the number of words evaluated in commodity, user evaluated for commodity a time period and user are for a business
Judge the time of valency；
The evaluation behavior characteristics of user is A, and evaluation threshold values is B, and the evaluation threshold values set by employing is special to the evaluation behavior of user
Levy and mate, if A>=B, using the evaluation behavior of this user as doubtful brush single file be, and carry out next step, if A<B, should
The evaluation behavior of user is as non-brush single file and deletes.
In step 2, it is judged that according to being:
Mourn in silence conversion ratio, extract user and buy the conversion ratio of mourning in silence of commodity place classification, by mourning in silence of this commodity place classification
Conversion ratio conversion ratio of comprehensively mourning in silence to the corresponding classification of shopping platform contrasts；
The shop time of staying, extract in the commodity page residence time when user buys commodity, extract according to the value of commodity and purchase
Thing platform At-the-Money plays the average residence time of the commodity page, when user is bought commodity when the commodity page stops
Between, contrast with mean residence time；
Shopping-time, extraction user buys shopping-time during commodity, when extracting the concentration shopping of this commodity place classification simultaneously
Between, user is bought shopping-time during commodity, contrasts with concentrating shopping-time；
Collection accounting, extracts user and buys the collection accounting in shop, commodity place, by the collection accounting in this shop, commodity place
Comprehensively collect accounting to the corresponding classification of shopping platform to contrast；
Evaluation content, the number of words one commodity evaluated according to user, the number of times that commodity are evaluated, a time
Number of words that section is evaluated for commodity, the number of times one commodity evaluated a time period and a commodity evaluation
Time judges；
Wireless side accounting, extracts user and buys the wireless side accounting in shop, commodity place, wireless by this shop, commodity place
End accounting classification corresponding to shopping platform comprehensive wireless end accounting contrasts.
As one embodiment of the present of invention: comprise the following steps:
Step 1, data collection and analysis, by the evaluation behavioural information gathering user that multiple small-sized data bases are real-time, collection
All evaluation behavioural informations of one user are sent to a big data base by all evaluation behavioural informations of unification user
In, extracting the evaluation behavior characteristics of user, set an evaluation threshold values, it is right the evaluation behavior characteristics of user and evaluation threshold values to be carried out
Ratio, the evaluation behavior to user divides.
User evaluate number of words that behavior characteristics refers to that user evaluates for commodity, user evaluates for commodity
Number of words that number of times that content, user evaluate for commodity, user evaluated for commodity a time period, Yong Hu
Number of times that one time period is evaluated for commodity and the time that user evaluates for commodity；
Evaluate number of words that threshold values refers to that user evaluates for commodity, content that user evaluates for commodity, user for
Number of times that the key word derivative words of one commodity evaluation content, user evaluate for commodity, user are a time period pair
Number of times that the number of words evaluated in commodity, user evaluated for commodity a time period and user are for a business
Judge the time of valency；
The evaluation behavior characteristics of user is A, and evaluation threshold values is B, and i.e. in user's evaluation behavior, user is for a commodity evaluation
Number of words be A, user for the content of a commodity evaluation be A, user be A, Yong Hu for the number of times of a commodity evaluation
One time period for the number of words of a commodity evaluation be A, user be A a time period for the number of times of a commodity evaluation
And user is A for time of a commodity evaluation；Evaluating in threshold values, user for the number of words of a commodity evaluation be B,
User for the content of a commodity evaluation be B, user for the number of times of a commodity evaluation be B, user is a time period
For the number of words of a commodity evaluation be B, user be B and user couple a time period for the number of times of a commodity evaluation
It is B in the time of a commodity evaluation；The evaluation behavior characteristics of user is mated by the threshold values of evaluating set by employing, if A >
=B, using the evaluation behavior of this user as doubtful brush single file is, and carries out next step, if A < B, the evaluation behavior of this user is made
It is for non-brush single file and deletes.
Step 2, is extracted the shopping information of the evaluated commodity of user, is commented by the collection user of multiple small-sized data bases
The shopping information of valency commodity, sends these information to a big data base, extracts the Shopping Behaviors feature of user, carry out
Judge；
The foundation judged is:
Mourn in silence conversion ratio, extract user and buy the conversion ratio of mourning in silence of commodity place classification, by mourning in silence of this commodity place classification
Conversion ratio conversion ratio of comprehensively mourning in silence to the corresponding classification of shopping platform contrasts, the product of general standard, staple commodities, valency
Value ratio relatively low commodity and the commodity of repeat buying, the conversion ratio of mourning in silence of these things is that comparison is high.On the contrary, it not standard
The thing of product changed, the thing that value ratio is higher conversion ratio just ratio of mourning in silence is relatively low, judges user and shop according to conversion ratio of mourning in silence
Whether paving exists brush single file is；
The shop time of staying, extract in the commodity page residence time when user buys commodity, extract according to the value of commodity and purchase
Thing platform At-the-Money plays the average residence time of the commodity page, when user is bought commodity when the commodity page stops
Between, contrasting with mean residence time, the value of commodity is the highest, just gets in the commodity page residence time when buying commodity
Long, if user buys during commodity too short in commodity page residence time, just there may be brush single file is；
Shopping-time, extraction user buys shopping-time during commodity, when extracting the concentration shopping of this commodity place classification simultaneously
Between, user is bought shopping-time during commodity, contrasts with concentrating shopping-time, the shopping-time corresponding to each commodity
Being different, some commodity were most at 23 o'clock to 0 o'clock, if no longer this time period, these commodity have been sold a lot, just
There may be brush single file is；
Collection accounting, extracts user and buys the collection accounting in shop, commodity place, by the collection accounting in this shop, commodity place
Comprehensively collect accounting to the corresponding classification of shopping platform to contrast；
Evaluation content, the number of words one commodity evaluated according to user, the number of times that commodity are evaluated, a time
Number of words that section is evaluated for commodity, the number of times one commodity evaluated a time period and a commodity evaluation
Time judges, if user has evaluation for each order, and evaluates the longest, the simplest,
Being worth the lowest commodity, just there may be brush single file is；
Wireless side accounting, extracts user and buys the wireless side accounting in shop, commodity place, wireless by this shop, commodity place
End accounting classification corresponding to shopping platform comprehensive wireless end accounting contrasts, it may be judged whether there is brush single file is.
Step 3, processes, according to judged result and the dependency rule of shopping platform, processes user and shop.
The present invention is by the evaluation of big data grabber user, and judges the Shopping Behaviors of user, effectively finds
The brush single file in user and shop is, thus foundation operates steadily, shopping at network safely and efficiently runs new mechanism, builds with artificially
This, benefit the people's livelihood new system for health service of the whole people, open that masses start an undertaking, the innovation of millions of people innovation drives new frame, cultivates high-end intelligence
Energy, the industry development nascent state of emerging prosperity.
The ultimate principle of the present invention and principal character and advantages of the present invention are more than shown and described, for this area skill
For art personnel, it is clear that the invention is not restricted to the details of above-mentioned one exemplary embodiment, and without departing substantially from the present invention spirit or
In the case of basic feature, it is possible to realize the present invention in other specific forms.Therefore, no matter from the point of view of which point, all should be by
Embodiment regards exemplary as, and is nonrestrictive, the scope of the present invention by claims rather than on state
Bright restriction, it is intended that include all changes fallen in the implication of equivalency and scope of claim in the present invention
In.Should not be considered as limiting involved claim by any reference in claim.
Although moreover, it will be appreciated that this specification is been described by according to embodiment, but the most each embodiment only wraps
Containing an independent technical scheme, this narrating mode of description is only that for clarity sake those skilled in the art should
Description can also be formed those skilled in the art through appropriately combined as an entirety, the technical scheme in each embodiment
May be appreciated other embodiments.
Claims (3)
1. behavior processing method evaluated by a brush list based on big data collection and analysis, it is characterised in that: comprise the following steps:
Step 1, data collection and analysis, by the evaluation behavioural information gathering user that multiple small-sized data bases are real-time, collection
All evaluation behavioural informations of one user are sent to a big data base by all evaluation behavioural informations of unification user
In, extracting the evaluation behavior characteristics of user, set an evaluation threshold values, it is right the evaluation behavior characteristics of user and evaluation threshold values to be carried out
Ratio, the evaluation behavior to user divides；
Step 2, extracts the shopping information of the evaluated commodity of user, by the evaluated business of collection user of multiple small-sized data bases
The shopping information of product, sends these information to a big data base, extracts the Shopping Behaviors feature of user, sentence
Disconnected；
Step 3, processes, according to judged result, processes user and shop.
Behavior processing method evaluated by a kind of brush list based on big data collection and analysis the most according to claim 1, and it is special
Levying and be: in step 1, user evaluates number of words that behavior characteristics refers to that user evaluates for commodity, user for one
Number of times, user that the content of commodity evaluation, user evaluate for commodity evaluated for commodity a time period
Number of times that number of words, user evaluated for commodity a time period and the time that user evaluates for commodity；
Described evaluation threshold values refers to content, the user that number of words that user evaluates, user evaluate for commodity for commodity
The commodity of number of times evaluate for to(for) the key word derivative words of a commodity evaluation content, user, user are a time
Number of times that number of words that section is evaluated for commodity, user evaluated for commodity a time period and user are for one
The time that part commodity are evaluated；
The evaluation behavior characteristics of user is A, and evaluation threshold values is B, and the evaluation threshold values set by employing is special to the evaluation behavior of user
Levy and mate, if A>=B, using the evaluation behavior of this user as doubtful brush single file be, and carry out next step, if A<B, should
The evaluation behavior of user is as non-brush single file and deletes.
Behavior processing method evaluated by a kind of brush list based on big data collection and analysis the most according to claim 1, and it is special
Levy and be: in step 2, it is judged that according to being:
Mourn in silence conversion ratio, extract user and buy the conversion ratio of mourning in silence of commodity place classification, by mourning in silence of this commodity place classification
Conversion ratio conversion ratio of comprehensively mourning in silence to the corresponding classification of shopping platform contrasts；
The shop time of staying, extract in the commodity page residence time when user buys commodity, extract according to the value of commodity and purchase
Thing platform At-the-Money plays the average residence time of the commodity page, when user is bought commodity when the commodity page stops
Between, contrast with mean residence time；
Shopping-time, extraction user buys shopping-time during commodity, when extracting the concentration shopping of this commodity place classification simultaneously
Between, user is bought shopping-time during commodity, contrasts with concentrating shopping-time；
Collection accounting, extracts user and buys the collection accounting in shop, commodity place, by the collection accounting in this shop, commodity place
Comprehensively collect accounting to the corresponding classification of shopping platform to contrast；
Evaluation content, the number of words one commodity evaluated according to user, the number of times that commodity are evaluated, a time
Number of words that section is evaluated for commodity, the number of times one commodity evaluated a time period and a commodity evaluation
Time judges；
Wireless side accounting, extracts user and buys the wireless side accounting in shop, commodity place, wireless by this shop, commodity place
End accounting classification corresponding to shopping platform comprehensive wireless end accounting contrasts.
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| CN107146089A (en) * | 2017-03-29 | 2017-09-08 | 北京三快在线科技有限公司 | The single recognition methods of one kind brush and device, electronic equipment |
| CN107481009A (en) * | 2017-08-28 | 2017-12-15 | 广州虎牙信息科技有限公司 | Identify that live platform supplements the method, apparatus and terminal of user with money extremely |
| CN107563814A (en) * | 2017-09-05 | 2018-01-09 | 合肥工业大学 | A kind of o2o based on customer's stream recommends method |
| CN107679870A (en) * | 2017-09-22 | 2018-02-09 | 广东欧珀移动通信有限公司 | Brush amount resource determining method and device |
| CN108171079A (en) * | 2017-12-27 | 2018-06-15 | 深圳创维－Rgb电子有限公司 | A kind of collecting method based on terminal, device, terminal and storage medium |
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| CN108550052A (en) * | 2018-04-03 | 2018-09-18 | 杭州呯嘭智能技术有限公司 | Brush list detection method and system based on user behavior data feature |
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| CN109064189A (en) * | 2018-07-13 | 2018-12-21 | 北京亚鸿世纪科技发展有限公司 | Brush list detecting and alarm device based on the detection of intensive block |
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| CN108182587A (en) * | 2018-01-29 | 2018-06-19 | 北京信息科技大学 | A kind of electric business platform brush single act detection method and system |
| CN108550052A (en) * | 2018-04-03 | 2018-09-18 | 杭州呯嘭智能技术有限公司 | Brush list detection method and system based on user behavior data feature |
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| CN108573432A (en) * | 2018-04-23 | 2018-09-25 | 重庆南米电子商务有限公司 | Transaction supervisory systems and method for e-commerce |
| CN109064189A (en) * | 2018-07-13 | 2018-12-21 | 北京亚鸿世纪科技发展有限公司 | Brush list detecting and alarm device based on the detection of intensive block |
| CN109598588A (en) * | 2018-12-04 | 2019-04-09 | 广州拓飞商贸有限公司 | A kind of online merchandise display method based on big data analysis |
| CN109829733A (en) * | 2019-01-31 | 2019-05-31 | 重庆大学 | A kind of false comment detection system and method based on Shopping Behaviors sequence data |
| CN109829733B (en) * | 2019-01-31 | 2023-02-03 | 重庆大学 | False comment detection system and method based on shopping behavior sequence data |
| CN112598431A (en) * | 2020-12-31 | 2021-04-02 | 浙江子不语电子商务有限公司 | Transaction platform list-swiping detection system |
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