CN106980663A - Based on magnanimity across the user's portrait method for shielding behavioral data - Google Patents
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Abstract
The invention discloses it is a kind of based on magnanimity across screen behavioral data user draw a portrait method.What this method was mainly solved is in face of more and more flexible two-way new media business, in face of million grades, or even the magnanimity behavioral data of millions user, the user behavior data collected is subjected to HDFS distributed storages, data are extracted by ETL module, after conversion and loading, by user behavior data fusion content tab of the optimal combined algorithm by magnanimity for meeting media industry feature, user tag, consume label, geographical labels, device label, user property etc. carries out efficient data prediction, and ultimately form user's portrait, drawn a portrait again by the related user of WEB application routine call, accurately data supporting is provided for Broadcast Television network operators service operation.
Description
Technical field：
The present invention relates to medium field network information processing technology, it is more particularly to a kind of based on magnanimity across screen behavioral data
User's portrait method.
Background technology：
With after CHINA RFTCOM Co Ltd company translate the epoch arrival, digital television business development it is increasingly mature, pay channel, when
Be moved back to see, VOD program requests, a variety of two-way interaction new business such as other value-added services (stock, TV store, game etc.) are continuous
Enrich the business service content of Broadcast Television network operators, the development priorities of Broadcast Television network operators gradually builds from digital platform,
Bidirectional network Transformation has turned to business operation and the profit model of more diversification.
Although the important handgrip for developing into the sharp synergy of Broadcast Television network operators increasing of value-added service, is due to not several
According to supporting, lacking to understand the solid of user, the often construction and operation of value-added service and the actual demand of user exists larger
Deviation, causes business project verification without standard, and function is reached the standard grade the awkward state of no one, how to obtain user's portrait, Quan Mianzhang in net
User's potential demand is held, goes accurate service guidance to develop according to user's request, becomes the problem of operator assistant officer is to be solved.
On the other hand, Broadcast Television network operators also rest on the aspect of basic business marketing to the understanding mode of user, lead to
Cross historical development experience to judge the use habit and potential demand of user, it is difficult to quantify, which can not be CHINA RFTCOM Co Ltd fortune
Seek business's service operation and accurately data supporting is provided.
The content of the invention：
In view of this, the invention provides it is a kind of based on magnanimity across screen behavioral data user draw a portrait method.This method master
To be solved be in face of more and more flexible two-way new media business, in face of million grades, in addition millions user magnanimity behavior
Data, carry out HDFS distributed storages by the user behavior data collected, data are extracted by ETL module, changed
After loading, by meet the optimal combined algorithm of media industry feature by the user behavior data of magnanimity merge content tab,
User tag, consumption label, geographical labels, device label, user property etc. carry out efficient data prediction, and ultimately form
User draws a portrait, then is drawn a portrait by the related user of WEB application routine call, is provided precisely for Broadcast Television network operators service operation
Data supporting.
The concrete technical scheme of the present invention is as follows：
Based on magnanimity across user's portrait method of screen behavioral data, comprise the following steps：
(1) terminal data acquisition module, HDFS distributed storages module, ETL module, portrait module, WEB application mould are set
Block；
(2) terminal data acquisition module is used to gather viewing behavior data of the user in multimedia messages playback terminal, and
The data forwarding gathered is responsible for storage to HDFS distributed storage modules；
(3) HDFS distributed storages module except be responsible for storage user audience data, be also responsible for storage other the 3rd
Method, system isomeric data；
(4) ETL module be responsible for the user audience data stored is extracted from HDFS distributed storages module,
Conversion and loading, and provide infrastructure elements data for the behavior modeling module in portrait module；
(5) portrait module includes behavior modeling, portrait label, model prediction, and user draws a portrait these modules；
(6) WEB application module is the weblication that terminal is embedded, visual presentation and download for user tag.
In such scheme, the multimedia messages playback terminal includes DVB STB (DTV STB), OTT (interconnections
Net set top box), intelligent television, mobile phone, tablet personal computer.
In such scheme, other described third party system isomeric datas are these page browsing data of PV, UV.
In such scheme, the behavior modeling module in the mark portrait block to the data after ETL of upper stage to enter every trade
For modeling, to take out the portrait label of user, this stage focuses on Great possibility, arranged as much as possible by mathematical algorithm model
Except the accidental behavior of user；Behavior modeling algorithm includes, text mining, natural language processing, prediction algorithm, clustering algorithm,
Machine learning algorithm etc..
In such scheme, the portrait label model in the portrait module is formed on the basis of the reliability of the adjustment model checking
Label, which define including content tab, user property, behavior label, user tag, consumption label, geographical labels, equipment
Label；The content tab gathers EPG (electronic program list) pieces forms data by terminal acquisition module and obtained, and content tab is defined
The dimensions such as one-level label, label dimension, detailed label, the label data based on programme information is provided for algorithm processing module；Institute
The main body that user property defines label object is stated, user property basic element evidence includes Customs Assigned Number, DTV STB
The information such as MAC Address, affiliated area；The terminal device viewing behavior number that the behavior label is obtained by terminal acquisition module
According to by analyzing user audience data, obtaining the data such as user watched duration, rating number of times, the rating frequency, be at algorithm
Manage module and calculating basis is provided；The user tag defines the rating preference of user；All basic metadatas of the user tag
Come from automatic data collection and the processing of machine, gather standard criterion, whole no manual intervention is a kind of user tag of standardization
Taxonomic hierarchies；The user tag is included：Sports, film, variety entertainment, service for life, juvenile's animation, science and education, TV column
Mesh, news program, documentary film, financial finance and economics, TV play, other etc..It is described to consume the tag definition consumption preferences mark of user
Label；Consume label and include shopping category, number of visits, single-page residence time, access duration, the transaction frequency, scoring, collection
Deng；The geographical labels define user behavior historical address information；Geographical labels comprising longitude and latitude, structuring address information,
Commercial circle information etc.；The device label defines the facility information of user；Device label comprising device type, brand, model, set
Standby characteristic etc..
In such scheme, the model prediction module in the portrait module by the analysis to business, will portrait label with
Marketing Model, business model etc. are combined, and form user's value models, content temperature model consumer loyalty degree model, height body
Pattern type, customer loss model etc.；User's value models calculate the value mould based on user watched behavior by RFM models
Type；Total viewing duration in the variate-value R=nearest viewing time F=viewing frequency M=cycles；The content temperature model passes through heat
Rank algorithm is spent, the prediction of video content temperature is formed；Key index：Pageview, push up, step on, the time；In conjunction with user interest label
The weight ratio of each element, is given a mark by weighted calculation for each content, passes through fraction formation temperature list；The user is loyal
Really degree model judges the loyalty of user by business rule, portrait label, clustering algorithm；The height and weight model by using
The commodity such as family purchase clothes, footwear, cap and consumption label are judged；The customer loss model passes through user behavior label, industry
Business rule, time dimension, consumption frequency etc. are judged.
In such scheme, user's portrait module in the portrait module is the labeling system of basic forming, includes use
Family value, liveness, loyalty, at heart influence power, feature, social networks, crowd's attribute, instantly consuming capacity, demand, potential
The multistage label such as demand and multiclass classification.
By the invention described above methods described Broadcast Television network operators can be made to make full use of existing bilateral network passage to obtain
The mass users behavioral data got, merges other third party's consumption data, geodatas etc., fast and effectively obtains solid
User is drawn a portrait and accurately user's request, and Operation Decision foundation is provided for operator.It is more existing simultaneously in resource utilization
Sampling survey techniques can save substantial amounts of hardware device resources and personnel cost.
Brief description of the drawings：
The present invention is further illustrated below in conjunction with the drawings and specific embodiments.
Fig. 1 is the step block diagram of method of the present invention of being drawn a portrait based on magnanimity across the user of screen behavioral data.
Embodiment：
In order that the technical means, the inventive features, the objects and the advantages of the present invention are easy to understand, tie below
Conjunction is specifically illustrating, and the present invention is expanded on further.
As shown in figure 1, it is of the present invention based on magnanimity across screen behavioral data user draw a portrait method, first be set eventually
End data acquisition module, HDFS distributed storages module, ETL module, portrait module, WEB application module；Secondly, terminal data
Acquisition module be used for gather user multimedia messages playback terminal (including DVB STB (DTV STB), OTT (interconnection
Net set top box), intelligent television, mobile phone, tablet personal computer etc.) viewing behavior data, and by the data forwarding gathered to HDFS
Distributed storage module is responsible for storage；HDFS distributed storages module is also responsible for except being responsible for storage user audience data
Store other third party system isomeric datas (these page browsing data of PV, UV)；ETL module is responsible for from HDFS distributed storages
Module is extracted, changed and loaded to the user audience data stored, and is the behavior modeling mould in portrait module
Block provides infrastructure elements data；Module of drawing a portrait includes behavior modeling, portrait label, model prediction, and user draws a portrait these modules；
WEB application module is the weblication that terminal is embedded, visual presentation and download for user tag.
It is to be noted that the behavior modeling module in mark portrait block is built to carry out behavior to the data after ETL of upper stage
Mould, to take out the portrait label of user, this stage focuses on Great possibility, use is excluded as much as possible by mathematical algorithm model
The accidental behavior at family；Behavior modeling algorithm includes, text mining, natural language processing, prediction algorithm, clustering algorithm, machine
Learning algorithm etc..
Portrait label model in portrait module is the label formed on the basis of the reliability of the adjustment model checking, is which defined
Including content tab, user property, behavior label, user tag, consumption label, geographical labels, device label；Content tab by
Terminal acquisition module collection EPG (electronic program list) pieces forms data is obtained, content tab define one-level label, label dimension,
The dimensions such as detailed label, the label data based on programme information is provided for algorithm processing module；User property defines label pair
The main body of elephant, user property basic element evidence includes the information such as Customs Assigned Number, DTV STB MAC Address, affiliated area；
The terminal device viewing behavior data that behavior label is obtained by terminal acquisition module, by analyzing user audience data,
The data such as user watched duration, rating number of times, the rating frequency are obtained, calculating basis is provided for algorithm processing module；User tag
Define the rating preference of user；All basic metadatas of the user tag come from automatic data collection and the processing of machine, collection
Standard criterion, whole no manual intervention is a kind of user tag taxonomic hierarchies of standardization；The user tag is included：Physical culture is competing
Skill, film, variety entertainment, service for life, juvenile's animation, science and education, TV column, news program, documentary film, financial finance and economics, electricity
Depending on it is acute, other etc..Consume the tag definition consumption preferences label of user；Consume label and include shopping category, number of visits, list
Page residence time, access duration, the transaction frequency, scoring, collection etc.；Geographical labels define user behavior historical address letter
Breath；Geographical labels include longitude and latitude, structuring address information, commercial circle information etc.；Device label defines the facility information of user；
Device label includes device type, brand, model, device characteristics etc..
Model prediction module in module of drawing a portrait is by the analysis to business, by portrait label and Marketing Model, business mould
Type etc. is combined, and forms user's value models, content temperature model consumer loyalty degree model, height build model, customer loss
Model etc.；User's value models calculate the value models based on user watched behavior by RFM models；Variate-value R=most close up
See total viewing duration in the time F=viewing frequency M=cycles；Content temperature model is formed in video by temperature rank algorithm
Hold temperature prediction；Key index：Pageview, push up, step on, the time；In conjunction with the weight ratio of each element of user interest label, pass through
Weighted calculation is given a mark for each content, passes through fraction formation temperature list；Consumer loyalty degree model passes through business rule, portrait
Label, clustering algorithm judge the loyalty of user；Height and weight model buys commodity and the consumption such as clothes, footwear, cap by user
Label is judged；Customer loss model is sentenced by user behavior label, business rule, time dimension, consumption frequency etc.
It is disconnected.
User's portrait module in module of drawing a portrait is the labeling system of basic forming, includes user's value, liveness, loyalty
Sincere degree, influence power, at heart feature, social networks, crowd's attribute, consuming capacity, instantly the multistage label such as demand, potential demand and
Multiclass classification.
The data prediction behaviour that magnanimity is carried out due to employing the algorithm bag and data model of optimum organization in the above method
Make, the processing of each user tag, it is only necessary to participate in real-time fortune from the extracting data related data by data prediction
Calculate, it is not necessary to inquired about and computing from complete original magnanimity behavioral data, analytic operation efficiency is as needed for prior art
Several hours, the very long stand-by period of more than ten hour, be promoted to the second level, or even Millisecond real-time response, greatly improve
Data operation efficiency, while whole data operation process uses Machine self-learning algorithm completely, it is only necessary to common PC services
Device resource can be completed, and greatly save the input of human resources input and hardware server resource.
The general principle and principal character and advantages of the present invention of the present invention has been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the simply explanation described in above-described embodiment and specification is originally
The principle of invention, without departing from the spirit and scope of the present invention, various changes and modifications of the present invention are possible, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (7)
1. based on magnanimity across user's portrait method of screen behavioral data, its feature is with comprising the following steps：
(1) terminal data acquisition module, HDFS distributed storages module, ETL module, portrait module, WEB application module are set；
(2) terminal data acquisition module is used to gather viewing behavior data of the user in multimedia messages playback terminal, and by institute
The data forwarding of collection is responsible for storage to HDFS distributed storage modules；
(3) HDFS distributed storages module is also responsible for storing other third parties system except being responsible for storage user audience data
System isomeric data；
(4) ETL module is responsible for extracting the user audience data stored from HDFS distributed storages module, changing
And loading, and provide infrastructure elements data for the behavior modeling module in portrait module；
(5) portrait module includes behavior modeling, portrait label, model prediction, and user draws a portrait these modules；
(6) WEB application module is the weblication that terminal is embedded, visual presentation and download for user tag.
2. it is according to claim 1 based on magnanimity across screen behavioral data user draw a portrait method, it is characterised in that the multimedia
Information playback terminal includes DVB STB, OTT, intelligent television, mobile phone, tablet personal computer.
3. according to claim 1 drawn a portrait method based on magnanimity across the user of screen behavioral data, it is characterised in that it is described other the
Three method, system isomeric datas are these page browsing data of PV, UV.
4. according to claim 1 drawn a portrait method based on magnanimity across the user of screen behavioral data, it is characterised in that mark portrait
Behavior modeling module in block to the data after ETL of upper stage to carry out behavior modeling, to take out the portrait label of user,
This stage focuses on Great possibility, excludes the accidental behavior of user as much as possible by mathematical algorithm model；Behavior modeling is calculated
Method includes, text mining, natural language processing, prediction algorithm, clustering algorithm, machine learning algorithm.
5. it is according to claim 1 based on magnanimity across screen behavioral data user draw a portrait method, it is characterised in that the portrait mould
Portrait label model in block is the label that is formed on the basis of the reliability of the adjustment model checking, which define including content tab,
User property, behavior label, user tag, consumption label, geographical labels, device label；The content tab is gathered by terminal
Module collection EPG (electronic program list) pieces forms data is obtained, and content tab defines one-level label, label dimension, detailed label
These dimensions, the label data based on programme information is provided for algorithm processing module；The user property defines label object
Main body, user property basic element is according to including Customs Assigned Number, DTV STB MAC Address, affiliated area these information；
The terminal device viewing behavior data that the behavior label is obtained by terminal acquisition module, by analyzing user watched behavior number
According to obtaining user watched duration, rating number of times, the rating frequency these data, provide calculating for algorithm processing module basic；It is described
User tag defines the rating preference of user；All basic metadatas of the user tag come from automatic data collection and the place of machine
Reason, gathers standard criterion, whole no manual intervention is a kind of user tag taxonomic hierarchies of standardization；The user tag is included：
Sports, film, variety entertainment, service for life, juvenile's animation, science and education, TV column, news program, documentary film, financial wealth
Through, TV play, other these classification；It is described to consume the tag definition consumption preferences label of user；Consume label and include shopping
Category, number of visits, the single-page residence time, access duration, transaction the frequency, score, collect these classification；The geographical labels
Define user behavior historical address information；Geographical labels include longitude and latitude, structuring address information, commercial circle information these points
Class；The device label defines the facility information of user；Device label comprising device type, brand, model, device characteristics this
A little classification.
6. it is according to claim 1 based on magnanimity across screen behavioral data user draw a portrait method, it is characterised in that the portrait mould
Model prediction module in block by the analysis to business, will portrait label with Marketing Model, business model these be combined, shape
Into user's value models, content temperature model consumer loyalty degree model, height build model, customer loss model；The user
Value models calculate the value models based on user watched behavior by RFM models；The nearest viewing time F=of variate-value R=are seen
See total viewing duration in the frequency M=cycles；The content temperature model forms video content temperature pre- by temperature rank algorithm
Survey；Key index：Pageview, push up, step on, the time；In conjunction with the weight ratio of each element of user interest label, pass through weighted calculation
Given a mark for each content, pass through fraction formation temperature list；The consumer loyalty degree model is marked by business rule, portrait
Label, clustering algorithm judge the loyalty of user；The height and weight model by user buy clothes, footwear, these commodity of cap and
Consumption label is judged；The customer loss model passes through user behavior label, business rule, time dimension, the consumption frequency
These are judged.
7. it is according to claim 1 based on magnanimity across screen behavioral data user draw a portrait method, it is characterised in that the portrait mould
User's portrait module in block is the labeling system of basic forming, includes user's value, liveness, loyalty, influence power, the heart
In feature, social networks, crowd's attribute, instantly consuming capacity, the multistage label such as demand, potential demand and multiclass classification.
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