CN105608171A - User portrait construction method - Google Patents
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- CN105608171A CN105608171A CN201510965619.0A CN201510965619A CN105608171A CN 105608171 A CN105608171 A CN 105608171A CN 201510965619 A CN201510965619 A CN 201510965619A CN 105608171 A CN105608171 A CN 105608171A
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Abstract
The invention discloses a user portrait construction method. The method comprises the following steps: first crawling various data of an internet through distributed crawlers, and fusing and getting through to form a mass knowledge base; obtaining internet surfing logs and matching the internet surfing logs with the knowledge base to generate user base labels; constructing user attribute labels by combining the specific customer relation management (CRM) data and geographic position data of telecom operators; and carrying out weight and attenuation factor processing on the label data to generate user base portraits, and generating user portraits which accord with the industry applications and externally providing services by combining the characteristics of the industry users and the personalized data mining of the industry costumer data. The method has the advantages of providing accurate whole network user portraits for the customers, fully utilizing the internet data and providing convenience for the application services such as user analysis, product recommendation and accurate marketing.
Description
Technical field
The present invention relates to communication technical field, be specifically related to a kind of user and draw a portrait construction method.
Background technology
User's portrait, is a kind of for delineating the effective tool of targeted customer, contact user demand and design direction, and it is all widely used in each field. For example, when specific implementation, user can be drawn a portrait as the label for portraying user characteristics (tag) set, such as, comprise the static attribute such as age, sex, also comprise user's interest characteristics, as tourism, dress ornament etc. The structure of user's portrait and renewal are for the direction propagation of follow-up information, such as the orientation input of advertisement has great significance.
In current internet industry, the method that builds user's portrait is generally the behavior in station according to user, as the user behaviors log such as media classification, commodity classification of access is preserved, then, in certain hour window, travel through all User action logs, according to certain weight attenuation function, it is calculated, obtain current up-to-date user portrait. The problem which exists is that the data of obtaining are very unilateral, only has the user data in this station, and the access behavior for user on other websites is known nothing; Meanwhile, for internet industry, be difficult to accomplish the registration of user's real name, so can only guess by ASSOCIATE STATISTICS algorithm for the user such as age, sex static attribute, reliability is not high.
And the data holder's of each side user data substantially all seals at present, fail to get through and share each data silo in the whole network aspect. This just causes and cannot in network-wide basis, accurately understand user's behavior preference and in conjunction with its industry requirement, data be carried out to personalized data mining data consumer, and the user's portrait that meets sector application to generating causes certain difficulty.
Summary of the invention
While the object of the invention is to draw a portrait for above-mentioned structure user, the reasons such as data slot, data sealing cause user to draw a portrait a kind of user that the technical problem such as accurate not proposes and draw a portrait construction method. By setting up accurate the whole network unification user portrait, facilitate client to carry out the application services such as customer analysis, Products Show, precision marketing.
In order to achieve the above object, the present invention proposes a kind of user and draws a portrait construction method, comprises the steps:
Step S1: set up knowledge base: obtain internet Various types of data, and the internet data obtaining is merged and gets through formation knowledge base;
Step S2: the internet log that obtains user;
Step S3: build user base label: above-mentioned internet log is mated with described knowledge base and form user base label;
Step S4: build user property label: CRM data and geographic position data in conjunction with telecom operators build user property label;
Step S5: calculate label data: above-mentioned basic label and attribute tags are carried out to weight and the decay factor processing of label;
Step S6: generate user's portrait according to above-mentioned label data.
Further, in described step S6, can also, for industry user's requirement, carry out individuation data excavation in conjunction with industry user and industry customer's data, build the user's portrait that meets sector application.
Further, in described step S1, while obtaining internet Various types of data, adopt distributed reptile to crawl mode, described distributed reptile adopts master slave mode to dispose. The URL crawl task that main controlled node arranges user is distributed to each reptile node, and reptile node is responsible for concrete Web page download parsing task.
Further, in described step S1, when internet data fusion is got through, first to internet data refinement classification, then carry out the automatic merger of label, classification is unified.
Further, in described step S5, when label is carried out to weight processing, total label weight adopts following formula to calculate: W=catalyst weight * behavior weight * visiting frequency, described catalyst weight refers to the importance of its differentiation user characteristics of different web sites, and behavior weight refers to the different behaviors of user for the importance of distinguishing user intent.
Further, in described step S5, when label is carried out to decay factor processing, adopt following attenuation function formula to calculate: Wts=W*exp (k (d-ds)), wherein, W is total label weight, and speed is forgotten in k representative, and d-ds refers to the time span of user behavior time of origin to current time.
Further, described basic label comprises media label, buys label, searches for label, industry label.
Further, described attribute tags comprise user's sex, age bracket, type, enliven geographical position, set meal rate.
Further, described internet data comprises portal website, video website, electric business website, tour site, forum, microblogging, micro-letter.
Compared with prior art, advantage of the present invention and good effect are:
Model mass knowledge storehouse, in conjunction with the thesaurus in knowledge base, can carry out the fusion of automation class target label for different websites and get through, and can be cross-domain in network-wide basis, set up user tag across fixed network mobile, from multiple dimensions, user characteristics is portrayed;
Simultaneously in conjunction with operator's data, comprise user's static data (sex, age bracket, terminal, set meal grade etc.), and location information data accurately portrays user tag, make user tag more comprehensively abundant, for client provides accurate the whole network user portrait.
Brief description of the drawings
Fig. 1 is that in embodiment, user draws a portrait construction method flow chart;
Fig. 2 is distributed reptile structure deployment diagram in embodiment;
Fig. 3 is the whole network user data management platform structure schematic diagram in embodiment;
Fig. 4 is real-time bid advertisement flow chart in embodiment.
Detailed description of the invention
The invention provides a kind of user and draw a portrait construction method, adopt a kind of new mentality of designing to build data label, overcome existing user data fragmentation drawback. Consider that operator is as basic data operator, in its intelligent pipeline, included all behaviors that user occurs on the internet, by analysis user in mobile terminal, the internet behavior of fixed network end can analyze user's online preference; Meanwhile, will accurately supplementing as user tag after the desensitization of the information such as the call daily record of operator itself, geographical position signaling, real name registration. Given this, the present invention proposes following mentality of designing:
First crawl internet Various types of data by distributed reptile and merge to get through and form mass knowledge storehouse, then generate user base label by the internet log obtaining is mated with knowledge base, and build user property label in conjunction with the distinctive Customer Relation Management (CRM) data of telecom operators and geographic position data; And can do personalized data mining in conjunction with industry user's feature and industry customer's data, generate and meet user's portrait of sector application and service is externally provided. Below in conjunction with specific embodiment, the present invention is described further:
A kind of user draws a portrait construction method, with reference to figure 1, comprises the steps:
Step S1: set up knowledge base: obtain internet Various types of data, and the internet data obtaining is merged and gets through formation knowledge base;
Step S2: the internet log that obtains user;
Step S3: build user base label: above-mentioned internet log is mated with described knowledge base and form user base label;
Step S4: build user property label: CRM data and geographic position data in conjunction with telecom operators build user property label;
Step S5: calculate label data: above-mentioned basic label and attribute tags are carried out to weight and the decay factor processing of label;
Step S6: generate user's portrait.
In step S1, while obtaining internet Various types of data, adopt distributed reptile to crawl mode, described distributed reptile adopts master slave mode to dispose, with reference to figure 2, URL (the UniformResourceLocator that main controlled node arranges user, URL) crawl task is distributed to each reptile node, and reptile node is responsible for concrete Web page download parsing task, and main controlled node carries out load balancing according to the loading condition of each working node. Meanwhile, this kind of mode has good scalability, in the time of system overload, shares by increasing reptile node the task of crawling. Reptile is realized constantly automatically upgrading of climbed content by carrying out timed task.
The various internet datas that obtain comprise following several large class: portal website, video website, electric business website, tour site, forum, microblogging, micro-letter etc. Now, because data volume is huge, for ease of building user tag, when internet data fusion is got through, first to internet data refinement classification, then carry out the automatic merger of label, classification is unified. For example, portal website's knowledge base will refine to the thinnest one-level classification, taking Sina as example, will realize such as " science and technology "-" internet " two-stage label; Video website will refine to concrete certain program details, as " TV play "-" continent play "-" thinkling sound Ya list "-" acting the leading role director "; Electricity business website will refine to concrete commodity details, as " large household electrical appliances "-" washing machine "-" Haier "-" XQG70-B12866 "-" 7kg/ kilogram of Full-automatic variable-frequency mute rotary drum washing machine, price 2199 ". Due to the classification disunity of each website, (for example clothes and clothes all represent a class things, but when merger, can cause data lengthy and tedious, difficult treatment), therefore in knowledge base, set up thesaurus, all kinds of internet datas are done and merged while getting through, first carry out the automatic merger of label according to thesaurus, classification is unified; Fraction that may be remaining can not merger classification, appends by carrying out automatically new label after artificial participation inspection, greatly alleviates workload. For the crawl data of mobile application end, such as APP application etc., due to cannot refinement, need artificial packet capturing to sort out, and the classification label that above reptile crawls is doing after mating and will form user media label and purchase label with internet log.
In the present invention, same user also can get through as QQ, the ID of Taobao by third party's account in fixed network and the behavior on mobile. user's online can leave various account information, as information such as No. QQ, mailbox number, electric business's account, and especially No. QQ and electric business's account, it is feasible carrying out based on this type of account that user data gets through. internet behavior by third party's account identification user on this third party's media domain, for cross-domain internet behavior, mobile terminal internet log carries out cross-domain tracking by information such as mobile devices number, for fixed network internet log, carry out the cross-domain tracking of coarseness by online account and equipment for surfing the net feature, because the same apparatus characteristic of same account may exist multiple users' possibility, identify to a certain extent single people's behavior by mining algorithm, then pass through the such third party's account of QQ as bridge, get through with the Internet data of fixed network mobile, and then calculating user is at the label data of the whole network.
Because operator is as basic data operator, it has included all behaviors that user occurs on the internet, for example, in the time that mobile device is provided, understand the static attribute information such as feedback user sex, age end; Therefore CRM data and geographic position data in conjunction with telecom operators build user property label, by the comprehensive analysis to data such as user's internet behavior, static registration information, geographical position, multiple dimensions are carried out basic label to user and are portrayed, and label are enriched more perfect.
In the present embodiment, static attribute information, as having label of one's own after the user profile desensitizations such as age bracket, sex, terminal; Geographical position label is by the position range occurring in each time period according to base station location signaling counting user; Search label forms search label by carrying out participle according to user at the search content of each large search engine.
Above each basic label and attribute tags structure complete after, need to carry out weight and the decay factor processing of label. Weight is divided into catalyst weight and behavior weight, and catalyst weight refers to the importance of its differentiation user characteristics of different web sites, and behavior weight refers to that the different behaviors of user are for the importance of distinguishing user intent. Buy cosmetics as example taking user, certain well-known high-end cosmetics website and certain cosmetics prefecture, common electric business website, the former catalyst weight is higher. Catalyst weight adopts normalized, and its weight of the catalyst of highest weighting is 1, and other catalyst weights set gradually as compared with fractional value by distinguishing the importance degree of user characteristics. User is browsing and adding shopping cart behavior to compare for commodity in electric business website, the latter's behavior weight is higher, behavior weight adopts normalized equally, and its weight of the behavior of highest weighting is 1, and other behavior weights set gradually as compared with fractional value according to distinguishing the importance of user intent. Meanwhile, user is more frequent for the access of certain things, and its label weight is also higher, and when label is carried out to weight processing, total label weight adopts following formula to calculate: W=catalyst weight * behavior weight * visiting frequency.
As time goes on, label is before more and more less to active user's feature differentiation role, therefore needs to carry out attenuation processing. When label is carried out to decay factor processing, adopt following attenuation function formula to calculate: Wts=W*exp (k (d-ds)), wherein, W is total label weight, and speed is forgotten in k representative, and d-ds refers to the time span of user behavior time of origin to current time.
Above-mentioned label weight and decay factor thereof are calculated and are all run on Hadoop cluster, carry out timed task every day, and Output rusults will be stored in real-time KV database and call for the external world. User data label is finally stored in unified user data platform and externally provides data, services with the form of open interface, can realize round-the-clock, whole network, distributed earth crawls internet Various types of data, carry out semantic analysis, text mining based on these data, thereby generate the basic label of each dimension of user.
For industry customer, for meeting client's customization demand, can customize digging user label and portrait for it, in described step S6, can also carry out individuation data excavation in conjunction with industry user and industry customer's data, carry out the relationship maps of supplementary and basic label and client's label of basic label, the final data, services providing to user is the data label that meets client's service needed, builds the user's portrait that meets sector application. For example, taking real estate client as example, real estate client has a large amount of user profile, the purpose of for example purchasing house, house-purchase time etc.; With real estate Communication with Customer, age bracket, media interest, search interest, the basic labels such as interest, residential area, working region of doing shopping in conjunction with user carry out machine learning algorithm excavation, export the purpose of just buying house, improve the user tag such as house purpose, high-end house purpose portrait. Because the user profile of real estate client is constantly updated, the algorithm that upgrades in time, output customer satisfaction user's portrait, for Products Show, advertisement putting are provided convenience, market more targeted.
In addition, draw a portrait construction method for user proposed by the invention, with reference to figure 3, set up the whole network user data management platform, comprising: base module: obtain the Various types of data of internet by distributed reptile, and data fusion is got through to formation knowledge base; Log acquisition module: in order to obtain user's internet log information; User tag builds module: by base module and log acquisition module data coupling, build user tag; Tag data process module: carry out the processing of weight and decay factor in order to the user tag that user tag is built to module construction; Finally draw a portrait generation module by user and generate the whole network unification user portrait, when generating user and drawing a portrait, can excavate for corresponding industry user side data, there is industry characteristic. Wherein, described user tag builds module and comprises that user base label builds module and user property label builds module, in order to build respectively user base label and user property label; While setting up attribute tags, the CRM data and the geographic position data that combine telecom operators build, described basic label comprises media label, buys label, searches for label, industry label etc., and described attribute tags comprises user's sex, age bracket, type, enlivens geographical position, set meal rate etc.
The whole network user data management platform adopts high concurrent front-end server interface, while access services device during for third party's calling data, and treatment effeciency is high. The weight of above-mentioned user tag and decay factor thereof are processed and are all run on Hadoop cluster, carry out timed task every day, Output rusults will be stored in real-time KV database and call for the external world, realize round-the-clock, whole network, distributed earth crawls internet Various types of data, and the unified portrait of structure the whole network user is more abundant and comprehensive.
Taking accurate advertisement marketing as example: as shown in Figure 4, be real-time bid advertisement flow chart, wherein, RTB: bid in real time; DSP: party in request's platform, represents that advertiser bids in real time to advertisement exposure each time; SSP: supplier's platform, gather the advertisement position of all kinds of online media sites, current ad spot information is passed to advertisement transaction platform; AdExchange: advertisement transaction platform, the final platform occurring of advertisement bidding transaction; DMP: data management platform
In the time of user's browsing media, media can send to AdExchange by SSP by user identifier and ad spot information, the latter can send to advertising message numerous DSP, DSP is according to current advertisement position context and active user's information, determine whether and how current advertisement exposure bidded by operation machine learning algorithm, if bidded successfully, advertisement will be thrown on the current media advertisement position, website of browsing of user. In the situation that there is no the whole network user data management platform, only depending on the user data of DSP oneself accumulation is the bitty data slot of user, cannot portray complete portrait label to user, the algorithm of bidding has also just lacked most important data supporting, this can cause advertisement cannot precisely render to target group, and cannot precisely bid. In conjunction with the whole network user data management platform, between DSP and DMP, can, by unique ID (as MEID) identification, clearly see clearly user behavior preference, thereby can find accurately target group to carry out advertisement bidding input, promote advertising results.
To sum up, user tag construction method proposed by the invention, model mass knowledge storehouse, can carry out the fusion of automation class target label gets through for different websites, can be cross-domain in network-wide basis, set up user tag portrait across fixed network mobile, from multiple dimensions, user characteristics is portrayed; And can, in conjunction with operator's data, user's static data accurately be portrayed. Meanwhile, according to client's industry requirement, the algorithm customizing excavates, the user tag portrait that output is met customer need. For client provides accurate the whole network user portrait, based on this, facilitate client to carry out the application services such as customer analysis, Products Show, precision marketing.
The above; it is only preferred embodiment of the present invention; it is not the restriction of the present invention being made to other form; the equivalent embodiment that any those skilled in the art may utilize the technology contents of above-mentioned announcement to be changed or be modified as equivalent variations is applied to other field; but every technical solution of the present invention content that do not depart from; any simple modification, equivalent variations and the remodeling above embodiment done according to technical spirit of the present invention, still belong to the protection domain of technical solution of the present invention.
Claims (9)
1. a construction method for user's portrait, is characterized in that, comprises the steps:
Step S1: set up knowledge base: obtain internet Various types of data, and the internet data obtaining is merged and gets through formation knowledge base;
Step S2: the internet log that obtains user;
Step S3: build user base label: above-mentioned internet log is mated with described knowledge base and form user base label;
Step S4: build user property label: CRM data and geographic position data in conjunction with telecom operators build user property label;
Step S5: calculate label data: above-mentioned basic label and attribute tags are carried out to weight and the decay factor processing of label;
Step S6: generate user's portrait.
2. the construction method of user's portrait according to claim 1, it is characterized in that, in described step S6, can also be for industry user's requirement, carry out individuation data excavation in conjunction with industry user and industry customer's data, build the user's portrait that meets sector application.
3. the construction method of user's portrait according to claim 2, is characterized in that, in described step S1, while obtaining internet Various types of data, adopts distributed reptile to crawl mode, and described distributed reptile adopts master slave mode to dispose.
4. the construction method of user's portrait according to claim 3, is characterized in that, in described step S1, when internet data fusion is got through, first to internet data refinement classification, then carries out the automatic merger of label, and classification is unified.
5. the construction method of user's portrait according to claim 4, it is characterized in that, in described step S5, when label is carried out to weight processing, total label weight adopts following formula to calculate: W=catalyst weight * behavior weight * visiting frequency, described catalyst weight refers to the importance of its differentiation user characteristics of different web sites, and behavior weight refers to the different behaviors of user for the importance of distinguishing user intent.
6. the construction method of user's portrait according to claim 5, is characterized in that, in described step S5, when label is carried out to decay factor processing, adopts following attenuation function formula to calculate: Wts=W*exp (k (d-ds)), wherein, W is total label weight, and speed is forgotten in k representative, and d-ds refers to the time span of user behavior time of origin to current time.
7. the construction method of user's portrait according to claim 6, is characterized in that, described basic label comprises media label, buys label, searches for label, industry label.
8. the construction method of user according to claim 7 portrait, is characterized in that, described attribute tags comprises user's sex, age bracket, type, enlivens geographical position, set meal rate.
9. the construction method of user's portrait according to claim 8, is characterized in that, described internet data comprises portal website, video website, electric business website, tour site, forum, microblogging, micro-letter.
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