【摘要】：互聯(lián)網(wǎng)正在從“用戶以獲取信息為主”的Web1.0時(shí)代過渡到“用戶既是網(wǎng)絡(luò)信息獲取者又是網(wǎng)絡(luò)信息制造者”的Web2.0時(shí)代。為了能夠在海量數(shù)據(jù)中發(fā)掘信息或者服務(wù),用戶畫像具有巨大的作用和價(jià)值,能夠?yàn)閭�(gè)性化搜索、個(gè)性化推薦、廣告營銷、產(chǎn)品策略以及運(yùn)營方向提供基礎(chǔ)性支持,指引方向。用戶屬性預(yù)測(cè)是用戶畫像研究的核心工作,如今用戶屬性預(yù)測(cè)的研究主要著重于單個(gè)屬性的預(yù)測(cè)模型構(gòu)建,缺乏較為完善的、全面的多個(gè)屬性同時(shí)預(yù)測(cè)的模型方法;此外,更是缺乏相應(yīng)領(lǐng)域的數(shù)據(jù)流挖掘以及概念漂移處理機(jī)制,無法實(shí)現(xiàn)用戶屬性動(dòng)態(tài)預(yù)測(cè),并且現(xiàn)有的概念漂移研究具有局限性,需要進(jìn)行相應(yīng)地改進(jìn)與加強(qiáng)。本文從以上問題入手,旨在構(gòu)建體系完備、效率與性能優(yōu)越的用戶屬性流式預(yù)測(cè)模型。在屬性預(yù)測(cè)方面,本文側(cè)重同時(shí)預(yù)測(cè)多個(gè)屬性的理念,基于多標(biāo)記學(xué)習(xí)技術(shù),采用多示例多標(biāo)記框架(MIML)將屬性預(yù)測(cè)作為一個(gè)廣義的多標(biāo)記分類研究,并且創(chuàng)新性構(gòu)建組成用戶對(duì)象的示例概念,采用聚類方法構(gòu)建示例,實(shí)現(xiàn)了能夠快速、準(zhǔn)確、同時(shí)預(yù)測(cè)多個(gè)屬性的模型構(gòu)建。不同于離線預(yù)測(cè)模型,本文創(chuàng)新性加入基于數(shù)據(jù)流挖掘技術(shù)的在線流式框架,處理用戶產(chǎn)生的在線行為與動(dòng)態(tài),側(cè)重處理數(shù)據(jù)流的各類概念漂移問題,提出了一種基于原型(Prototype-based)學(xué)習(xí)的自適應(yīng)概念漂移分類算法SyncPrototype,相較于現(xiàn)有算法,SyncPrototype在分類性能、概念漂移的響應(yīng)速度以及時(shí)間性能等方面都有明顯提高,能夠更加有效處理并適應(yīng)數(shù)據(jù)流概念漂移問題。為用戶屬性流式增量迭代方面提供了有力支持,從而實(shí)現(xiàn)用戶屬性動(dòng)態(tài)預(yù)測(cè)以及流式迭代。本文運(yùn)用基于多標(biāo)記學(xué)習(xí)的用戶屬性流式預(yù)測(cè)模型設(shè)計(jì)開發(fā)了用戶屬性認(rèn)證系統(tǒng)的數(shù)據(jù)挖掘驗(yàn)證模塊,能夠有效驗(yàn)證微博用戶所填個(gè)人信息真實(shí)性,衡量屬性可信度。
[Abstract]:The Internet is transitioning from the era of Web1.0, where users are mainly to get information, to the era of Web2.0, where users are both network information acquirers and network information makers. In order to discover information or service in mass data, user portrait has great function and value. It can provide basic support and direction for personalized search, personalized recommendation, advertising marketing, product strategy and operation direction. User attribute prediction is the core work of user portrait research. Nowadays, the research of user attribute prediction mainly focuses on the construction of a single attribute prediction model, which lacks a more perfect and comprehensive model method for simultaneous prediction of multiple attributes. There is also a lack of data stream mining and conceptual drift processing mechanism in the corresponding fields, which can not realize the dynamic prediction of user attributes, and the existing research on concept drift has limitations, so it needs to be improved and strengthened accordingly. The purpose of this paper is to construct a user attribute flow prediction model with complete system, high efficiency and superior performance. In the aspect of attribute prediction, this paper focuses on the concept of simultaneous prediction of multiple attributes. Based on multi-label learning technology, this paper uses multi-example and multi-label framework (MIML) to study attribute prediction as a generalized multi-label classification. The concept of user object is innovatively constructed and the example is constructed by clustering method. The model can be constructed quickly and accurately and can predict multiple attributes at the same time. Different from the offline prediction model, this paper creatively adds an online flow framework based on data stream mining technology to deal with the online behavior and dynamics generated by users, and focuses on dealing with various conceptual drift problems of data flow. An adaptive concept drift classification algorithm based on prototype learning (Prototype-based) is proposed. Compared with the existing algorithm, SyncPrototype, has a significant improvement in classification performance, response speed and time performance of concept drift. It can deal with and adapt to the concept drift problem of data flow more effectively. It provides powerful support for user attribute flow incremental iteration, so as to realize user attribute dynamic prediction and flow iteration. In this paper, the user attribute flow prediction model based on multi-label learning is used to design and develop the data mining verification module of user attribute authentication system, which can effectively verify the authenticity of personal information filled by Weibo user and measure the reliability of attributes.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.0;TP311.13

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