基于社交大數(shù)據的用戶信用畫像方法研究
發(fā)布時間：2018-08-08 11:42
【摘要】：近年來,移動互聯(lián)網和社交媒體異軍突起,逐步取代了傳統(tǒng)的博客、BBS論壇,成為人們進行社交、學習、娛樂的主要平臺。與此同時,伴隨著整個社會對于互聯(lián)網、特別是移動互聯(lián)網的廣泛接受,被連接的用戶數(shù)以及用戶產生數(shù)據(UGC),呈現(xiàn)出爆發(fā)式增長。較之前的互聯(lián)網媒體技術(諸如郵件、論壇、博客),社交媒體記錄的數(shù)據類型更加豐富、及時,時效性更強。特別的,微博類型的社交媒體數(shù)據,成為了信息發(fā)布、用戶間互動、事件發(fā)現(xiàn)擴散的公眾平臺。作為一種允許公開訪問、內容簡短、近乎實時、海量規(guī)模的典型大數(shù)據,微博等社交媒體數(shù)據獲得了學術界以及工業(yè)界研究者的廣泛關注。為了充分利用這一數(shù)據源,學術界在社交網絡理論、用戶行為模式、公共事件發(fā)展規(guī)律、謠言發(fā)現(xiàn)檢測方法的等方面開展了廣泛的研究�？傮w而言,社交媒體大數(shù)據中蘊含的寶貴信息、知識需要新穎的數(shù)據處理、分析方法來解決。但是,社交媒體大數(shù)據的長度短、質量差、變化迅速、相關性弱這些問題也形成了新的挑戰(zhàn)和問題,使得傳統(tǒng)的數(shù)據挖掘方法無能為力。為了應對社交媒體大數(shù)據中"序列性"、"行為性"、"多源性"等挑戰(zhàn),更好的實現(xiàn)基于社交數(shù)據的用戶信用畫像這一目標,本文進行了微博類型數(shù)據的高效序列挖掘算法、基于微博用戶隱行為模式的用戶信用畫像、基于特征設計和集成學習融合多源信息的用戶信用畫像三個方面的研究。除此之外,在微博數(shù)據下用戶信用畫像算法的研究過程中,對于社交大數(shù)據上用戶畫像算法、技術做出研究歸納和展望。具體而言,本論文的主要研究內容、創(chuàng)新點和學術貢獻包括以下三方面:1)微博數(shù)據是以時間線(Timeline)的形式呈現(xiàn)給用戶,本質上是一種事件類型的序列數(shù)據。事件序列數(shù)據挖掘除了考慮項目(item)出現(xiàn)頻率外,也開始考慮項目的效用(utility),進而實現(xiàn)高效用事件片段挖掘。針對單條事件序列的高效用挖掘,本文提出了對現(xiàn)有高效用片段(high utility episode)挖掘算法的多重優(yōu)化策略,使得算法運行速度和內存效率都得到大規(guī)模的提升。更重要的一點,本文引入的詞法序前綴樹挖掘框架具有更緊的剪枝閾值估計,使得針對事件序列的高效用事件片段挖掘算法變得快速而實用(第三章)。2)微博數(shù)據中每一條微博,包含了限定長度的用戶產生文本內容,也包含了與用戶行為相關的上下文信息。文本和行為兩種數(shù)據源可以同時為用戶信用建模提供數(shù)據支持,但是簡單特征抽取然后組合的做法,難以發(fā)現(xiàn)用戶文本和行為數(shù)據之間的緊相關性。為了實現(xiàn)基于行為模式的用戶信用畫像,本文通過概率圖模型的建模方法,將可觀察的用戶文本和多種行為特征融合,從而獲取為信用預測提供輸入的用戶隱行為模式。本文設計的概率主題模型LUBD-CM,假設一條微博由同一個主題產生,并且微博上行為數(shù)據和文本數(shù)據都受所分配主題約束。實驗結果表明,LUBD-CM這一模型相對LUBD-CM的簡化變種,傳統(tǒng)的LDA,樸素Bayes算法,對于用戶信用標簽的預測性能都有大幅提升(第四章)。3)社交平臺上的用戶數(shù)據,除了用戶產生內容,也包括用戶個人信息、社交網絡關系。不同來源的用戶社交數(shù)據,蘊含了不同類型的與用戶信用相關的信息。但是,微博社交數(shù)據的"即時性"導致數(shù)據質量一般很低,難以作為標準分類器如SVM、決策樹的輸入并獲得較高的用戶標簽預測性能。為了融合多源異構社交數(shù)據中信用畫像有效信息,本文從個人信用相關的領域知識入手,廣泛分析多種可能的特征設計方案,從中選擇較好的社交特征,并且利用雙層集成學習框架,全面挖掘隱含在多種社交特征中的有效信息,從而實現(xiàn)綜合堆棧方法、提升方法和集成方法的用戶信用畫像預測系統(tǒng)(第五章)。值得一提的是,本文提出的針對微博社交數(shù)據的系列數(shù)據挖掘、用戶畫像方法,對于用戶產生的其他類型社交大數(shù)據(如Facebook數(shù)據,微信數(shù)據),很大程度上也是適用的。本文雖然重點研究了用戶信用屬性的預測和畫像,所提出新方法、新技術對于其他類型個人標簽(如年齡、性別、婚否)的預測,也都是適用的。
[Abstract]:In recent years, mobile Internet and social media have sprung up, gradually replacing traditional blogs, BBS forums, as the main platform for people to socialize, learn, and entertain. At the same time, with the wide acceptance of the whole society, especially the mobile Internet, the number of connected users and the user generated data (UGC). Explosive growth. Compared with previous Internet media technologies (such as mail, forums, blogs), social media records are more rich in data, timely and more timeliness. In particular, micro-blog type social media data has become a public platform for information release, interuser interaction, and event discovery diffusion. In order to make full use of this data source, the academic community has carried out a wide range of social network theory, user behavior patterns, public event development rules, and rumor discovery detection methods, in order to make full use of this data source. In general, the valuable information contained in the large data of social media requires new data processing and analytical methods to solve them. However, new challenges and problems have been formed by the short length, poor quality, rapid change and weak correlation of the social media data, which makes the traditional data mining methods incapable. In response to the challenges of "sequence", "behavioural" and "multi source" in social media data, the goal of a user credit portrait based on social data is better implemented. This paper carries out an efficient sequence mining algorithm for micro-blog type data, based on the user's credit portrait of the implicit behavior model of micro-blog users, based on feature design and integrated learning fusion. Research on three aspects of user credit portrait of multi source information. In addition, in the research process of user credit image algorithm under micro-blog data, this paper makes a summary and prospect for the user portrait algorithm on social large data. In particular, the main research content, innovation and academic contributions of this paper include the following three aspects: 1) Micro-blog data is presented to the user in the form of time line (Timeline), which is essentially an event type sequence data. Event sequence data mining, in addition to considering the frequency of the project (item), also starts to consider the utility of the project (utility), and then implements efficient fragment mining. This paper proposes a multiple optimization strategy for the existing high utility episode mining algorithm, which makes the algorithm running speed and memory efficiency improved on a large scale. More important, the word sequence prefix tree mining framework introduced in this paper has a tighter pruning threshold value estimation, which makes the event sequence efficient use of events. Fragment mining algorithm becomes fast and practical (third chapter).2) every micro-blog in micro-blog data contains text content and context information related to user behavior. Text and behavior two data sources can provide data support for user's credit model at the same time, but simple feature extraction is then used. In order to realize the user's credit picture based on the behavior pattern, this paper, through the modeling method of the probability graph model, combines the observable user text with a variety of behavior features to obtain the user's implicit behavior pattern which provides input for the prediction of the letter. The probabilistic theme model, LUBD-CM, is designed to assume that a micro-blog is generated by the same topic and that both the behavior data and the text data on micro-blog are constrained by the assigned topic. The experimental results show that the LUBD-CM model is a simplified variant of the LUBD-CM, the traditional LDA, and the simple Bayes algorithm, for the prediction performance of the user credit label. Promotion (fourth chapter).3) user data on social platform, except user generated content, including user personal information, social network relationship. Different sources of user social data contain different types of information related to user credit. However, the "immediacy" of micro-blog social data causes the data quality to be generally very low and difficult to be used as a standard. Quasi classifier such as SVM, the input of the decision tree and the higher user tag prediction performance. In order to fuse the effective information of the credit picture in the multi-source heterogeneous social data, this paper, starting with the personal credit related domain knowledge, analyzes a wide variety of possible feature design schemes to select better social features and use the double layer integration. Learning framework, fully mining the effective information hidden in a variety of social characteristics, so as to realize the comprehensive stack method, the promotion method and the integration method user credit picture prediction system (fifth chapter). It is worth mentioning that the series data mining for micro-blog social data, the user portrait method and the user generated by this paper His type of social data (such as Facebook data, WeChat data) is largely applicable. Although this paper focuses on the prediction and portrait of the user's credit attributes, the new method is also applicable to other types of personal tags such as age, sex, or marital status.
【學位授予單位】：中國科學技術大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP311.13
本文編號：2171683
[Abstract]:In recent years, mobile Internet and social media have sprung up, gradually replacing traditional blogs, BBS forums, as the main platform for people to socialize, learn, and entertain. At the same time, with the wide acceptance of the whole society, especially the mobile Internet, the number of connected users and the user generated data (UGC). Explosive growth. Compared with previous Internet media technologies (such as mail, forums, blogs), social media records are more rich in data, timely and more timeliness. In particular, micro-blog type social media data has become a public platform for information release, interuser interaction, and event discovery diffusion. In order to make full use of this data source, the academic community has carried out a wide range of social network theory, user behavior patterns, public event development rules, and rumor discovery detection methods, in order to make full use of this data source. In general, the valuable information contained in the large data of social media requires new data processing and analytical methods to solve them. However, new challenges and problems have been formed by the short length, poor quality, rapid change and weak correlation of the social media data, which makes the traditional data mining methods incapable. In response to the challenges of "sequence", "behavioural" and "multi source" in social media data, the goal of a user credit portrait based on social data is better implemented. This paper carries out an efficient sequence mining algorithm for micro-blog type data, based on the user's credit portrait of the implicit behavior model of micro-blog users, based on feature design and integrated learning fusion. Research on three aspects of user credit portrait of multi source information. In addition, in the research process of user credit image algorithm under micro-blog data, this paper makes a summary and prospect for the user portrait algorithm on social large data. In particular, the main research content, innovation and academic contributions of this paper include the following three aspects: 1) Micro-blog data is presented to the user in the form of time line (Timeline), which is essentially an event type sequence data. Event sequence data mining, in addition to considering the frequency of the project (item), also starts to consider the utility of the project (utility), and then implements efficient fragment mining. This paper proposes a multiple optimization strategy for the existing high utility episode mining algorithm, which makes the algorithm running speed and memory efficiency improved on a large scale. More important, the word sequence prefix tree mining framework introduced in this paper has a tighter pruning threshold value estimation, which makes the event sequence efficient use of events. Fragment mining algorithm becomes fast and practical (third chapter).2) every micro-blog in micro-blog data contains text content and context information related to user behavior. Text and behavior two data sources can provide data support for user's credit model at the same time, but simple feature extraction is then used. In order to realize the user's credit picture based on the behavior pattern, this paper, through the modeling method of the probability graph model, combines the observable user text with a variety of behavior features to obtain the user's implicit behavior pattern which provides input for the prediction of the letter. The probabilistic theme model, LUBD-CM, is designed to assume that a micro-blog is generated by the same topic and that both the behavior data and the text data on micro-blog are constrained by the assigned topic. The experimental results show that the LUBD-CM model is a simplified variant of the LUBD-CM, the traditional LDA, and the simple Bayes algorithm, for the prediction performance of the user credit label. Promotion (fourth chapter).3) user data on social platform, except user generated content, including user personal information, social network relationship. Different sources of user social data contain different types of information related to user credit. However, the "immediacy" of micro-blog social data causes the data quality to be generally very low and difficult to be used as a standard. Quasi classifier such as SVM, the input of the decision tree and the higher user tag prediction performance. In order to fuse the effective information of the credit picture in the multi-source heterogeneous social data, this paper, starting with the personal credit related domain knowledge, analyzes a wide variety of possible feature design schemes to select better social features and use the double layer integration. Learning framework, fully mining the effective information hidden in a variety of social characteristics, so as to realize the comprehensive stack method, the promotion method and the integration method user credit picture prediction system (fifth chapter). It is worth mentioning that the series data mining for micro-blog social data, the user portrait method and the user generated by this paper His type of social data (such as Facebook data, WeChat data) is largely applicable. Although this paper focuses on the prediction and portrait of the user's credit attributes, the new method is also applicable to other types of personal tags such as age, sex, or marital status.
【學位授予單位】：中國科學技術大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP311.13
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