發(fā)布時(shí)間：2018-03-17 03:29

  本文選題：轉(zhuǎn)發(fā)行為預(yù)測(cè)　切入點(diǎn)：重疊社交圈發(fā)現(xiàn)　出處：《吉林大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：影響信息傳播過程的關(guān)鍵因素之一是網(wǎng)絡(luò)結(jié)構(gòu),由于用戶是社會(huì)網(wǎng)絡(luò)中的行動(dòng)主體,因此,用戶行為亦影響著信息的傳播過程,有效地分析并挖掘社會(huì)網(wǎng)絡(luò)中的用戶行為背后蘊(yùn)藏的深層次規(guī)律有助于深入理解社會(huì)網(wǎng)絡(luò)的形成及演化機(jī)制。本文針對(duì)社會(huì)網(wǎng)絡(luò)中用戶行為分析及預(yù)測(cè)的相關(guān)問題,展開了深入研究,主要研究?jī)?nèi)容包括:(1)針對(duì)用戶轉(zhuǎn)發(fā)信息的行為,通過量化用戶社會(huì)關(guān)系因子約束目標(biāo)函數(shù),將預(yù)測(cè)問題轉(zhuǎn)化為求解用戶概要和用戶發(fā)布內(nèi)容兩個(gè)維度的最優(yōu)解問題,提出一種基于加權(quán)非負(fù)矩陣分解的用戶轉(zhuǎn)發(fā)行為預(yù)測(cè)算法;(2)針對(duì)用戶自定義社交圈的行為,通過重新定義密度估計(jì)函數(shù)和增加社交圈整合步驟,提出一種改進(jìn)的基于密度點(diǎn)聚類的重疊社交圈發(fā)現(xiàn)方法;(3)基于垃圾用戶和正常用戶不同的行為,通過重新定義親和力度量標(biāo)準(zhǔn)、多樣化的親和力閾值以及標(biāo)準(zhǔn)正態(tài)分布變異因子,提出一種基于改進(jìn)人工免疫算法的垃圾用戶識(shí)別模型;(4)基于五大人格特質(zhì)所表現(xiàn)出的用戶行為,通過將動(dòng)態(tài)的用戶人格特質(zhì)閾值以及關(guān)于用戶人格特質(zhì)間相關(guān)性的先驗(yàn)知識(shí)引入多標(biāo)記分類算法中,提出一種基于改進(jìn)ML-KNN算法的多維用戶人格特質(zhì)識(shí)別模型。在多個(gè)公用數(shù)據(jù)集上的實(shí)驗(yàn)表明,本文提出的方法能獲得較好的效果。
[Abstract]:One of the key factors affecting the process of information dissemination is the network structure. As users are the main actors in social networks, user behavior also affects the process of information dissemination. It is helpful to understand the formation and evolution mechanism of social network by analyzing and mining the deep-seated law of user behavior in social network. This paper aims at the related problems of user behavior analysis and prediction in social network. The main contents of this study include: 1) quantifying the objective function of user social relationship factor, according to the behavior of transmitting information. In this paper, the prediction problem is transformed into the optimal solution problem of two dimensions of user summary and user content, and a user forwarding behavior prediction algorithm based on weighted non-negative matrix decomposition is proposed, which is aimed at the behavior of user-defined social circle. By redefining the density estimation function and increasing the social circle integration step, an improved social circle discovery method based on density point clustering is proposed, which is based on different behaviors of garbage users and normal users. By redefining affinity metrics, diverse affinity thresholds, and standard normal distribution variation factors, A garbage user identification model based on improved artificial immune algorithm (AIA) is proposed. By introducing dynamic user personality trait threshold and prior knowledge about the correlation between user personality traits into multi-marker classification algorithm, A multi-dimensional user personality recognition model based on improved ML-KNN algorithm is proposed. Experiments on several common data sets show that the proposed method can achieve good results.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09;TP18
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本文編號(hào)：1622985