社會化媒體內(nèi)容關(guān)注度分析與建模方法研究
發(fā)布時間：2019-06-05 17:46
【摘要】：社會化媒體近年來得到極大發(fā)展，已經(jīng)在整個互聯(lián)網(wǎng)中占據(jù)主流地位。根據(jù)世界著名流量統(tǒng)計網(wǎng)站Alexa的數(shù)據(jù)，全球訪問量排名前十的網(wǎng)站中，有五個是社會化媒體網(wǎng)站。社會化媒體的空前發(fā)展和應(yīng)用，孕育了大量新的研究領(lǐng)域，比如催生了新的信息技術(shù)研究，促進了針對人類社會行為規(guī)律的理論研究。2009年Science雜志發(fā)表了題為《計算社會學》的文章，標志著計算科學和社會科學的交叉領(lǐng)域已成為國際前沿研究熱點，而社會關(guān)注度是其中最為重要的研究領(lǐng)域之一。社會關(guān)注度分布及動態(tài)增長特性的研究不僅能夠加深對人類宏觀行為規(guī)律的理解，而且對于理解和提升諸如預取緩存、P2P網(wǎng)絡(luò)、搜索引擎和推薦系統(tǒng)的性能具有重要的理論價值。本文在社會關(guān)注度分布特征分析、社會關(guān)注度傳播過程特性、基于社會關(guān)注度分布特性的預取緩存技術(shù)以及提高社會關(guān)注度方法等問題上進行了深入的研究。 首先，分析了多來源社會關(guān)注度分布的若干特征以及各來源對社會關(guān)注度分布的影響。社會化媒體內(nèi)容規(guī)模巨大，并且具有高度動態(tài)性和高度分散性的特點，可能使得傳統(tǒng)的分布模型和預測方法失效。本文從全局和局部兩個層面同時對多來源社會關(guān)注度整體分布特征進行了分析，發(fā)現(xiàn)了全局和局部社會關(guān)注度分布的差異。在此基礎(chǔ)上，深入分析了不同來源對社會關(guān)注度分布的影響，，結(jié)果表明搜索引擎和推薦系統(tǒng)是社會關(guān)注度的兩大主要來源，并且搜索引擎傾向于加劇“馬太效應(yīng)”，而推薦系統(tǒng)則有助于減輕“馬太效應(yīng)”。該研究成果有助于回答學術(shù)界所廣泛關(guān)心的搜索引擎和推薦系統(tǒng)如何影響被觀看媒體內(nèi)容多樣性的問題。 其次，提出了基于用戶行為模型聚類(Clustered User Behavior Model, CUBM)的媒體對象預取緩存方法。本文借助PlanetLab平臺測量和分析了社會化多媒體網(wǎng)站在傳送大尺寸多媒體對象時出現(xiàn)頻繁中斷的問題，論述了采用預取緩存技術(shù)的必要性。在此基礎(chǔ)上，提出一種基于用戶行為模型聚類(CUBM)的媒體對象預取緩存方法。該方法將行為模式類似的用戶歸類并分別建立Markov鏈，克服了傳統(tǒng)方法未能體現(xiàn)用戶差異以及在局部代理部署時覆蓋率不高的缺點，并且抓住了活躍用戶比不活躍用戶傾向于觀看更多內(nèi)容的事實，從而提高了預取的準確率和命中率。 再次，提出了基于隨機游走的社會關(guān)注度傳播模型(Random Walk based PopularityPropagation Model, RWPPM)。為了深入理解媒體對象如何通過媒體對象關(guān)系網(wǎng)影響對方的社會關(guān)注度，本文提出了一個基于隨機游走的社會關(guān)注度傳播模型。隨后分析了模型的收斂條件，論述了模型的功能并驗證了模型的正確性。在此基礎(chǔ)上，運用RWPPM模型對YouTube視頻網(wǎng)絡(luò)中視頻間社會關(guān)注度的相互影響力及其特征進行了分析。 最后，提出了一種基于KTK (Keywords-Topics-Keywords)關(guān)鍵詞推薦的社會關(guān)注度提高方法。分析了媒體對象標識文本關(guān)鍵詞在搜索引擎檢索和推薦系統(tǒng)推薦媒體對象中的重要性。進而研究了媒體對象關(guān)系網(wǎng)的簇結(jié)構(gòu)以及各簇主要關(guān)鍵詞代表話題的能力。在此基礎(chǔ)上，提出一種遵循“關(guān)鍵詞—主題—關(guān)鍵詞”思路，兼顧相關(guān)度和社會關(guān)注度的KTK關(guān)鍵詞推薦算法。最后，實驗結(jié)果表明所推薦關(guān)鍵詞能夠大幅提高媒體對象的社會關(guān)注度。
[Abstract]:The social media has been greatly developed in recent years and has been in the mainstream in the whole Internet. According to Alexa's data from the world's well-known traffic statistics website, there are five social media sites in the top 10 of the world's top-ranked websites. The unprecedented development and application of the social media has given birth to a large number of new research fields, such as the emergence of new information technology research and a theoretical study on the law of human society's behavior. In 2009, the Science magazine published an article entitled "Computing sociology>," It is one of the most important research fields in which the cross-cutting area of computing science and social science has become a hot spot in the international front, and the degree of social attention is one of the most important research fields. The study of the distribution of social attention and the characteristics of dynamic growth not only can deepen the understanding of the law of human macro-behavior, but also has important theoretical value to understand and improve the performance of such as pre-fetch cache, P2P network, search engine and recommendation system. In this paper, the characteristics of the distribution of the social attention, the characteristics of the communication process of the social attention, the prefetch buffer technology based on the distribution characteristics of the social attention and the methods of improving the social attention are deeply studied in this paper. First of all, the characteristics of the distribution of the multi-source social attention and the shadow of the distribution of the social attention of each source are analyzed. In response, the content of the social media is large, and it has the characteristics of high dynamic and high dispersivity, which can make the traditional distribution model and the prediction method to be lost. In this paper, the overall distribution characteristics of the multi-source social attention are analyzed from both the global and the local aspects, and the difference between the global and local social attention distribution is found. On this basis, the influence of different sources on the distribution of social attention is analyzed. The results show that the search engine and the recommendation system are the two main sources of the social attention, and the search engine tends to aggravate the "Matthew's effect", and the recommendation system will help to reduce the "big>" Matthew's effect ". The research results help to answer the question of how the search engine and the recommended system of the academic community have an impact on the diversity of the content of the media to be viewed Secondly, a media object pre-fetch delay based on the user behavior model (CUBM) is proposed. In this paper, using the PlanetLab platform to measure and analyze the problem of frequent interruption of the social multimedia web site in the transmission of large-size multimedia objects, this paper discusses the use of the prefetch buffer technology. In this paper, a kind of media object pre-fetching buffer based on user behavior model clustering (CUBM) is put forward. The method comprises the following steps of: classifying and establishing a Markov chain by a user similar to the behavior mode, the fact that the accuracy of pre-fetching is improved and In this paper, a random walk-based social attention propagation model (R) is presented. WPPM (WPPM). In order to understand how the media objects influence the social attention of each other through the media object relationship network, this paper puts forward a social concern based on random walk In this paper, the convergence condition of the model is analyzed, the function of the model is discussed, and the model is verified. On the basis of this, the mutual influence and characteristics of the social attention of the video in the YouTube video network by the RWPPM model Finally, a society based on KTK (Keyworld-Topics-Keywords) keyword is proposed. The method for improving the attention degree is analyzed, and the media object identification text keyword is analyzed in a search engine search and recommendation system recommendation medium The importance of the object is also studied. The cluster structure of the media object and the key words of each cluster are also studied. On the basis of this, this paper puts forward a kind of KTK which follows the "Key words and key words" of thinking, takes into account the degree of correlation and the degree of social attention. Finally, the experimental results show that the recommended keywords can greatly improve the media pair.
【學位授予單位】：哈爾濱工程大學
【學位級別】：博士
【學位授予年份】：2012
【分類號】：TP393.0
本文編號：2493710
[Abstract]:The social media has been greatly developed in recent years and has been in the mainstream in the whole Internet. According to Alexa's data from the world's well-known traffic statistics website, there are five social media sites in the top 10 of the world's top-ranked websites. The unprecedented development and application of the social media has given birth to a large number of new research fields, such as the emergence of new information technology research and a theoretical study on the law of human society's behavior. In 2009, the Science magazine published an article entitled "Computing sociology>," It is one of the most important research fields in which the cross-cutting area of computing science and social science has become a hot spot in the international front, and the degree of social attention is one of the most important research fields. The study of the distribution of social attention and the characteristics of dynamic growth not only can deepen the understanding of the law of human macro-behavior, but also has important theoretical value to understand and improve the performance of such as pre-fetch cache, P2P network, search engine and recommendation system. In this paper, the characteristics of the distribution of the social attention, the characteristics of the communication process of the social attention, the prefetch buffer technology based on the distribution characteristics of the social attention and the methods of improving the social attention are deeply studied in this paper. First of all, the characteristics of the distribution of the multi-source social attention and the shadow of the distribution of the social attention of each source are analyzed. In response, the content of the social media is large, and it has the characteristics of high dynamic and high dispersivity, which can make the traditional distribution model and the prediction method to be lost. In this paper, the overall distribution characteristics of the multi-source social attention are analyzed from both the global and the local aspects, and the difference between the global and local social attention distribution is found. On this basis, the influence of different sources on the distribution of social attention is analyzed. The results show that the search engine and the recommendation system are the two main sources of the social attention, and the search engine tends to aggravate the "Matthew's effect", and the recommendation system will help to reduce the "big>" Matthew's effect ". The research results help to answer the question of how the search engine and the recommended system of the academic community have an impact on the diversity of the content of the media to be viewed Secondly, a media object pre-fetch delay based on the user behavior model (CUBM) is proposed. In this paper, using the PlanetLab platform to measure and analyze the problem of frequent interruption of the social multimedia web site in the transmission of large-size multimedia objects, this paper discusses the use of the prefetch buffer technology. In this paper, a kind of media object pre-fetching buffer based on user behavior model clustering (CUBM) is put forward. The method comprises the following steps of: classifying and establishing a Markov chain by a user similar to the behavior mode, the fact that the accuracy of pre-fetching is improved and In this paper, a random walk-based social attention propagation model (R) is presented. WPPM (WPPM). In order to understand how the media objects influence the social attention of each other through the media object relationship network, this paper puts forward a social concern based on random walk In this paper, the convergence condition of the model is analyzed, the function of the model is discussed, and the model is verified. On the basis of this, the mutual influence and characteristics of the social attention of the video in the YouTube video network by the RWPPM model Finally, a society based on KTK (Keyworld-Topics-Keywords) keyword is proposed. The method for improving the attention degree is analyzed, and the media object identification text keyword is analyzed in a search engine search and recommendation system recommendation medium The importance of the object is also studied. The cluster structure of the media object and the key words of each cluster are also studied. On the basis of this, this paper puts forward a kind of KTK which follows the "Key words and key words" of thinking, takes into account the degree of correlation and the degree of social attention. Finally, the experimental results show that the recommended keywords can greatly improve the media pair.
【學位授予單位】：哈爾濱工程大學
【學位級別】：博士
【學位授予年份】：2012
【分類號】：TP393.0
【引證文獻】
相關(guān)碩士學位論文 前2條
1 羅躍紅;“高度關(guān)注”下鄉(xiāng)村艾滋病人社會行動策略研究[D];華中師范大學;2013年
2 羅燕妮;社會化媒體環(huán)境中的口碑傳播研究[D];華南理工大學;2013年
本文編號：2493710
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