發(fā)布時間：2018-07-01 15:24

  本文選題：社交網(wǎng)絡(luò) + 時間間隔�。� 參考：《中北大學(xué)》2017年碩士論文

【摘要】：隨著Web2.0技術(shù)的推廣應(yīng)用,涌現(xiàn)出各類在線社交網(wǎng)站。借助這些社交網(wǎng)站,用戶可以分享內(nèi)容、表達(dá)觀點、建立私密關(guān)系等,因而社交網(wǎng)站對豐富人們的情感、文化和娛樂等需求起到了很好的作用。與此同時,用戶在社交網(wǎng)站上也留下大量的行為痕跡�；谶@些行為信息,挖掘用戶在線行為規(guī)律,預(yù)測用戶在線行為,對輿情分析、網(wǎng)絡(luò)安全、社會安全、信息推薦和商品營銷等領(lǐng)域都具有極其重要的意義。本畢業(yè)論文就是要利用某高校用戶對社交網(wǎng)絡(luò)的訪問數(shù)據(jù),分析用戶的社交網(wǎng)絡(luò)訪問行為特性并揭示用戶在線行為背后的內(nèi)在機理。具體研究工作及貢獻(xiàn)包括:(1)基于個體層面和群體層面分析了用戶連續(xù)兩次在線訪問行為之間的時間間隔分布,研究了用戶時間間隔序列的相關(guān)性和活躍性等特征,并揭示了其背后的內(nèi)在機制;(2)分析了用戶訪問行為中的記憶特性,發(fā)現(xiàn)用戶的在線行為具有較強的短記憶性,其分布服從高斯分布。并據(jù)此建立了馬爾科夫過程模型,用于解釋用戶訪問行為中的記憶特性;(3)基于上述所發(fā)現(xiàn)的訪問行為特性,本文進行了用戶訪問行為的時間序列預(yù)測研究。針對用戶歷史訪問數(shù)據(jù),采用ARIMA模型和Holt-Winters三參數(shù)指數(shù)平滑法對點擊流時間序列進行預(yù)測分析,通過建立模型來預(yù)測數(shù)據(jù)的未來走向,并分析比較兩模型的優(yōu)劣。
[Abstract]:With the popularization and application of Web 2.0 technology, various online social networking sites have emerged. With the help of these social networking sites, users can share content, express their opinions, establish private relationships and so on, so social networking sites play a good role in enriching people's emotional, cultural and entertainment needs. At the same time, users on social networking sites also leave a lot of behavior traces. Based on these behavioral information, it is of great significance for the analysis of public opinion, network security, social security, information recommendation and commodity marketing to excavate the rules of online behavior of users and predict the online behavior of users. The purpose of this thesis is to analyze the characteristics of users' social network access behavior and reveal the underlying mechanism of users' online behavior by using the data of users' access to social networks in a certain university. The specific research works and contributions are as follows: (1) based on the individual level and the group level, this paper analyzes the time interval distribution between two continuous online access behaviors, and studies the characteristics of the correlation and activity of the user time interval series. It also reveals the internal mechanism behind it. (2) We analyze the memory characteristics of user access behavior and find that the online behavior of users has strong short memory and its distribution is distributed from Gao Si. The Markov process model is established to explain the memory characteristics of user's access behavior. (3) based on the characteristics of user's access behavior, the time series prediction of user's access behavior is studied in this paper. The Arima model and Holt-Winters' three-parameter exponential smoothing method are used to predict and analyze the click-stream time series. The future trend of the data is predicted by establishing the model, and the advantages and disadvantages of the two models are analyzed and compared.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.0

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本文編號：2088212