發(fā)布時(shí)間：2018-01-16 16:35

  本文關(guān)鍵詞：基于通信數(shù)據(jù)的用戶重要位置識(shí)別及區(qū)域功能發(fā)現(xiàn)　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 通信數(shù)據(jù) 行為特征 重要位置 區(qū)域功能

【摘要】：隨著智能手機(jī)的普及,越來越多的人習(xí)慣隨時(shí)攜帶手機(jī),而手機(jī)的功能也在不斷豐富,集成了更多的傳感器。用戶在使用智能手機(jī)時(shí),產(chǎn)生了大量的使用記錄。其中,語音通話、短信和流量數(shù)據(jù)等使用了運(yùn)營商服務(wù)的行為,會(huì)在運(yùn)營商端產(chǎn)生相應(yīng)的日志記錄。日志記錄中包括使用時(shí)間、地點(diǎn)、業(yè)務(wù)類型,但是不包括具體的通信內(nèi)容。這類數(shù)據(jù)最大的優(yōu)勢在于包含大規(guī)模的用戶和覆蓋大規(guī)模的用戶活動(dòng)空間,盡管在時(shí)間和空間上都有一定的稀疏性,還是能為用戶行為的研究提供足夠的信息。本文提出了一種基于運(yùn)營商端通信數(shù)據(jù)對(duì)用戶重要位置進(jìn)行識(shí)別的方案。該方案沒有依賴任何先驗(yàn)知識(shí),是純數(shù)據(jù)驅(qū)動(dòng)的,在通信數(shù)據(jù)本身稀疏的情況下,仍然表現(xiàn)出了不錯(cuò)的性能。首先,我們從用戶在各個(gè)位置上的通信數(shù)據(jù)中提取特征用于刻畫用戶在各個(gè)位置上的行為。然后,我們利用各個(gè)位置上得到的用戶行為特征進(jìn)行聚類分析。通過聚類分析我們發(fā)現(xiàn),用戶在大多數(shù)位置上的通信行為十分稀疏,但在某些位置上很密集,同時(shí)用戶在這些位置上表現(xiàn)出來的行為模式也各不相同。我們稱這種行為密集且行為模式各異的位置為特殊位置。根據(jù)對(duì)特殊位置的分析和友好用戶提供的信息,我們認(rèn)為用戶的重要位置就在這些特殊位置附近。其次,文中還利用友好用戶提供的住家和工作位置真值構(gòu)建了識(shí)別住家和工作位置的分類器,90%的位置預(yù)測誤差小于1600米。同時(shí),還分析了與這兩個(gè)特殊的重要位置相關(guān)的行為特征。分析結(jié)果表示,用戶0～8點(diǎn)出現(xiàn)在住家位置的概率較大,且在住家位置沒有明顯的行為;12～20點(diǎn)在工作位置的概率較大,其行為傾向于通話短信等形式的通信行為。最后,我們將識(shí)別住家及工作位置的分類器推廣到了在網(wǎng)的全量用戶,并根據(jù)得到的預(yù)測結(jié)果對(duì)上海市的居住和辦公功能的分布進(jìn)行了分析。上海市工作區(qū)域的分布相對(duì)居住區(qū)域更加集中,但是總的來說居住區(qū)域和辦公區(qū)域的分布基本重合。
[Abstract]:With the popularity of smartphones, more and more people are used to carrying mobile phones at any time, and the functions of mobile phones are becoming richer, integrating more sensors. A large number of usage records have been generated. Among them, voice calls, short messages and traffic data, which use the operator services, will generate the corresponding log records in the operator end. The log records include the time and place of use. Business types, but not specific communication content. The greatest advantage of such data is that it includes large scale users and covers large scale user activity space, although there is a certain amount of sparsity in time and space. It can provide enough information for the research of user behavior. This paper proposes a scheme to identify the important location of user based on the communication data of the operator. The scheme does not rely on any prior knowledge. It is pure data driven and still shows good performance when the communication data itself is sparse. First of all. We extract features from the user's communication data at each location to characterize the user's behavior at each location. Through clustering analysis, we find that the communication behavior of users in most locations is very sparse, but in some locations it is very dense. At the same time, the behavior patterns of users in these locations are different. We call this behavior dense and different behavior patterns as special location. Based on the analysis of the special location and the information provided by friendly users. . We think that the important location of the user is near these special locations. Secondly, we also construct a classifier to identify the home and working location using the real value of the home and working position provided by the friendly user. The position prediction error of 90% is less than 1600m. At the same time, the behavior characteristics related to these two special important positions are analyzed. And there is no obvious behavior in the house position; 1220 points in the working position of the probability is large, its behavior tends to call short message and other forms of communication behavior. Finally, we will identify the home and working location of the classifier to the full number of users in the network. According to the predicted results, the distribution of residential and office functions in Shanghai is analyzed. The distribution of working area in Shanghai is more concentrated than that in residential area. But on the whole, the distribution of living area and office area basically coincide.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.56;TN929.5

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本文編號(hào)：1433944