本文選題：智能手機 + 用戶特性挖掘　； 參考：《浙江大學》2017年博士論文

【摘要】：智能手機逐漸成為人們日常生活中不可或缺的一部分。作為智能手機的主體,用戶在頻繁使用手機的過程中產生了大量的個人歷史數(shù)據。這些歷史數(shù)據可以概括為以下幾種:1)位置信號,通過GPS、手機信號塔、WiFi等方式獲取的地理位置信息;2)使用信號,記錄了用戶在何時何地使用了手機做了什么;3)社交信號,隱含在CDR(call detail record),GPS,WiFi/藍牙連接以及通訊錄等數(shù)據里;4)個人行為信號,通過加速度、陀螺儀、相機等傳感器獲取�？紤]到智能手機經常被同一個用戶使用,這些歷史數(shù)據隱含了很多與用戶相關的個性化信息,例如性別,年齡,職業(yè),婚姻狀況等,也在一定程度上反應了用戶的生活習慣和興趣愛好。智能手機為推測用戶屬性與特征、理解用戶提供了新的信息渠道。通過智能手機感知數(shù)據理解用戶不僅有商業(yè)價值,并且可以幫助用戶更好地理解自已。首先,通過智能手機感知數(shù)據理解用戶有很強的商業(yè)價值,可以用來改善設備,應用和服務。例如,通過理解用戶的興趣愛好、屬性等基本信息更好地提高應用的個性化,例如,個性化網頁搜索和個性化推薦,進而提高商業(yè)利益。其次,通過手機記錄的數(shù)據來理解用戶可以幫助用戶更全面更客觀地了解自已。手機記錄的一些行為信息可以幫助用戶去客觀的了解自已,也幫助他們發(fā)現(xiàn)自已不了解的一面。另外,人們的記憶能力是有限的,而手機的記錄是無限的,可以持續(xù)長時間的記錄用戶的行為信息,從而幫助用戶全面地理解自已。用戶更全面地理解自已,可以幫助用戶改善不健康的生活習慣等,從而提高生活質量。本文基于真實的手機感知數(shù)據,以理論研究為基礎,著重從位置信息、手機App的安裝信息以及手機app的使用信息等三個方面來理解用戶的移動性、生活模式、興趣偏好及習慣等特性。考慮到移動信息揭示了用戶在日常生活中“何時”“何地”的基本要素,我們首先通過匿名WiFi掃描列推測用戶的動態(tài)屬性,移動性;其次,試圖通過手機App安裝列表挖掘用戶的靜態(tài)屬性,例如年齡、性別、興趣、偏好等;最后,我們通過手機App的使用信息去綜合理解用戶之間的相似性和差異性,并發(fā)現(xiàn)多個用戶群體的存在。我們具體研究內容與意義描述如下:(1)基于匿名WiFi掃描列表的用戶移動模式分析首先,我們試圖從匿名的WiFi掃描列表里推測用戶的移動軌跡,并在此基礎上發(fā)現(xiàn)用戶的生活方式。我們在WiFi掃描列表里提取出駐留地點之后,利用圖論知識給每個用戶建立了移動圖,以描述他/她的移動軌跡。在用戶的移動圖里,我們通過社群檢測的方法推測出用戶的活動區(qū)域。在發(fā)現(xiàn)的活動區(qū)域的基礎之上,我們定義了活躍性和多樣性兩個指標來衡量用戶的移動性。除此之外,我們識別出家庭和工作地點兩個重要的地點,并學習用戶在家和工作地點方面的生活習慣,例如,某個用戶在家待的平均時長,晚上外出的活躍性,分別在工作日和周末的工作時長等。我們在Device Analyzer數(shù)據集上驗證了我們的方法,其中Device Analyzer數(shù)據及包含了17,000多個用戶詳細的手機使用信息。(2)基于手機App安裝列表的用戶屬性挖掘除了推測用戶的動態(tài)屬性,移動性,我們還試圖通過手機app安裝列表挖掘用戶的靜態(tài)屬性,例如,性別、年齡、興趣、偏好等。我們嘗試通過用戶的手機App安裝列表去挖掘用戶的屬性。我們提出基于特定屬性的表征方法來描述用戶的特性,并且對手機app與特定的屬性之間的關系進行建模。為了驗證我們的方法,我們在一個包含100,000多用戶的手機App列表的數(shù)據集上做了很多實驗。我們的方法對于12個預定義的用戶屬性,平均等錯誤率為16.4%。據我們所知,這是第一個通過手機App安裝列表來挖掘用戶屬性的工作。(3)基于手機App使用記錄的用戶群體發(fā)現(xiàn)最后,我們試圖通過分析手機App的使用情況,綜合地理解用戶之間的差異性和相似性,從而發(fā)現(xiàn)多個用戶群體。我們分析了 106,672個安卓手機用戶持續(xù)一個月的手機App的使用信息,利用我們提出的兩步聚類法和特征排序的方法,基于手機App使用行為的相似性,發(fā)現(xiàn)了 382個明顯不同的手機用戶群體。我們的研究結果對可推廣的研究,手機應用的設計和開發(fā),不同用戶群體的手機應用預安裝的決策方面都有著深遠的意義。
[Abstract]:Smartphone has gradually become an integral part of people's daily life. As the main body of smart phones, users generate a lot of personal historical data in the process of frequent use of their mobile phones. These historical data can be summed up as follows: 1) location signals, access to geographical locations through GPS, cell phone tower, WiFi and so on. Interest; 2) using signals, recording what when and where a user has used a mobile phone; 3) social signals, hidden in data such as CDR (call detail record), GPS, WiFi/ Bluetooth connection and address book; 4) personal behavior signals, obtained by sensors such as acceleration, gyroscopes, phase machines, etc., considering that smart phones are often used by the same user, These historical data imply a lot of user related personalized information, such as sex, age, occupation, marital status and so on. It also reacts to the user's habits and interests to a certain extent. Smart phones provide new information channels to speculate on user attributes and features, understand users and understand the data through smart phones. Users not only have commercial value, but also help users to better understand themselves. First, it can be used to improve the equipment, application and service by using the smart phone to understand the user's strong business value, for example, to improve the personalization of the application by understanding the user's interests, attributes and other basic information, for example, personalization. Web search and personalized recommendation to improve business interests. Secondly, it is understood that users can help users understand themselves more comprehensively and objectively through the data recorded by the mobile phone. Some behavioral information of the mobile phone records help users to understand themselves objectively and help them find out their own side. In addition, people's records are recorded. The memory ability is limited, and the record of the mobile phone is unlimited, it can record the user's behavior information for a long time, thus helping the user to understand themselves fully. The user can understand themselves more comprehensively, can help the users to improve the unhealthy habits and so on, so as to improve the quality of life. This article is based on the real mobile phone perception data. On the basis of research, we can understand the mobility, life pattern, interest preference and habit of the user from three aspects, such as location information, the installation information of mobile phone App and the use information of mobile phone app. The name WiFi scan shows the dynamic property and mobility of the user. Secondly, it tries to excavate the static attributes of the user through the App installation list of the mobile phone, such as age, sex, interest, preference and so on. Finally, we understand the similarity and the difference between the users through the use information of the mobile phone App, and discover the existence of multiple user groups. The specific content and significance are described as follows: (1) first of all, based on anonymous WiFi scan list, we attempt to speculate the user's mobile trajectory from the anonymous WiFi scan list and discover the user's lifestyle on this basis. We use the graph theory knowledge to extract the resident location in the WiFi scan list. Each user has set up a mobile map to describe his / her movement trajectory. In the user's mobile graph, we speculate the user's active area by the method of community detection. On the basis of the discovered active area, we define two indicators of activity and diversity to measure the mobility of the user. In addition, we identify the home. There are two important locations in the court and the workplace and learn the habits of the user at home and place of work, such as the average length of a user at home, the activity of night out, and the length of work on the weekdays and weekends, respectively. We verify our methods on the Device Analyzer data set, of which the number of Device Analyzer According to and includes more than 17000 users' detailed mobile phone use information. (2) user attributes mining based on the App installation list of mobile phones, in addition to speculating the user's dynamic properties and mobility, we also try to excavate the user's static properties through the mobile app installation list, such as sex, age, interest, preference and so on. We try to use the user's mobile Ap P installation list to excavate user properties. We propose a characterization method based on specific attributes to describe user characteristics and model the relationship between mobile app and specific attributes. In order to verify our method, we have done a lot of experiments on a data set of a mobile App list containing more than 100000 users. Method for 12 predefined user attributes, the average error rate is 16.4%., as we know, this is the first work to dig user attributes through a mobile App installation list. (3) finally, based on the user group discovery of the App using the mobile phone, we try to analyze the use of the hand machine App and understand the difference in the user. We have analyzed the use of 106672 Android mobile phone users for one month, using our two step clustering method and the feature sorting method, based on the similarity of the mobile App usage behavior, and found 382 distinct group of mobile phone users. We have found 382 Android mobile phone users. The research results have far-reaching significance for the research and development of mobile phones, the design and development of mobile applications, and the pre installation decisions of different user groups.
【學位授予單位】：浙江大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP311.13;TP311.56

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相關期刊論文 前1條

1 陳龍彪;李石堅;潘綱;;智能手機:普適感知與應用[J];計算機學報;2015年02期

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本文編號：1956895