探勘智慧型手機中應用程式使用行為之研究

Abstract

隨著智慧型手機的普及，愈來愈多的行為應用程式(mobile applications)被開發及設計。使用者可以由網路上下載這些Apps來處理各種需求，例如：相機、地圖、瀏覽器、音樂播放器，…等。並且，由於手機的行動性，這些下載、執行及移除的行為可以發生在任何的時間及任何的地點。因為，智慧型手機的使用記錄便成為一個複雜的時間空間資料。在本論文中，主要將探討四個主題：(1)以個人化特徵挑選進行Apps使用預測、(2)以時間性履歷進行Apps使用預測、(3)使用者Apps動態喜好模型、(4)探勘以多維度序列為基礎之使用者Apps使用樣式。 在第一個主題中，我們將收集智慧型手機上的各種感測器，包含硬體感測器，例如：時間、加速度、地理位置、等…；軟體感測器，例如：Apps的使用順序。這些感測裝置的讀數可以有效的利用來預測使用者的Apps使用情況。然而，智慧型手機上的感測裝置非常多，收集這些讀數不只會造成儲存空間上的浪費，也會花費額外的能源來進行感測。因此，我們在這個主題中，會以個人化的觀點進行特徵挑選，只有有利於預測該使用者的Apps使用的感測裝置會進行感測。如此便用大量的降低儲存訓練資料所需的空間及感測時消耗的能量。 在第二個主題中，我們只參考時間的因素來進行Apps使用預測。我們利用傅立葉轉換來得到Apps的使用週期，接著根據這些找出來的週期為每一個App進立他的時間性履歷。此時，由於時間資訊已經被 傅立葉轉換所消除，我們必需將具有相同週期但發生在不同時間的行為區隔開。在這裡，我們利用階層式分群法來將類似的行為分為一群，並且視其為一種使用行為。最後，我們提出一個分數計算的系統，可以計算出每一個App在目前時間可能被執行的機會。但由於這個機率的運算需要對App的使用機率密度進行積分，而積分的計算對於手機來說是一項耗費能源的動作。因此，我們提出一個以柴比雪夫不等式為基礎的分數計算方式，可以不需要進行積分，便能計算出App可能被使用的機率。 在第三個主題中，我們發現使用者的喜好是會隨著時間而改變。但大部份的使用者不會對他們所下載的Apps進行評分，而不可能在他們每次改變喜好時，不停的重新評分。在這個主題中，我們以上一個時間點的喜好搭配上目前時間點的使用次數來計算目前時間點的喜好。然而，使用次數並無法完整的反應出使用者的喜好。例如：對於某些使用者來說，通訊Apps，像是Line、Whatsapp的使用次數必定比生產力工具來得多。於是，在這個主題中，我們以線性迴歸來代表使用者喜好變化的趨勢，而該使用者喜好的變化，便能以迴歸線的斜率來表示。 在第四個主題中，我們設計了一個特殊的序列樣式，稱為多維度序列樣式。由於Apps可能歸屬於各種不同的類別，因為，我們可以將Apps的使用記錄看成是一個多維度(類別)的序列。而最常出現的多維度序列樣式，便可以用來代表這個使用者的使用行為。在這裡，我們提出一個傳遞式的探勘方式，只需要在第一個維度進行序列樣式探，接著再透過傳遞的方式來組合其他維度相對應的樣式，便能組合出一個多維度的序列樣式。除此之外，我們還提出一個增加效率的資料結構，可以只找出最短的樣式，再利用此資料結構來延長樣式的長度。

Smartphones have played an important role nowadays. There are more and more mobile applications (Apps) designed for smartphones. Users could download and execute different Apps for different purposes, such as camera, maps, browser, mp3 player, and so on. Furthermore, users could buy (download), launch, close and remove Apps in any location and any time due to the powerful mobility of smartphones. Therefore, the usage behavior of smartphone obviously could be seen as a complex spatio-temporal data. In this thesis, we will focus on 1) identifying users personal features for predicting their mobile Apps usage, 2) predicting the Apps to be launched regarding the usage trace, 3) modelling the dynamic preference of Apps usage, and 4) discovering users mobile usage patterns which are represented as multi-domain sequential patterns. In the first work, we predict Apps usage for users according to their personalized features which are collected from sensors attached on smartphones. We claim that the Apps usage behavior would be affected by the hardware sensors, such as time, GPS, Accelerometer, etc. and the software sensors, such as the Apps usage sessions. Thus, we could predict user’s Apps usage in advance through collecting those sensor readings. However, to collect all of the sensors readings is impractical and inefficient. Here, we only select a set of most useful sensors for every individual user. Therefore, the training data size and the sensing energy could be reduced. In the second work, the temporal profiles is discovered for mobile Apps. We identify the periodicity of Apps via Fourier transform and consequently, the temporal profiles are thus constructed according to the usage periods of Apps. Furthermore, due to the temporal information is eliminated after we perform the Fourier transform, we have to identify the different sub-patterns which share the same period. Thus, a hierarchical clustering is adopted to group similar sub-patterns and different groups are considered asdifferent usage behaviors. Finally, we propose a scoring system based on Chebychev inequality which calculate the usage probability without performing integral on the usage density probability function. In the third work, we observe that a user’s preference to the mobile Apps (s)he has installed is dynamic. However, users seldom rate their Apps and even re-rate them when their reference is changed. In this work, we collect the mobile Apps usage trace of a user and model the current preference according to previous preference and the current usage counts. However, the usage count does not reflect the preference directly. For example, for some users, the usage count of an IM App is definitely higher than that of a productive App. Therefore, we model the usage trend by linear regression and thus the preference change is based on the slope of the regression line. In the forth work, we design a novel sequential pattern across multiple sequence databases to model the mobile Apps usage behavior and proposed an efficient algorithm, called PropagatedMine. The proposed PropagatedMine performs sequential pattern mining in one starting sequence database, and then propagate the discovered sequential patterns to other sequence databases. Furthermore, to reducing the amount of propagated patterns, a lattice structure is proposed to organize and composes multi-domain sequential patterns.