個人特徵發現之手機應用程式預測

Abstract

隨著越來越多的手機應用軟件的開發，它們正密切地融入人們的日常生活中。在本論文中，我們發展了一個基於當前設備狀態以預測智能手機上最有可能被使用的應用軟件的框架。這樣的應用程序使用的預測框架對於加速應用程式發動，智能手機的用戶體驗，和電源管理都是不可或缺的條件.透過真實的應用程序使用日誌數據的分析，我們發現兩種特徵數值：從內置的感測器所測得讀數，稱為顯性特徵（EF），以及從應用程序使用關係轉換的數值，稱為隱性特徵。IF特徵數值是從建構應用程序使用轉變的應用程序使用圖（簡稱ＡＵＧ）的模型而推衍。鑒於ＡＵＧ的圖形，我們能夠發現應用程序之間的關係。由於用戶可能有不同的在智能手機的使用行為，我們進一步提出一個個人化的特徵選擇演算法。從訓練數據裡，我們探索最低描述長度（MDL），並選擇需要較少位元數來形容訓練數據資料的特徵。個人特徵選擇演算法，可以成功地減少日誌的大小和預測所花費的時間。最後，我們採用KNN分類模型來預測應用程序的使用。需要注意的是當我們使用k近鄰分類器時，我們只需要保留通過個人化特徵選擇演算法的特徵，這不僅可以降低預測的時間又能避免多維度所帶來的缺點。最後，我們做了在真實行動應用程式的數據集的一個完整綜合的實驗研究。得到的結果表示，我們提出的框架是有效的並顯示其在應用程式使用上的預測的能力。

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduce the prediction time and avoid the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.