探勘手機應用程式的使用模式以預測其使用行為

Abstract

隨著行動應用程式 (簡稱Apps) 迅速發展，使用者可以下載或執行這些 Apps 以滿足及方便生活上的需求。Apps的使用紀錄 (Apps usage logs) 可以反應使用者的使用行為。給定Apps usage logs，我們的目標是探勘使用者使用應用程式的使用模式 (Apps usage pattern)，其使用模式可以代表使用者使用Apps的行為。產生Apps usage logs，為了節省手機的使用電量，只有Apps被使用的時間會被紀錄，換句話說，只有跟時間相關的資訊會被紀錄。因為Apps usage logs 跟時間相關，所以我們定義了Apps usage pattern是由三種在時間上的特性所組成的，其三種特性為：global-frequency, temporal-frequency和periodicity。Global-frequency指的是在所收集到的Apps usage logs的時間內，其一個App的總使用次數，temporal-frequency指的是有些Apps在某些特定的時間區間很常被使用，而periodicity則是指Apps週期性的使用。透過這些在時間上使用Apps的特性來預測使用者使用Apps的行為。我們定義其預測使用者使用Apps行為的問題為：給定一個查詢時間和預查詢多少Apps的數量 (number of K Apps)，產生使用者在這個時間點最有可能會使用的top K Apps。根據所探勘出的Apps usage pattern，我們提出了兩個預測演算法：na□ve prediction algorithm和adaptive prediction algorithm。針對每個App，透過我們所開發的Apps usage probability model將所具有的特性轉換成相對應的使用機率，然後再利用我們所提出的prediction algorithm來選擇top K Apps。為了評估我們所提出的方法，在實驗上我們使用了兩組real datasets。實驗的結果可證實我們提出探勘Apps usage pattern的方法是有效率且準確的，另外也證實了我們提出的兩個預測演算法可以很準確的預測使用者使用Apps的行為。

Due to the proliferation of mobile applications (abbreviated as Apps) on mobile devices, users can download and execute Apps to facilitate their life. Clearly, Apps usage logs on mobile devices reflect users’ behavior. Given Apps usage logs, we intend to mine Apps usage patterns, which refers how and when Apps are used. To save the energy consumption for Apps usage logs generation, Apps usage logs usually record when Apps are executed. In other words, only temporal information is collected in Apps usage logs. With only temporal information is available in Apps usage logs, for each App, its usage pattern consists of three features: global-frequency, temporal-frequency, and periodicity. Explicitly, the global frequency of Apps refers the number of executions from the global view of Apps usages, the temporal-frequency of Apps is used to capture the execution distribution of Apps within a pre-defined time slot, and the periodicity is to identify whether Apps is periodically executed or not. In light of the three features of Apps, we address the mobile Apps usage prediction problem. Given a query time and the number of Apps, denoted as K, the top K Apps that are likely to be executed at the query time are generated. Based on Apps usage patterns, we propose two prediction algorithms: naive prediction algorithm and adaptive prediction algorithm. In particular, we derive the probability model for each feature in Apps usage patterns and give a set of Apps with their features, the above two algorithms could select top K Apps. To evaluate our proposed methods for mining Apps usage patterns and two proposed prediction algorithms, two real mobile Apps usage datasets are used. The experiment results show that our proposed methods can discover the Apps usage patterns effectively and our proposed prediction algorithms are able to accurately predict the Apps, and demonstrate the advantage of using Apps usage patterns for mobile Apps prediction.