Mining Temporal Profiles of Mobile Applications for Usage Prediction Zhung-Xun Liao ∗ , Po-Ruey Lei † , Tsu-Jou Shen ∗ , Shou-Chung Li ∗ and Wen-Chih Peng ∗ ∗ Department of Computer Science, National Chiao Tung University, HsinChu, Taiwan Email: {zxliao.cs96g, acrt3820, dreampilot.cs00g, wcpeng}@nctu.edu.tw † Department of Electrical Engineering, Chinese Naval Academy, Kaohsiung, Taiwan Email: kdboy1225@gmail.com Abstract—Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users’ Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. AppNow consists of three components, the usage logger, the temporal profile constructor and the Apps predictor. First, the usage logger records every App start time. Then, the temporal profiles are built by applying Discrete Fourier Transform and exploring usage periods and specific times. Finally, the system calculates the usage probability at current time for each App and shows a list of Apps with highest probability. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage. Keywords-mobile application; temporal profile; prediction; data mining; I. I NTRODUCTION Smart phones have became an important smart device in people’s daily life. We use them to communicate with friends, check emails, take pictures, and play games. Con- currently, we can install many kinds of mobile applications (abbreviated as Apps) in our smart phone and invoke them for individual purposes. However, according to our observa- tion, the average number of Apps of each device is about 70 to 80 and that of some devices which even exceed 150. We realize that as the number of Apps in a user’s smart phone increases, users will spend an increasing amount of time looking for and launching the Apps they want to use. To deal with this problem, we have designed an AppNow widget which can dynamically predict users’ App usage through mining temporal profiles from the users’ previous usage behavior. For example, Figure 1 shows different pre- diction results at different times in one day. In Figure 1(a), the time is 9:00 a.m. and AppNow shows that the user is intending to start work by checking calender, emails, and so on. In Figure 1(b), the time goes to 12:30 p.m. and AppNow indicates that the user is about to communicate with friends using social network services. In Figure 1(c), the user is likely to play games at home when the time is 8:30 p.m. However, there are two challenges when designing the AppNow widget: 1) connecting the relation between Apps and their launched times, and 2) calculating the usage probability through comparing the App launched times and current time. First, Apps are not always launched at the same time. For example, if a user checks Facebook approximately once every one and a half hours, the usage time could be around 9:00, 10:30, 12:00, and so on. Therefore, to connect the relation between time and Apps usage, we proposed a temporal profile to summarize the usage history of each App. Second, since the launched times of an App may not exactly match the current time, we have to model the usage probability over the time different between the launched times and current time. To the best of the authors’ knowledge, although there are many research works focusing on smart phones [1], [2], [3], [4], [5], [6], [7], [8], there are no existing works that explore predicting usage behavior, let alone developing a widget on smart phones. On the other hand, current prediction algorithms on location, purchasing, and co-authoring [9], [10], [11], [12] do not create the relation with the aspect of time, such that they cannot be applied to solve the novel problem of predicting the App usages. A. System Framework The system flow of the AppNow widget is shown in Figure 2, where AppNow possesses three main components, a usage logger, a temporal profile constructor and an App usage predictor. The usage logger records the launched time and App ID on every App launch. The temporal profile constructor builds a temporal profile for each App. We summarize and investigate the usage history for each App into a set of (period, {specific time}) tuples which is, therefore, called a temporal profile for that App. The usage predictor calculates the probability of using each App at the current time. The AppNow widget then shows the 4 Apps with the highest probability. 2012 IEEE 12th International Conference on Data Mining Workshops 978-0-7695-4925-5/12 $26.00 © 2012 IEEE DOI 10.1109/ICDMW.2012.11 890 2012 IEEE 12th International Conference on Data Mining Workshops 978-0-7695-4925-5/12 $26.00 © 2012 IEEE DOI 10.1109/ICDMW.2012.11 890