| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 99 === With the maturations of technologies about positioning, touching, and sensing, mobile applications (abbreviated as App) are developed rapidly to satisfy kinds of users' requirements. The usages of Apps are highly dependent on human behavior. As a result, analyzing App usage logs of smart phone offers a unique opportunity to discover a person's App usage behavior. In this paper, by discovering the usage behaviors on smart phones, we aim to provide a reasonable prediction on App usage intentions to avoid users from wasting time on searching wanted Apps. The goal of this paper is two-fold: (1) discovering the time-dependent Apps from App usage logs and representing the information of each time-dependent App into a temporal-profile; (2) deriving the prediction mechanisms to predict a set of time-dependent Apps that has highly intension to be used at a query time. A framework AppNow is proposed to explore a user's App usage behavior and provide a prediction on App used by the user at future time. AppNow consists of time-dependency determining phase and intension prediction phase. In time-dependency determining phase, the time-dependent features are defined to determine the time-dependency of an App. For App usage intension prediction, we generate a temporal-profile to describe its temporal information of each time-dependent App. In intension prediction phase, two score functions are proposed for evaluating the usage intension of an App at the query time. The probability-based score function derives the usage intension by the temporal-profile of an App, while the TF-IDF-based score function does by referring the temporal-profiles of multiple Apps. Comprehensive experimental results are conducted by our real dataset. The results show that AppNow can effectively and precisely predict the usage intension of Apps of a smart phone user.
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