| Summary: | 博士 === 國立交通大學 === 資訊科學與工程研究所 === 101 === 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.
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