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篇名 |
Mobile App Recommendation with Sequential App Usage Behavior Tracking
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並列篇名 | Mobile App Recommendation with Sequential App Usage Behavior Tracking |
作者 | Yongkeun Hwang、Donghyeon Lee、Kyomin Jung |
英文摘要 | The recent evolution of mobile devices and services have resulted in such plethora of mobile applications (apps) that users have difficulty finding the ones they wish to use in a given moment. We design an app recommendation system which predicts the app to be executed with high accuracy so that users are able to access their next app conveniently and quickly. We introduce the App-Usage Tracking Feature (ATF), a simple but powerful feature for predicting next app launches, which characterizes each app use from the sequence of previously used apps. In addition, our method can be implemented without compromising the user privacy since it is solely trained on the target user’s mobile usage data and it can be conveniently implemented in the individual mobile device because of its less computation-intensive behavior. We provide a comprehensive empirical analysis of the performance and characteristics of our proposed method on real-world mobile usage data. We also demonstrate that our system can accurately predict the next app launches and outperforms the baseline methods such as the most frequently used apps (MFU) and the most recently used apps (MRU). |
起訖頁 | 827-838 |
關鍵詞 | Mobile App、Recommendation system、Usage prediction、Feature extraction、Distance learning |
刊名 | 網際網路技術學刊 |
期數 | 201905 (20:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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