توضیحات
ABSTRACT
Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile
environment, which is critical for the success of contextaware mobile services. While there are prior work on mobile
context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users
INTRODUCTION
Recent years have witnessed a revolution in mobile devices, which is driven by the ever-increasing needs of mobile
services. As mobile services keep evolving, there are clear signs that context modeling of mobile users will have huge demand. A distinct property of mobile users is that they are usually exposed in volatile contexts, such as waiting abus, walking in a building, driving a car, or doing shopping.Thus, building context-aware services by leveraging the rich contextual information of mobile users has attracted the great attention of many researchers [2], [11], [17]. Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is a fundamental research problem towards leveraging the rich contextual information of mobile users There are prior work on mobile context modeling such as[1], [17]. However, most of these previous studies have a need to predefine the typical contexts of users and predetermine the corresponding rules for detecting them
Year : 2010
Publisher : IEEE International Conference on Data Mining
By : Tengfei Bao , Happia Cao , Enhong Chen , Jilei Tian , Hui Xiong
File Information : English Language / 10 Page / Size : 242 KB
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سال : 2010
ناشر : IEEE International Conference on Data Mining
کاری از : Tengfei Bao , Happia Cao , Enhong Chen , Jilei Tian , Hui Xiong
اطلاعات فایل : زبان انگلیسی / 10 صفحه / حجم : 242 KB
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