An unsupervised approach to modeling personalized contexts of mobile users

• Published in: Knowledge and information systems Vol. 31; no. 2; pp. 345 - 370
• Main Authors: Bao, Tengfei, Cao, Huanhuan, Chen, Enhong, Tian, Jilei, Xiong, Hui
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.05.2012; Springer; Springer Nature B.V
• Subjects: Applied sciences; Artificial intelligence; Cellular telephones; Clusters; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Database Management; Exact sciences and technology; General aspects; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Learning; Memory organisation. Data processing; Mining; Mobile communications networks; Occupational training. Personnel. Work management; Personalized; Raw; Recognition; Regular Paper; Segments; Software; Studies; User behavior; Web content delivery; China; Mobile context modeling; Unsupervised approach; Probabilistic approach; Line segment; Customization; Cluster; Context aware; Data mining; Modeling; Remote teaching; Unsupervised learning; News; Wireless network; Mobile computing
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-011-0417-1

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 context-aware mobile services. While there are prior works 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 two methods for mining personalized contexts from context sessions. The first method is to cluster context sessions and then to extract the frequent contextual feature-value pairs from context session clusters as contexts. The second method leverages 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.