What constitutes relevant information to an individual may vary widely under different contexts. However, previous work on pervasive information systems has mostly focused on context-aware delivery of application-specific information. Such systems are only able to operate within narrow application domains and cannot be generalized to handle other heterogeneous types of information. To fill this gap, we propose a context-aware system for information integration that can handle arbitrary information types and determine their relevance to the user's current context. In contrast to existing model-based approaches to context reasoning, we log user interaction and perform usage mining using OLAP to discover context-dependent preferences for different information types. This allows us to build a more generic and adaptive system that automatically selects the most relevant content and presents it to the user in a succinct manner that supports ease of consumption and comprehension.
CITATION STYLE
Xu, K., Zhu, M., Zhang, D., & Gu, T. (2009). Context-aware content filtering and presentation for pervasive and mobile information systems. Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST). https://doi.org/10.4108/icst.ambisys2008.2907
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