Talks and Poster Presentations (with Proceedings-Entry):
F. Li, K. Rasch, S. Sehic, S. Dustdar, R. Ayani:
"Unsupervised Context-Aware User Preference Mining";
Talk: Workshop on Activity Context-Aware System Architectures at AAAI 2013,
Bellevue, Washington, USA;
- 2013-07-18; in: "Activity Context-Aware System Architectures, Papers from the 2013 AAAI Workshop",
In pervasive environments, users are situated in rich context and can interact with their surroundings through various services. To improve user experience in such environments, it is essential to find the services that satisfies user preferences in certain context. Thus the suitability of discovered services is highly dependent on how much the context-aware system can understand users' current context and preferred activities. In this paper, we propose an unsupervised learning solution for mining user preferences from the user's past context. To cope with the high dimensionality and heterogeneity of context data, we propose a subspace clustering approach that is able to find user preferences identified by different feature sets. The results of our approach are validated by a series of experiments.
Project Head Schahram Dustdar:
Cloud computing research lab
Created from the Publication Database of the Vienna University of Technology.