Unsupervised context-aware user preference mining
2013 (English)In: Proceeding of Workshop on Activity Context-Aware System Architectures at the 27th AAAI Conference on Artificial Intelligence, 2013, 36-43 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2013. 36-43 p.
Clustering algorithms, Context-Aware, Context-aware systems, Feature sets, High dimensionality, Pervasive environments, Preference mining, Sub-Space Clustering, User experience, User interfaces
IdentifiersURN: urn:nbn:se:kth:diva-147285ScopusID: 2-s2.0-84898866077ISBN: 978-157735616-5OAI: oai:DiVA.org:kth-147285DiVA: diva2:737554
2013 AAAI Workshop; Bellevue, WA; United States; 14 July 2013 through 14 July 2013
QC 201408132014-08-132014-06-252014-08-13Bibliographically approved