Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Collaboration in Context-Aware Mobile Phone Applications
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
Context-Aware Systems for Mobile and Ubiquitous Networks
ICNICONSMCL '06 Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies
Toward a multidisciplinary model of context to support context-aware computing
Human-Computer Interaction
Context-aware systems: A literature review and classification
Expert Systems with Applications: An International Journal
A system for context-dependent user modeling
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
Generalized association rule mining with constraints
Information Sciences: an International Journal
Semi-Automatic Ontology Construction by Exploiting Functional Dependencies and Association Rules
International Journal on Semantic Web & Information Systems
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Context-aware applications allow service providers to adapt their services to actual user needs, by offering them personalized services depending on their current application context. Hence, service providers are usually interested in profiling users both to increase client satisfaction, and to broaden the set of offered services. Since association rule extraction allows the identification of hidden correlations among data, its application in context-aware platforms is very attractive. However, traditional association rule extraction, driven by support and confidence constraints, may entail either (i) generating an unmanageable number of rules in case of low support thresholds, or (ii) discarding rare (infrequent) rules, even if their hidden knowledge might be relevant to the service provider. Novel approaches are needed to effectively manage different data granularities during the mining activity. This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine exploits a novel and efficient algorithm to extract generalized association rules. Support driven opportunistic aggregation is exploited to exclusively generalize infrequent rules. User-provided taxonomies on different attributes (e.g., a geographic hierarchy on spatial coordinates, a temporal hierarchy, a classification of provided services), drive the rule generalization process that prevents discarding relevant but infrequent knowledge. Experiments performed on both real and synthetic datasets show the effectiveness and the efficiency of the proposed framework in mining different types of correlations between user habits and provided services.