X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Orchestrating context-aware systems: a design perspective
Proceedings of the first international workshop on Context-aware software technology and applications
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Context relevance assessment for recommender systems
Proceedings of the 16th international conference on Intelligent user interfaces
COSAR: hybrid reasoning for context-aware activity recognition
Personal and Ubiquitous Computing
Ranking in context-aware recommender systems
Proceedings of the 20th international conference companion on World wide web
Towards contextual search: social networks, short contexts and multiple personas
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
CAMPUS: context aware mobile platform for uniformed security
Proceedings of the 13th international conference on Ubiquitous computing
Colocation networks: exploring the use of social andgeographical patterns in context-aware services
Proceedings of the 13th international conference on Ubiquitous computing
Application of dimensionality reduction techniques for mobile social context
Proceedings of the 13th international conference on Ubiquitous computing
Why is context-aware computing less successful?
CASEMANS '11 Proceedings of the 5th ACM International Workshop on Context-Awareness for Self-Managing Systems
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
A wide-area context-awareness approach for Android
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
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Our work focuses on the improvement of the accuracy of context-aware recommender systems. Contextual information showed to be promising factor in recommender systems. However, pure context-based recommender systems can not outperform other approaches mainly due to high sparsity of contextual information. We propose an idea to improve accuracy of context based recommender systems by context inference. Context inference is based on effect discovered by analyses of the context as a factor influencing user needs. Analyses of the news readers reveals existence of behavioural correlation which is the main pillar of proposed context inference. Method for context inference is based on collaborative filtering and clustering of web usage (as a non-discretizing alternative to association rules mining).