Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Understanding and Using Context
Personal and Ubiquitous Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive Hypermedia And Adaptive Web-based Systems: Third International Conference, Ah 2004, Eindhoven, The Netherlands, August 23-26, 2004, Proceedings (Lecture Notes in Computer Science)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders
IEEE Transactions on Knowledge and Data Engineering
User profiling with hierarchical context: an e-Retailer case study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
A case study in a recommender system based on purchase data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Vizier: a generic and multidimensional agent-based recommendation framework
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
SmarterDeals: a context-aware deal recommendation system based on the smartercontext engine
CASCON '12 Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research
| Hi-index | 0.00 |
Recommender Systems help an on-line user to tame information overload and are being used now in complex domains where it could be beneficial to exploit context-awareness, e.g., in travel recommendation. Technically, in Recommender Systems we can interpret context as a set of constraints or preferences over the usage of items determined by the contextual conditions (e.g., today it is raining or the user is in a particular location). In fact, there is a lack of approaches to deal effectively with contextual data. This thesis investigates some approaches to exploit context in Recommender Systems. It provides a general architecture of context-aware Recommender Systems and analyzes separate components of this model. The main focus is to investigate new approaches that can bring a real added value to users. In this paper I also describe my initial results on item selection and item weighting for context-dependent Collaborative Filtering (CF). Moreover, I shall present my ongoing research on CF hybridization using context.