Understanding and Using Context
Personal and Ubiquitous Computing
Case-Based User Profiling for Content Personalisation
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Constrained optimalities in query personalization
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Fast contextual preference scoring of database tuples
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A personalized access model: concepts and services for content delivery platforms
Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services
Ranking Query Results using Context-Aware Preferences
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Creating User Profiles Using Wikipedia
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
PerK: personalized keyword search in relational databases through preferences
Proceedings of the 13th International Conference on Extending Database Technology
Investigating the specifics of contextual elements management: the CEManTIKA approach
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Visualizing the relevance of social ties in user profile modeling
Web Intelligence and Agent Systems
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Profiles and contexts are the main concepts used by modern applications (e.g. e-commerce and recommender systems) to adapt content delivery services to the users' needs, preferences and environment. Although the definitions of the two terms slightly differ from one application to another, there is a general agreement to distinguish them and use them separately or jointly in a given application. When used jointly, the relationship between the two concepts remains often unclear. This paper aims at providing a personalization model that encompasses profile, context, and a formal relationships between the two. This relationship, called con-textualization, is represented by a set of ranked mappings, automatically extracted from a usage history (log file of user actions). Profile, context and contextualization constitute three structuring elements over which any personalized system should be built. The proposal is supported by a design platform which helps in instantiating profiles and contexts and in generating contextual mappings between them. An instantiation of the meta model is given for an advanced recommender system, called context-aware recommender system (or CARS for short). This instantiation is followed by an experiment highlighting the benefit of contextualization.