Communications of the ACM
Stereotypes in information filtering systems
Information Processing and Management: an International Journal
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Supporting group interactions in museum visiting
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Recommender systems (RSs) proved to make easier the task of accessing relevant information in a broad range of domains. In content-based RSs, preferences on content items expressed by users turned out to be reliable indicators to suggest and filter interesting contents. Item representation plays a key role in content-based RSs, thus choosing proper facets to represent items is a fundamental task for deploying effective RSs. Contextual facets are often marginally relevant to predict user preferences, but in some domains disregarding contextual facets makes recommendations useless. This paper proposes a strategy to improve the effectiveness of a content-based RS that dynamically suggests tours within a museum by exploiting contextual facets such the physical layout of items and the interaction of users with the environment.