Understanding and Using Context
Personal and Ubiquitous Computing
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
A service-oriented middleware for building context-aware services
Journal of Network and Computer Applications
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Proceedings of the 2005 ACM symposium on Applied computing
IEEE Transactions on Knowledge and Data Engineering
Combining ECA Rules with Process Algebras for the Semantic Web
RULEML '06 Proceedings of the Second International Conference on Rules and Rule Markup Languages for the Semantic Web
Collective Intelligence in Action
Collective Intelligence in Action
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Text-to-query: dynamically building structured analytics to illustrate textual content
Proceedings of the 2010 EDBT/ICDT Workshops
The adaptive web
A contextual user model for web personalization
WISE'07 Proceedings of the 2007 international conference on Web information systems engineering
Exploiting E-C-A rules for defining and processing context-aware push messages
RuleML'07 Proceedings of the 2007 international conference on Advances in rule interchange and applications
Ontology-Based Context Representation and Reasoning Using OWL and SWRL
CNSR '10 Proceedings of the 2010 8th Annual Communication Networks and Services Research Conference
Gumo: the general user model ontology
UM'05 Proceedings of the 10th international conference on User Modeling
An architecture for developing context-aware systems
MRC'05 Proceedings of the Second international conference on Modeling and Retrieval of Context
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Information overload is an increasingly important concern as users access and generate steadily growing amounts of data. Besides, enterprise applications tend to grow more and more complex which hinders their usability and impacts business users' productivity. Personalization and recommender systems can help address these issues, by predicting items of interest for a given user and enabling a better selection of the proposed information. Recommendations have become increasingly popular in web environments, with sites like Amazon, Netflix or Google News. However, little has been done so far to leverage recommendations in corporate settings. This paper presents our approach to integrate recommender systems in enterprise environments, taking into account their specific constraints. We present an extensible framework enabling heterogeneous recommendations, based on a semantic model of users' situations and interactions. We illustrate this framework with a system suggesting structured queries and visualizations related to an unstructured document.