An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Capturing experience: a matter of contextualising events
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
Toward a Common Event Model for Multimedia Applications
IEEE MultiMedia
Linked data on the web (LDOW2008)
Proceedings of the 17th international conference on World Wide Web
Programming collective intelligence
Programming collective intelligence
F--a model of events based on the foundational ontology dolce+DnS ultralight
Proceedings of the fifth international conference on Knowledge capture
LODE: Linking Open Descriptions of Events
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
An upper ontology for event classifications and relations
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
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Today, people have only limited, valuable spare time at their hands which they want to fill in as good as possible according to their interests. At the same time, cultural institutions are trying to attract interested communities to their carefully planned cultural programs. To distribute these cultural events to the right people, we developed a framework that will aggregate, enrich, recommend and distribute these events as targeted as possible. The aggregated events are published as Linked Open Data using an RDF/OWL representation of the EventsML-G2 standard. These event items are categorised and enriched via smart indexing and linked open datasets available on the Web of data. For recommending the events to the end-user, a global profile of the end-user is automatically constructed by aggregating his profile information from all user communities the user trusts and is registered to. This way, the recommendations take profile information into account from different communities, which has a detrimental effect on the recommendations. As such, the ultimate goal is to provide an open, user-friendly recommendation platform that harnesses the end-user with a tool to access useful event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national cultural community with standardised mechanisms to describe/distribute event and profile information.