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
Trust-based agent community for collaborative recommendation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Do Metrics Make Recommender Algorithms?
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
3rd international workshop on automated information extraction in media production
Proceedings of the international conference on Multimedia
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Today, people have only limited, valuable leisure time at their hands which they want to fill in as good as possible according to their own interests, whereas broadcasters want to produce and distribute news items as fast and targeted as possible. These (developing) news stories can be characterised as dynamic, chained, and distributed events in addition to which it is important to aggregate, link, enrich, recommend, and distribute these news event items as targeted as possible to the individual, interested user. In this paper, we show how personalised recommendation and distribution of news events, described using an RDF/OWL representation of the NewsML-G2 standard, can be enabled by automatically categorising and enriching news events metadata via smart indexing and linked open datasets available on the web of data. The recommendations - based on a global, aggregated profile, which also takes into account the (dis)likings of peer friends - are finally fed to the user via a personalised RSS feed. 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 news event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national community with standardised mechanisms to describe/distribute news event and profile information.