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SERUM: Collecting Semantic User Behavior for Improved News Recommendations
[chapter]
2012
Lecture Notes in Computer Science
We present our recent work on recommending personalized news articles to users based on implicit collected feedback and large scale semantic datasets. Our personalized recommendation application SERUM exploits the fact that semantically linked and structured information becomes more and more available driven by a strong research community. Our solution combines these semantic, encyclopedic knowledge sources with a large news article dataset and collected implicit user feedback using an RDFa
doi:10.1007/978-3-642-28509-7_37
fatcat:n3o7avm35be2vcepvztbq3m7nu