Corpus ID: 13132979

Proceedings of the International Workshop on Semantic Technologies meet Recommender Systems & Big Data SeRSy 2012 co-located with the :

@inproceedings{Gemmis2012ProceedingsOT,
  title={Proceedings of the International Workshop on Semantic Technologies meet Recommender Systems \& Big Data SeRSy 2012 co-located with the :},
  author={Marco de Gemmis and T. D. Noia and Pasquale Lops and Thomas Lukasiewicz and Giovanni Semeraro},
  year={2012}
}
We present a general and novel framework for predicting links in multirelational graphs using a set of matrices describing the various instantiated relations in the knowledge base. We construct matrices that add information further remote in the knowledge graph by join operations and we describe how unstructured information can be integrated in the model. We show that efficient learning can be achieved using an alternating least squares approach exploiting sparse matrix algebra and low-rank… Expand

References

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TLDR
It is shown that efficient learning can be achieved using an alternating least squares approach exploiting sparse matrix algebra and low-rank approximations and a kernel solution which is of interest when it is easy to define sensible kernels. Expand
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TLDR
A new hybrid methodology to rank resources exploiting the graphbased nature of the underlying RDF structure, context independent semantic relations in the graph and external information sources such as classical search engine results and social tagging systems is proposed. Expand
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TLDR
This paper implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. Expand
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TLDR
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