Titlebook: Advances in Information Retrieval; 38th European Confer Nicola Ferro,Fabio Crestani,Gianmaria Silvello Conference proceedings 2016 Springer

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https://doi.org/10.1007/978-3-319-30671-1Information retrieval; machine learning; multimedia; recommender systems; social media IR; data modeling;

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978-3-319-30670-4Springer International Publishing Switzerland 2016

Hallmark

Chronisch-entzündliche Darmerkrankungenevent. This paper proposes a framework named . to take advantage of information from Twitter viz. Diversity and reflection of document content to generate high-quality summaries by a novel sentence similarity measurement. The framework first formulates sentences and tweets by recognizing textual ent

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Claus Peter Trimborn,Jörg C. Hoffmannlinear regression model of similarity between the graph nodes (comments) based on similarity features and weights trained using automatically derived training data. To label the clusters our graph-based approach makes use of DBPedia to abstract topics extracted from the clusters. We evaluate the clu

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Grundprinzipien der CED-Behandlungcommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response predicti