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In this paper we introduce S\n                    <jats:sc>PEECH<\/jats:sc>\n                    R\n                    <jats:sc>EC<\/jats:sc>\n                    , a speech-enabled, knowledge-based recommender system, that engages the user in a natural-language dialog, identifying not only purely factual constraints from the users\u2019 input, but also integrating nuanced lexical qualifiers and paralinguistic information into the recommendation strategy. In order to assess the viability of this concept, we present the results of an empirical study where we compare S\n                    <jats:sc>PEECH<\/jats:sc>\n                    R\n                    <jats:sc>EC<\/jats:sc>\n                    to a traditional knowledge-based recommender system and show how incorporating more granular user preferences in the recommendation strategy can increase recommendation quality, while reducing median session length by 46 %.\n                  <\/jats:p>","DOI":"10.1515\/icom-2015-0017","type":"journal-article","created":{"date-parts":[[2015,6,10]],"date-time":"2015-06-10T20:47:12Z","timestamp":1433969232000},"page":"41-52","source":"Crossref","is-referenced-by-count":2,"title":["On the Importance of Subtext in Recommender Systems"],"prefix":"10.1515","volume":"14","author":[{"given":"Peter","family":"Grasch","sequence":"first","affiliation":[{"name":"Graz University of Technology, Institute for Software Technology, Graz, Austria"}]},{"given":"Alexander","family":"Felfernig","sequence":"additional","affiliation":[{"name":"Graz University of Technology, Institute for Software Technology, Graz, Austria"}]}],"member":"374","published-online":{"date-parts":[[2015,4,1]]},"reference":[{"unstructured":"Alina Andreevskaia, Sabine Bergler, and Monica Urseanu. 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