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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 361-364
ABSTRACT
"What movies do you like?" Everyone has had to answer this question at least once. And the answer is often given by means of examples: "I like Star Wars." Often an examples explains a lot more than trying to characterize movies by other means, like giving a category like "Science Fiction" or providing actor or director names. The Movie Oracle recommends movies by comparing examples provided by the user to movie contents, which the Movie-Oracle derives from the movie dialogues gathered from movie subtitle files, without using any human generated meta-data.
INDEX TERMS
content based prediction, inductive learning
CITATION
Jochen Nessel, Barbara Cimpa, "The MovieOracle - Content Based Movie Recommendations", WI-IAT, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 361-364, doi:10.1109/WI-IAT.2011.236
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