@inProceedings{
 title = {Visual Analytics for the Prediction of Movie Rating and Box Office Performance},
 type = {inProceedings},
 year = {2013},
 pages = {3-4},
 websites = {http://users.soe.ucsc.edu/~pang/visweek/2013/vast/challenge/el-assady.pdf%0Ahttps://bib.dbvis.de/uploadedFiles/elassady.pdf},
 id = {b4743ba1-4e25-330a-bb0e-562b8a90aa44},
 created = {2017-07-11T16:55:14.941Z},
 file_attached = {false},
 profile_id = {4569e30c-c012-333a-b4c4-a51cb0b66c47},
 last_modified = {2017-07-11T16:55:14.941Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Assady2013a},
 private_publication = {false},
 abstract = {This paper describes our solution to the IEEE VAST 2013 Mini Challenge 11. The task of the challenge was to create a visual and interactive tool to predict the popularity of new movies in terms of viewer ratings and ticket sales for the opening weekend in the U.S. The data usage was restricted by the challenge organizers to data from the Internet Movie Database (IMDb)2 and a predefined set of Twitter3 microblog messages. To tackle the challenge we designed a system together with an analysis workflow, combining machine learning and visualization paradigms in order to obtain accurate predictions. In Section 2 we describe the machine learning components used within the analysis workflow. Next, in Section 3, we describe where and how the human analyst is enabled to enhance the prediction with her/his world knowledge. Finally, Section 4 concludes the paper providing an evaluation of the prediction accuracy with and without human intervention.},
 bibtype = {inProceedings},
 author = {Assady, M El and Hafner, D and Hund, M},
 booktitle = {IEEE VIS, Oct 13, 2013 - Oct 18, 2013, Atlanta, Georgia, USA}
}

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