Given a data table, dataworkers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts . We contribute a deep-learning-based method that assists in designinganalytical dashboards . Our model could makerecommendations given optional user-input selections of data . The model, in turn, learns from provenance data of authoring logs in an offlinemanner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the system. We present a deep learning approach for selecting datacolumns and recommending multiple charts. The system could make recommendations given optionaluser- input selections ofData columns. The model

Author(s) : Aoyu Wu, Yun Wang, Mengyu Zhou, Xinyi He, Haidong Zhang, Huamin Qu, Dongmei Zhang

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Keywords : deep - data - model - learning - charts -

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