DC Field | Value | Language |
---|---|---|
dc.contributor.author | Qian, Xin | ko |
dc.contributor.author | Rossi, Ryan A. | ko |
dc.contributor.author | Du, Fan | ko |
dc.contributor.author | Kim, Sungchul | ko |
dc.contributor.author | Koh, Eunyee | ko |
dc.contributor.author | Malik, Sana | ko |
dc.contributor.author | Lee, Tak Yeon | ko |
dc.contributor.author | Chan, Joel | ko |
dc.date.accessioned | 2021-10-28T01:50:17Z | - |
dc.date.available | 2021-10-28T01:50:17Z | - |
dc.date.created | 2021-10-26 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, pp.1359 - 1369 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288399 | - |
dc.description.abstract | Visualization recommendation is important for exploratory analysis and making sense of the data quickly by automatically recommending relevant visualizations to the user. In this work, we propose the first end-to-end ML-based visualization recommendation system that leverages a large corpus of datasets and their relevant visualizations to learn a visualization recommendation model automatically. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derives scores for the visualizations, and outputs a list of recommended visualizations to the user ordered by effectiveness. We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. Through quantitative experiments, a user study, and qualitative analysis, we show that our end-to-end ML-based system recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems. © 2021 ACM. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Learning to Recommend Visualizations from Data | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85114934815 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1359 | - |
dc.citation.endingpage | 1369 | - |
dc.citation.publicationname | 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 | - |
dc.identifier.conferencecountry | SI | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3447548.3467224 | - |
dc.contributor.localauthor | Lee, Tak Yeon | - |
dc.contributor.nonIdAuthor | Qian, Xin | - |
dc.contributor.nonIdAuthor | Rossi, Ryan A. | - |
dc.contributor.nonIdAuthor | Du, Fan | - |
dc.contributor.nonIdAuthor | Kim, Sungchul | - |
dc.contributor.nonIdAuthor | Koh, Eunyee | - |
dc.contributor.nonIdAuthor | Malik, Sana | - |
dc.contributor.nonIdAuthor | Chan, Joel | - |
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