论文标题
table2charts：通过学习共享表表示来推荐图表
Table2Charts: Recommending Charts by Learning Shared Table Representations
论文作者
论文摘要
人们通常创建不同类型的图表来探索多维数据集（表）。但是，要推荐现实世界中通常构成的图表，应该考虑效率，不平衡数据和表上表面的挑战。在本文中，我们提出了Table2Charts框架，该框架从大量的（表，图表）对中学习了共同的模式。基于复制机制和启发式搜索的深度Q学习，Table2Charts会进行表格到序列的生成，每个序列都遵循图表模板。在具有165k表和266K图表的大型电子表格语料库上，我们表明Table2charts可以学习表字段的共享表示形式，以便不同图表类型上的建议任务可以相互增强。 Table2Charts在多类型任务中的其他图表推荐系统（带有召回号码r@3=0.61和r@1=0.43）和人类评估。
It is common for people to create different types of charts to explore a multi-dimensional dataset (table). However, to recommend commonly composed charts in real world, one should take the challenges of efficiency, imbalanced data and table context into consideration. In this paper, we propose Table2Charts framework which learns common patterns from a large corpus of (table, charts) pairs. Based on deep Q-learning with copying mechanism and heuristic searching, Table2Charts does table-to-sequence generation, where each sequence follows a chart template. On a large spreadsheet corpus with 165k tables and 266k charts, we show that Table2Charts could learn a shared representation of table fields so that recommendation tasks on different chart types could mutually enhance each other. Table2Charts outperforms other chart recommendation systems in both multi-type task (with doubled recall numbers R@3=0.61 and R@1=0.43) and human evaluations.
