Adobe, Inc.

Xin Qian

Eunyee Koh

Fan Du

Sungchul Kim

Sana Malik Lee

Tak Yeon Lee

Ryan A Rossi

Vasanthi Holtcamp

Nathan Carl Ross

John Anderson

Duy-Trung Trong Dinh

A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.

Machine learning techniques for generating visualization recommendations

A dataset captioning system is described that generates captions of text to describe insights identified from a dataset, automatically and without user intervention. To do so, given an input of a dataset the dataset captioning system determines which data insights are likely to support potential visualizations of the dataset, generates text based on these insights, orders the text, processes the ordered text for readability, and then outputs the text as a caption. These techniques also include adjustments made to the complexity of the text, globalization of the text, inclusion of links to outside sources of information, translation of the text, and so on as part of generating the caption.

Automated caption generation from a dataset

Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

Personalized visualization recommendation system

Techniques of captioning for figures includes generating a caption unit for a figure by defining a finite set of caption types. From each caption type, additional input for that caption type, as well as figure image data and figure metadata, an automated system may generate a respective caption unit, each caption unit including a sequence of words. Further, the generated caption for a figure includes a combination of the generated caption units.

Generating accurate and natural captions for figures

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Xin Qian