Visualization Recommendation Through Visual Relation Learning and Visual Preference Learning

Abstract

Visualization recommendation (VisRec) is to automatically generate the most relevant visualization for a table of interest to a user. In this paper, we present a novel machine learning-based VisRec method, VisFormer, which solves VisRec in three stages: 1) Table representation learning, which is to learn accurate column-level representations for a table. To achieve it, we resort to Transformer, a powerful language model that can learn accurate word embeddings by modeling context. Specifically, we propose a hierarchical Transformer-based architecture to learn expressive column representations by capturing two types of context, intra-column context and cross-column context; 2) Visual Relation Learning, which is to capture column relations. To achieve it, we regard each visualization as a relation tuple with a special relation, visual relation, between the columns. Then for each visual relation, we use a neural network to evaluate the corresponding visualizations; 3) Visual Preference Learning, which is to extract visual preference features that can affect users’ decision from a visualization. To achieve so, we use a Convolution Neural Network to extract such features and explore how to use them to refine the recommendation results. We conduct experiments to compare with three state-of-the-art ML-based methods on a large real-world dataset, Plotly community feed. The experimental results show that compared with the most competitive baseline, the relative improvements of VisFormer on Recall@1, Recall@2, and Recall@3 are 8.8%, 20.6%, and 21.0%, respectively.