Visualizations Relevant to The User By Multi-View Latent Variable Factorization

TL;DR

This paper introduces a multi-view latent variable factorization model to identify user-relevant visualizations by analyzing primary and auxiliary data, enhancing personalized data display.

Contribution

It proposes a novel generative model that jointly factorizes primary and user data to determine relevant visualization mappings, addressing noisy user inputs.

Findings

Model successfully identifies user-relevant data aspects.

Demonstrated effectiveness on multiple datasets.

Improves personalized visualization relevance.

Abstract

A main goal of data visualization is to find, from among all the available alternatives, mappings to the 2D/3D display which are relevant to the user. Assuming user interaction data, or other auxiliary data about the items or their relationships, the goal is to identify which aspects in the primary data support the user\'s input and, equally importantly, which aspects of the user\'s potentially noisy input have support in the primary data. For solving the problem, we introduce a multi-view embedding in which a latent factorization identifies which aspects in the two data views (primary data and user data) are related and which are specific to only one of them. The factorization is a generative model in which the display is parameterized as a part of the factorization and the other factors explain away the aspects not expressible in a two-dimensional display. Functioning of the model is demonstrated on several data sets.