Haptipedia aims to close the gap between device and interaction designers, accelerate design exploration, and reduce fruitless reinventions. We accomplish this by exposing decades of haptic device inventions that have been buried in the literature and making them easy to search and compare through an open-source visualization and database.

We present the concept of a text-based topic similarity measure to be used towards content-based recommendations in visualization repositories. We investigate four applicable NLP models and conduct a user study that demonstrates it is possible to obtain good alignment between human similarity perception and off-the-shelf model predictions.