  With an exponentially growing number of scientific papers published each
year, advanced tools for exploring and discovering publications of interest are
becoming indispensable. To empower users beyond a simple keyword search
provided e.g. by Google Scholar, we present the novel web application PubVis.
Powered by a variety of machine learning techniques, it combines essential
features to help researchers find the content most relevant to them. An
interactive visualization of a large collection of scientific publications
provides an overview of the field and encourages the user to explore articles
beyond a narrow research focus. This is augmented by personalized content based
article recommendations as well as an advanced full text search to discover
relevant references. The open sourced implementation of the app can be easily
set up and run locally on a desktop computer to provide access to content
tailored to the specific needs of individual users. Additionally, a PubVis demo
with access to a collection of 10,000 papers can be tested online.
