Recommending Research Papers to Chemists: A Specialized Interface for Chemical Entity Exploration

TL;DR

This paper introduces ChemVis, a specialized research paper recommender system for chemists that visualizes chemical structures and related data within full-text papers, addressing a gap in existing literature recommendation tools.

Contribution

The paper presents the first implementation of a chemistry-specific paper recommender system that visualizes chemical entities, enhancing domain-specific discovery and comparison capabilities.

Findings

Supports visualization of chemical structures within papers

Integrates chemical formulae and synonyms for compounds

Aims to improve chemical literature discovery

Abstract

Researchers and scientists increasingly rely on specialized information retrieval (IR) or recommendation systems (RS) to support them in their daily research tasks. Paper recommender systems are one such tool scientists use to stay on top of the ever-increasing number of academic publications in their field. Improving research paper recommender systems is an active research field. However, less research has focused on how the interfaces of research paper recommender systems can be tailored to suit the needs of different research domains. For example, in the field of biomedicine and chemistry, researchers are not only interested in textual relevance but may also want to discover or compare the contained chemical entity information found in a paper's full text. Existing recommender systems for academic literature do not support the discovery of this non-textual, but semantically valuable, chemical entity data. We present the first implementation of a specialized chemistry paper recommender system capable of visualizing the contained chemical structures, chemical formulae, and synonyms for chemical compounds within the document's full text. We review existing tools and related research in this field before describing the implementation of our ChemVis system. With the help of chemists, we are expanding the functionality of ChemVis, and will perform an evaluation of recommendation performance and usability in future work.