Visualizing Feature-based Similarity for Research Paper Recommendation

Abstract

Research paper recommender systems are widely used by academics to discover and explore the most relevant publications on a topic. While existing recommendation interfaces present researchers with a ranked list of publications based on a global relevance score, they fail to visualize the full range of non-textual features uniquely present in academic publications: citations, figures, charts, or images, and mathematical formulae or expressions. Especially for STEM literature, examining such non-textual features efficiently can provide utility to researchers interested in answering specialized research questions or information needs. If research paper search and recommender systems are to consider the similarity of such features as one facet of a content-based similarity assessment for academic literature, new methods for visualizing these non-textual features are needed. In this paper, we review the state-of-the-art in visualizing feature-based similarity in documents. We subsequently propose a set of user-customizable visualization approaches tailored to STEM literature and the research paper recommendation context. Results from a study with 10 expert users show that the interactive visualization interface we propose for the exploration of non-textual features in publications can effectively address specialized information retrieval tasks, which cannot be addressed by existing research paper search or recommendation interfaces.