Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems

TL;DR

This paper introduces a new framework for research paper recommender systems that emphasizes novelty and diversity to foster interdisciplinary research, demonstrating that representational choices significantly influence recommendation quality.

Contribution

It proposes a novel evaluation framework and a new paper embedding method that enhances recommendation diversity and novelty without losing precision.

Findings

The representational method impacts recommendation diversity and novelty.

The new embedding method improves diversity and novelty.

Recommendations maintain precision with the new method.

Abstract

In the extensive recommender systems literature, novelty and diversity have been identified as key properties of useful recommendations. However, these properties have received limited attention in the specific sub-field of research paper recommender systems. In this work, we argue for the importance of offering novel and diverse research paper recommendations to scientists. This approach aims to reduce siloed reading, break down filter bubbles, and promote interdisciplinary research. We propose a novel framework for evaluating the novelty and diversity of research paper recommendations that leverages methods from network analysis and natural language processing. Using this framework, we show that the choice of representational method within a larger research paper recommendation system can have a measurable impact on the nature of downstream recommendations, specifically on their novelty and diversity. We highlight a novel paper embedding method, which we demonstrate offers more innovative and diverse recommendations without sacrificing precision, compared to other state-of-the-art baselines.