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arXiv · 2412.15229

Building an Explainable Graph-based Biomedical Paper Recommendation System (Technical Report)

Hermann Kroll·Christin K. Kreutz·Bill Matthias Thang·Philipp Schaer·Wolf-Tilo Balke
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Abstract

Digital libraries provide different access paths, allowing users to explore their collections. For instance, paper recommendation suggests literature similar to some selected paper. Their implementation is often cost-intensive, especially if neural methods are applied. Additionally, it is hard for users to understand or guess why a recommendation should be relevant for them. That is why we tackled the problem from a different perspective. We propose XGPRec, a graph-based and thus explainable method which we integrate into our existing graph-based biomedical discovery system. Moreover, we show that XGPRec (1) can, in terms of computational costs, manage a real digital library collection with 37M documents from the biomedical domain, (2) performs well on established test collections and concept-centric information needs, and (3) generates explanations that proved to be beneficial in a preliminary user study. We share our code so that user libraries can build upon XGPRec.

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Hermann Kroll, Christin K. Kreutz, Bill Matthias Thang, Philipp Schaer, Wolf-Tilo Balke. 2024-12-05. Building an Explainable Graph-based Biomedical Paper Recommendation System (Technical Report). https://arxiv.org/abs/2412.15229

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