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This paper introduces aspect-aware content-based research paper recommendation (CbRPR) for mathematics, a domain where existing methods fail due to the conceptual nature of paper relatedness. They create two new datasets, GoldRiM and SilverRiM, and propose AchGNN, an aspect-conditioned heterogeneous graph neural network that jointly models textual semantics, citation structure, and author lineage. AchGNN outperforms prior aspect-based CbRPR methods on both the new mathematics datasets and the Papers with Code dataset, demonstrating its effectiveness and domain generality.
LLMs alone can't capture the nuances of mathematical research, but injecting aspect-aware information into a heterogeneous GNN unlocks surprisingly effective paper recommendations.
Content-based research paper recommendation (CbRPR) has seen advances in computer science and biomedicine, but remains unexplored for mathematics, where paper relatedness is more conceptual than explicit textual or citation-based similarity. Mathematics papers may be connected through shared proof techniques, logical implications, or natural generalizations, yet exhibit minimal textual or citation overlap, rendering existing CbRPR ineffective. To address this gap, we first conduct an expert-driven study characterizing mathematical recommendations, revealing that relevance is inherently \textit{aspect}-driven. Grounded in this insight, we introduce GoldRiM (small, expert-annotated) and SilverRiM (large, automatically derived), the first datasets for \textit{aspect}-aware CbRPR in mathematics. Recognizing that LLM embeddings of mathematical content alone yield suboptimal representation, we propose AchGNN, an \textit{aspect}-conditioned heterogeneous GNN that jointly models textual semantics, citation structure, and author lineage. Across GoldRiM and SilverRiM, AchGNN consistently outperforms prior \textit{aspect}-based CbRPR methods, achieving substantial gains across all evaluated \textit{aspects}. We conduct ablation studies to analyze the contributions of individual \textit{aspect} supervision, authorship lineage, and graph-structural signals to AchGNN's performance. To assess domain generality, we further evaluate AchGNN on the \textit{Papers with Code} dataset of machine learning publications, demonstrating that our \textit{aspect}-aware approach effectively transfers beyond mathematics. We deploy our system on the MaRDI platform to help mathematicians with recommendations and release datasets and code publicly for reproducibility.
