Beyond Single-Score Ranking: Facet-Aware Reranking for Controllable Diversity in Paper Recommendation
This addresses the need for more controllable and diverse paper recommendations for researchers, though it is incremental as it builds on existing reranking and facet-based methods.
The paper tackled the problem of paper recommendation systems lacking controllability by proposing SciFACE, a reranking framework that models independent facets (Background and Method) for diversity, achieving 70.63 NDCG@20 on Background and 49.06 NDCG@20 on Method, with improvements over baselines like SPECTER and FaBLE.
Current paper recommendation systems output a single similarity score that mixes different notions of relatedness, so users cannot specify why papers should be similar. We present SciFACE (Scientific Faceted Cross-Encoder), a reranking framework that models two independent facets: Background (what problem is studied) and Method (how it is solved). SciFACE trains two separate cross-encoders on 5,891 real seed-candidate paper pairs labeled by GPT-4o-mini with facet-specific criteria and validated against human judgments. On CSFCube, SciFACE reaches 70.63 NDCG@20 on Background (5.9 points above SPECTER) and 49.06 NDCG@20 on Method (31.1 points above SPECTER), competitive with state-of-the-art results. Compared with FaBLE without citation pre-training, SciFACE improves Method NDCG@20 by 4.1 points while using 5,891 labeled pairs versus 40K synthetic augmentations. These results show that high-quality grounded facet labels can be more data-efficient than large-scale synthetic augmentation for learning fine-grained scientific similarity.