Directed Criteria Citation Recommendation and Ranking Through Link Prediction
<|reference_start|>Directed Criteria Citation Recommendation and Ranking Through Link Prediction: We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each document, presented as a node within a citation network. We show that the semantic representations that our model generates can outperform other content-based methods in recommendation and ranking tasks. This provides a holistic approach to exploring citation graphs in domains where it is critical that these documents properly cite each other, so as to minimize the possibility of any inconsistencies<|reference_end|>
@article{watson2024directed,
    title={Directed Criteria Citation Recommendation and Ranking Through Link
  Prediction},
    author={William Watson and Lawrence Yong},
    journal={arXiv preprint arXiv:2403.18855},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18855},
    primaryClass={cs.SI cs.IR cs.LG}
}