Directed Criteria Citation Recommendation and Ranking Through Link Prediction

• URL: http://arxiv.org/abs/2403.18855v1
• Date: Mon, 18 Mar 2024 20:47:38 GMT
• Title: Directed Criteria Citation Recommendation and Ranking Through Link Prediction
• Authors: William Watson, Lawrence Yong,
• Abstract summary: 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.
• Score: 0.32885740436059047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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