Directed Criteria Citation Recommendation and Ranking Through Link Prediction

arXiv:2403.18855v1 Announce Type: cross
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 …

abstract arxiv cs.ir cs.lg cs.si document documents embeddings encode explore graph link prediction literature meaning network node prediction ranking recommendation through transformer type