Directed Criteria Citation Recommendation and Ranking Through Link Prediction
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914731537727488 |
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| author | Watson, William Yong, Lawrence |
| author_facet | Watson, William Yong, Lawrence |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Directed Criteria Citation Recommendation and Ranking Through Link Prediction Watson, William Yong, Lawrence Social and Information Networks Information Retrieval Machine Learning 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 |
| title | Directed Criteria Citation Recommendation and Ranking Through Link Prediction |
| topic | Social and Information Networks Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2403.18855 |