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Directed Criteria Citation Recommendation and Ranking Through Link Prediction (2403.18855v1)
Published 18 Mar 2024 in cs.SI, cs.IR, and cs.LG
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
- Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:cs.CL/1409.0473
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv:cs.LG/1511.07289
- A new model for learning in graph domains. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., Vol. 2. 729–734 vol. 2.
- Inductive Representation Learning on Large Graphs. arXiv:cs.SI/1706.02216
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv:cs.LG/1412.6980
- Thomas N. Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. arXiv:cs.LG/1609.02907
- Effective Approaches to Attention-based Neural Machine Translation. arXiv:cs.CL/1508.04025
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579–2605.
- Distributed Representations of Words and Phrases and their Compositionality. arXiv:cs.CL/1310.4546
- The Graph Neural Network Model. IEEE Transactions on Neural Networks 20, 1 (2009), 61–80.
- Attention Is All You Need. arXiv:cs.CL/1706.03762
- Graph Attention Networks. arXiv:stat.ML/1710.10903
- Embedding Entities and Relations for Learning and Inference in Knowledge Bases. arXiv:cs.CL/1412.6575
- Revisiting Semi-Supervised Learning with Graph Embeddings. arXiv:cs.LG/1603.08861