What is Citation Recommendation?
Citation recommendation is the automated suggestion of scholarly references that are relevant to a given document, query, or research context. It helps authors discover pertinent works, improves literature reviews, and enhances the completeness of academic writing.
How Citation Recommendation Works
Data Sources
- Bibliographic databases (e.g., Scopus, Web of Science, Microsoft Academic).
- Full‑text repositories (e.g., arXiv, PubMed Central).
- Citation graphs that capture who cites whom.
Modeling Approaches
- Content‑Based Methods: Use textual similarity (TF‑IDF, embeddings) between the target document and candidate papers.
- Collaborative Filtering: Leverage patterns of co‑citation and user interaction histories.
- Graph‑Based Techniques: Apply algorithms such as PageRank, random walks, or graph neural networks on citation networks.
- Hybrid Models: Combine content and graph signals; examples include SymTax, which integrates semantic taxonomy with citation structure.
Why Use Citation Recommendation?
- Efficiency: Reduces time spent manually searching for relevant literature.
- Comprehensiveness: Increases the likelihood of including seminal and recent works.
- Discovery: Surfaces interdisciplinary or less‑known papers that might be missed otherwise.
- Quality Assurance: Helps avoid citation gaps that can affect peer review and research impact.