# Content-Based Citation Recommendation

arXiv: https://arxiv.org/abs/1802.08301 · HTML: https://mlsanity.com/paper/1802.08301/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 30 of 43)

- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval](https://mlsanity.com/paper/2307.00589/) (2023)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [LitSearch: A Retrieval Benchmark for Scientific Literature Search](https://mlsanity.com/paper/2407.18940/) (2024)
- [CiteME: Can Language Models Accurately Cite Scientific Claims?](https://mlsanity.com/paper/2407.12861/) (2024)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://mlsanity.com/paper/2303.14957/) (2023)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking](https://mlsanity.com/paper/2112.01206/) (2021)
- [CDLM: Cross-Document Language Modeling](https://mlsanity.com/paper/2101.00406/) (2021)
- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents](https://mlsanity.com/paper/2205.15051/) (2022)
- [Improving Wikipedia Verifiability with AI](https://mlsanity.com/paper/2207.06220/) (2022)
- [How Neural Processes Improve Graph Link Prediction](https://mlsanity.com/paper/2109.14894/) (2021)
- [PubGraph: A Large-Scale Scientific Knowledge Graph](https://mlsanity.com/paper/2302.02231/) (2023)
- [Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching](https://mlsanity.com/paper/2101.06423/) (2021)
- [CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring](https://mlsanity.com/paper/2110.15794/) (2021)
- [KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models](https://mlsanity.com/paper/2404.09763/) (2024)
- [SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts](https://mlsanity.com/paper/2507.13105/) (2025)
- [Why Did You Not Compare With That? Identifying Papers for Use as Baselines](https://mlsanity.com/paper/2201.08089/) (2022)
- [Large-scale Evaluation of Transformer-based Article Encoders on the Task of Citation Recommendation](https://mlsanity.com/paper/2209.05452/) (2022)
- [Anchor Prediction: Automatic Refinement of Internet Links](https://mlsanity.com/paper/2305.14337/) (2023)
- [Transferability of Graph Neural Networks using Graphon and Sampling Theories](https://mlsanity.com/paper/2307.13206/) (2023)
- [SymTax: Symbiotic Relationship and Taxonomy Fusion for Effective Citation Recommendation](https://mlsanity.com/paper/2406.01606/) (2024)
- [Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens](https://mlsanity.com/paper/2309.04333/) (2023)
- [Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation](https://mlsanity.com/paper/2504.00187/) (2025)
- [SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment](https://mlsanity.com/paper/2505.20103/) (2025)
- [Knowledge-based XAI through CBR: There is more to explanations than models can tell](https://mlsanity.com/paper/2108.10363/) (2021)

## Abstract (arXiv)

We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup. We release an online portal (http://labs.semanticscholar.org/citeomatic/) for citation recommendation based on our method, and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.