Citation recommendation: approaches and datasets

By Michael Färber, Adam Jatowt. 2020

116 citations recorded by the source corpus.

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction to automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.

Subfields: Artificial Intelligence, Information Systems, Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

Improving Wikipedia verifiability with AI (2022, background): “…(McNee et al., 2002; Ren et al., 2014; Bhagavatula et al., 2018; Chou et al., 2020), albeit with less of a focus on veriﬁability of existing citations, with citations drawn from much smaller and less diverse sources than the open web, see Färber & Jatowt (2020) for a recent comprehensive review.”
LaMP: When Large Language Models Meet Personalization (2023, methodology): “The citation behavior of researchers is dependent on their interests and is commonly used to evaluate and develop personalized systems for recommending papers (Färber and Jatowt, 2020).”