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A graph-based taxonomy of citation recommendation models

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Abstract

Recommender systems have been used since the beginning of the Web to assist users with personalized suggestions related to past preferences for items or products including books, movies, images, research papers and web pages. The availability of millions research articles on various digital libraries makes it difficult for a researcher to find relevant articles to his/er research. During the last years, a lot of research have been conducted through models and algorithms that personalize papers recommendations. With this survey, we explore the state-of-the-art citation recommendation models which we categorize using the following seven criteria: platform used, data factors/features, data representation methods, methodologies and models, recommendation types, problems addressed, and personalization. In addition, we present a novel k-partite graph-based taxonomy that examines the relationships among surveyed algorithms and corresponding k-partite graphs used. Moreover, we present (a) domain’s popular issues, (b) adopted metrics, and (c) commonly used datasets. Finally, we provide some research trends and future directions.

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Notes

  1. https://www.researchgate.net/

  2. https://www.ncbi.nlm.nih.gov/pubmed/

  3. http://www.citeulike.org/

  4. http://www.citeulike.org/faq/data.adp

  5. https://acl-arc.comp.nus.edu.sg/

  6. https://www.aminer.cn/citation

  7. https://dblp.uni-trier.de/

  8. https://www.aminer.cn/data

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Zafar Ali, Guilin Qi, Waheed Ahmad Abro & Bahadar Ali

  2. Department of Informatics, Aristotle University, Thessaloniki, Greece

    Pavlos Kefalas

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  1. Zafar Ali
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  2. Guilin Qi
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  3. Pavlos Kefalas
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  4. Waheed Ahmad Abro
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  5. Bahadar Ali
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Ali, Z., Qi, G., Kefalas, P. et al. A graph-based taxonomy of citation recommendation models. Artif Intell Rev 53, 5217–5260 (2020). https://doi.org/10.1007/s10462-020-09819-4

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