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Chronological citation recommendation with time preference

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Abstract

Citation recommendation is an important task to assist scholars in finding candidate literature to cite. Traditional studies focus on static models of recommending citations, which do not explicitly distinguish differences between papers that are caused by temporal variations. Although, some researchers have investigated chronological citation recommendation by adding time related function or modeling textual topics dynamically. These solutions can hardly cope with function generalization or cold-start problems when there is no information for user profiling or there are isolated papers never being cited. With the rise and fall of science paradigms, scientific topics tend to change and evolve over time. People would have the time preference when citing papers, since most of the theoretical basis exist in classical readings that published in old time, while new techniques are proposed in more recent papers. To explore chronological citation recommendation, this paper wants to predict the time preference based on user queries, which is a probability distribution of citing papers published in different time slices. Then, we use this time preference to re-rank the initial citation list obtained by content-based filtering. Experimental results demonstrate that task performance can be further enhanced by time preference and it’s flexible to be added in other citation recommendation frameworks.

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Notes

  1. Available at: https://www.ncbi.nlm.nih.gov/pmc/.

  2. Available at: https://www.aminer.cn/citation.

  3. Available at: https://github.com/usnistgov/trec_eval.

  4. Available at: https://radimrehurek.com/gensim/models/doc2vec.html.

  5. Available at: https://github.com/thunlp/OpenNE.

  6. Available at: https://scikit-learn.org/stable/modules/preprocessing.html.

  7. Available at: https://radimrehurek.com/gensim/models/ldaseqmodel.html.

  8. Available at: https://github.com/elifesciences/citerank.

  9. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=22775499%5Buid%5D.

  10. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=21048124%5Buid%5D.

  11. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=12722974%5Buid%5D.

  12. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=20808844%5Buid%5D.

  13. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=18757819%5Buid%5D.

  14. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=15335669%5Buid%5D.

  15. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=9157250%5Buid%5D.

  16. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=19251655%5Buid%5D.

  17. Available at: https://www.ncbi.nlm.nih.gov/pubmed/?term=17276342%5Buid%5D.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 72074113), Science Fund for Creative Research Group of the National Natural Science Foundation of China (No. 71921002) and Major Projects of National Social Science Fund (No. 16ZDA224).

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Authors and Affiliations

  1. Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094, China

    Shutian Ma, Heng Zhang & Chengzhi Zhang

  2. Department of Information & Library Science, Indiana University, Bloomington, USA

    Xiaozhong Liu

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  1. Shutian Ma
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  2. Heng Zhang
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  3. Chengzhi Zhang
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  4. Xiaozhong Liu
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Correspondence to Chengzhi Zhang.

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Ma, S., Zhang, H., Zhang, C. et al. Chronological citation recommendation with time preference. Scientometrics 126, 2991–3010 (2021). https://doi.org/10.1007/s11192-021-03878-2

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