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Citation recommendation employing heterogeneous bibliographic network embedding

  • S. I. : Effective and Efficient Deep Learning
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Abstract

The massive number of research articles on the Web makes it troublesome for researchers to identify related works that could meet their preferences and interests. Consequently, various network representation learning-based models have been proposed to produce citation recommendations. Nevertheless, these models do not exploit semantic relations and contextual information between the objects of bibliographic papers’ networks, which can result in inadequate citation recommendations. Moreover, existing citation recommendation methods face problems such as lack of personalization, cold-start, and network sparsity. To mitigate such problems and produce individualized citation recommendations, we propose a heterogeneous network embedding model that jointly learns node representations by exploiting semantics corresponding to the author, time, context, field of study, citations, and topics. Compared to baseline models, the results produced by the proposed model over the DBLP datasets prove 10% and 12% improvement on mean average precision (MAP) and normalized discounted cumulative gain (nDCG@10) metrics, respectively. Also, the effectiveness of our model is analyzed on the cold-start papers and network sparsity problems, where it gains 12% and 9% better MAP and recall@10 scores, respectively.

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Notes

  1. https://lfs.aminer.cn/misc/dblp.v11.zip

  2. https://originalstatic.aminer.cn/misc/dblp.v12.7z

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Authors and Affiliations

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

    Zafar Ali, Guilin Qi & Waheed Abro

  2. Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747, Republic of Korea

    Khan Muhammad

  3. Rajnagar Mahavidyalaya, Birbhum, India

    Siddhartha Bhattacharyya

  4. Department of Computer Science, Shaheed Benazir Bhutto University, Sheringal, Pakistan

    Irfan Ullah

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  1. Zafar Ali
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Correspondence to Zafar Ali or Khan Muhammad.

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Ali, Z., Qi, G., Muhammad, K. et al. Citation recommendation employing heterogeneous bibliographic network embedding. Neural Comput & Applic 34, 10229–10242 (2022). https://doi.org/10.1007/s00521-021-06135-y

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