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Exploring dynamic research interest and academic influence for scientific collaborator recommendation

Author

Listed:
  • Xiangjie Kong

    (Dalian University of Technology)

  • Huizhen Jiang

    (Dalian University of Technology)

  • Wei Wang

    (Dalian University of Technology)

  • Teshome Megersa Bekele

    (Dalian University of Technology)

  • Zhenzhen Xu

    (Dalian University of Technology)

  • Meng Wang

    (Dalian University of Technology)

Abstract

In many cases, it is time-consuming for researchers to find proper collaborators who can provide researching guidance besides simply collaborating. The Most Beneficial Collaborators (MBCs), who have high academic level and relevant research topics, can genuinely help researchers to enrich their research. However, how can we find the MBCs? In this paper, we propose a most Beneficial Collaborator Recommendation model called BCR. BCR learns on researchers’ publications and associates three academic features: topic distribution of research interest, interest variation with time and researchers’ impact in collaborators network. We run a topic model on researchers’ publications in each year for topic clustering. The generated topic distribution matrix is fixed by a time function to fit the interest dynamic transformation. The academic social impact is also considered to mine the most prolific researchers. Finally, a TopN MBCs recommendation list is generated according to the computed score. Extensive experiments on a dataset with citation network demonstrate that, in comparison to relevant baseline approaches, our BCR performs better in terms of precision, recall, F1 score and the recommendation quality.

Suggested Citation

  • Xiangjie Kong & Huizhen Jiang & Wei Wang & Teshome Megersa Bekele & Zhenzhen Xu & Meng Wang, 2017. "Exploring dynamic research interest and academic influence for scientific collaborator recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 369-385, October.
  • Handle: RePEc:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2485-9
    DOI: 10.1007/s11192-017-2485-9
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    Citations

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    Cited by:

    1. Xiaowen Xi & Jiaqi Wei & Ying Guo & Weiyu Duan, 2022. "Academic collaborations: a recommender framework spanning research interests and network topology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6787-6808, November.
    2. Kong, Xiangjie & Mao, Mengyi & Jiang, Huizhen & Yu, Shuo & Wan, Liangtian, 2019. "How does collaboration affect researchers’ positions in co-authorship networks?," Journal of Informetrics, Elsevier, vol. 13(3), pages 887-900.
    3. Qi Zhang & Rui Mao & Rui Li, 2019. "Spatial–temporal restricted supervised learning for collaboration recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1497-1517, June.
    4. Guoqiang Liang & Haiyan Hou & Qiao Chen & Zhigang Hu, 2020. "Diffusion and adoption: an explanatory model of “question mark” and “rising star” articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 219-232, July.
    5. Samreen Ayaz & Nayyer Masood & Muhammad Arshad Islam, 2018. "Predicting scientific impact based on h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 993-1010, March.
    6. Liang, Guoqiang & Hou, Haiyan & Ding, Ying & Hu, Zhigang, 2020. "Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country," Journal of Informetrics, Elsevier, vol. 14(3).
    7. Diana Purwitasari & Chastine Fatichah & Surya Sumpeno & Christian Steglich & Mauridhi Hery Purnomo, 2020. "Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1407-1443, March.
    8. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
    9. Chaocheng He & Jiang Wu & Qingpeng Zhang, 2022. "Proximity‐aware research leadership recommendation in research collaboration via deep neural networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 70-89, January.
    10. Guoqiang Liang & Haiyan Hou & Xiaodan Lou & Zhigang Hu, 2019. "Qualifying threshold of “take-off” stage for successfully disseminated creative ideas," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1193-1208, September.

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