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SRRS: Design and Development of a Scholarly Reciprocal Recommendation System

Author

Listed:
  • Shilpa Verma

    (Punjab Engineering College)

  • Sandeep Harit

    (Punjab Engineering College)

  • Kundan Munjal

    (Punjabi University)

Abstract

The aim of this work is to propose a hybrid reciprocal recommendation algorithm for cold-start authors in a network based on text information and network-based features. The proposed algorithm is a novel collaborative filtering algorithm that combines text information with network features for more accurate and personalized recommendations. The feature importance values are used to understand the impact of each feature on the prediction and to identify the most important features for a given task. In the proposed algorithm, a community detection algorithm is used in addition to the baseline method, which uses a first-order neighborhood approach. Furthermore, varying T on edge weights in the co-author graph with optimal T is used to obtain hybrid recommendations in the same community. The results demonstrate that the proposed method is effective in predicting collaborators for cold-start authors in the network.

Suggested Citation

  • Shilpa Verma & Sandeep Harit & Kundan Munjal, 2024. "SRRS: Design and Development of a Scholarly Reciprocal Recommendation System," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6839-6866, November.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05143-8
    DOI: 10.1007/s11192-024-05143-8
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    References listed on IDEAS

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    1. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    2. 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.
    3. Alhoori, Hamed & Furuta, Richard, 2017. "Recommendation of scholarly venues based on dynamic user interests," Journal of Informetrics, Elsevier, vol. 11(2), pages 553-563.
    Full references (including those not matched with items on IDEAS)

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