IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i11d10.1007_s11192-024-05143-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-024-05143-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-024-05143-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05143-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.