SRRS: Design and Development of a Scholarly Reciprocal Recommendation System

Shilpa Verma (), Sandeep Harit and Kundan Munjal
Additional contact information
Shilpa Verma: Punjab Engineering College
Sandeep Harit: Punjab Engineering College
Kundan Munjal: Punjabi University

Scientometrics, 2024, vol. 129, issue 11, No 14, 6839-6866

Abstract: 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.

Keywords: Reciprocal recommendation; Content filtering; Collaborative filtering; Clustering (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-024-05143-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05143-8

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-024-05143-8

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().