A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity

Tribikram Pradhan, Sukomal Pal

Research output: Contribution to journalArticle

Abstract

Rapidly developing academic venues throw a challenge to researchers in identifying the most appropriate ones that are in-line with their scholarly interests and of high relevance. Even a high-quality paper is sometimes rejected due to a mismatch between the area of the paper, and the scope of the journal attempted to. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and to publish in impactful journals. Although a researcher may know a few leading high-profile venues for her specific field of interest, a venue recommender system becomes particularly helpful when one explores a new field or when more options are needed. We propose DISCOVER: A Diversified yet Integrated Social network analysis and COntextual similarity-based scholarly VEnue Recommender system. Our work provides an integrated framework incorporating social network analysis, including centrality measure calculation, citation and co-citation analysis, topic modeling based contextual similarity, and key-route identification based main path analysis of a bibliographic citation network. The paper also addresses cold start issues for a new researcher and a new venue along with a considerable reduction in data sparsity, computational costs, diversity, and stability problems. Experiments based on the Microsoft Academic Graph (MAG) dataset show that the proposed DISCOVER outperforms state-of-the-art recommendation techniques using standard metrics of precision@k, nDCG@k, accuracy, MRR, F−measuremacro, diversity, stability, and average venue quality.

Original languageEnglish
JournalFuture Generation Computer Systems
DOIs
Publication statusAccepted/In press - 01-01-2019

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity'. Together they form a unique fingerprint.

  • Cite this