A hybrid approach for article recommendation in research social networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

1 Scopus Citations
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Author(s)

  • Jianshan Sun
  • Yuanchun Jiang
  • Xusen Cheng
  • Wei Du
  • Yezheng Liu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)696-711
Journal / PublicationJournal of Information Science
Volume44
Issue number5
Early online date19 Sep 2017
Publication statusPublished - Oct 2018

Abstract

With the prevalence of research social networks, determining effective methods for recommending scientific articles to online scholars has become a challenging and complex task. Current studies on article recommendation works are focused on digital libraries and reference sharing websites while studies on research social networking websites have seldom been conducted. Existing content-based approaches or collaborative filtering approaches suffer from the problem of data sparsity. The quality information of articles has been largely ignored in previous studies, thus raising the need for a unified recommendation framework. We propose a hybrid approach to combine relevance, connectivity and quality to recommend scientific articles. The effectiveness of the proposed framework and methods is verified using a user study on a real research social network website. The results demonstrate that our proposed methods outperform baseline methods.

Research Area(s)

  • Article recommendation, connectivity analysis, quality analysis, relevance analysis, research social networks

Citation Format(s)

A hybrid approach for article recommendation in research social networks. / Sun, Jianshan; Jiang, Yuanchun; Cheng, Xusen; Du, Wei; Liu, Yezheng; Ma, Jian.

In: Journal of Information Science, Vol. 44, No. 5, 10.2018, p. 696-711.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review