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Coauthorship networks and academic literature recommendation

Electronic Commerce Research and Applications
Publication Date
DOI: 10.1016/j.elerap.2010.01.001
  • Academic Literature
  • Coauthorship
  • Hybrid Method
  • Networks
  • Recommender System
  • Social Networks
  • Web 2.0


Abstract Recommender systems are increasingly touted as an indispensable service of many online stores and websites. Most existing recommendation techniques typically rely on users’ historical, long-term interest profiles, derived either explicitly from users’ preference ratings or implicitly from their purchasing/browsing history, to arrive at recommendation decisions. In this study, we propose a coauthorship network-based, task-focused literature recommendation technique to meet users’ information need specific to a task under investigation and develop three different schemes for estimating the closeness between scholars based on their coauthoring relationships. We empirically evaluate the proposed coauthorship network-based technique. The evaluation results suggest that our proposed technique outperforms the author-based technique across various degrees of content coherence in task profiles. The proposed technique is more effective than the content-based technique when task profiles specified by users are similar in their contents but is less effective otherwise. We further develop a hybrid method that switches between the coauthorship network-based and content-based techniques on the basis of the content coherence of a task profile. It achieves comparable or better recommendation effectiveness, when compared with the pure coauthorship network-based and content-based techniques.

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