Journal
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
Volume 9, Issue 4, Pages 323-334Publisher
ELSEVIER
DOI: 10.1016/j.elerap.2010.01.001
Keywords
Academic literature; Coauthorship; Hybrid method; Networks; Recommender system; Social networks; Web 2.0
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Funding
- National Science Council of the Republic of China [NSC 97-2752-H-110-005-PAE, NSC 97-2752-H-007-003-PAE]
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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. (C) 2010 Elsevier B.V. All rights reserved.
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