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A Hybrid Trust-Based Recommender System for Online Communities of Practice
IEEE Transactions on Learning Technologies
  • Xiao-Lin Zheng, Zhejiang University
  • Chao-Chao Chen, Zhejiang University
  • Jui-Long Hung, Boise State University
  • Wu He, Old Dominion University
  • Fu-Xing Hong, Zhejiang University
  • Zhen Lin, Zhejiang University
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The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.
Citation Information
Xiao-Lin Zheng, Chao-Chao Chen, Jui-Long Hung, Wu He, et al.. "A Hybrid Trust-Based Recommender System for Online Communities of Practice" IEEE Transactions on Learning Technologies (2015)
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