This study introduces a hybrid, trust-based recommender system designed to address the challenges of bounded rationality and metacognition in online Communities of Practice (CoPs), particularly under conditions of information overload and the absence of a central knowledge authority. Using Stack Overflow data for a case study, the research highlights that participants in online CoPs form stronger social ties and prefer interactions within a smaller, select group. The findings reveal that the hybrid recommender system outperforms both celebrity-based and content-based algorithms in accuracy, promoting the development of personalized learning communities within online CoPs.
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Zheng, Xiao-Lin, et al. "A hybrid trust-based recommender system for online communities of practice." IEEE Transactions on Learning Technologies 8.4 (2015): 345-356.
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