期刊
INFORMATION SCIENCES
卷 479, 期 -, 页码 231-249出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.12.003
关键词
Interaction behaviours; Dynamic interaction tripartite graph; Attention-driven CNN; Multi-objective learning peer recommendation
资金
- Major Project of the National Social Science Fund of China [18ZDA334]
- National Natural Science Foundation of China [61877020, 61370178]
- S&T Project of Guangdong Province [2015A030401087]
Learning peer recommendation (LPR) is one of the effective solutions to overcome the information load of learners. This paper presents a multi-objective LPR framework for online learning. Using a dynamic interaction tripartite graph (DITG), we characterize and model the complex relationships among learners, learning content, and interaction behaviours, followed by capturing the dynamic interactions among learners with an attention-driven convolution neural network (CNN). The proposed attention-driven CNN is leveraged to tune the weights of interaction behaviours according to the features of the learning content. A multi-objective function composed of three conflicting metrics, interaction intensity, diversity and novelty, is optimized to achieve simultaneous multiple recommendations for a group of learners. Compared to the state-of-the-art approaches, the proposed LPR framework and algorithms perform favourably. (C) 2018 Elsevier Inc. All rights reserved.
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