Learning peer recommendation using attention-driven CNN with interaction tripartite graph

Abstract 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.