Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning

Abstract Recommender system is regarded as a crucial strategy for realizing personalized e-learning. To better enhance the learning outcomes for learners, exercise recommendation plays a vital role in this system. However, current modeling methods are insufficient to fully capture the dynamic process of learners’ knowledge concept forgetting. Moreover, recommending learning resources based solely on the learner’s current level, without exploring the learner’s learning intentions, leads to inefficient utilization of learning resources. To address these issues, this paper proposes a novel Intention-aware Exercise Recommendation enhanced by deep Forgetting modeling for e-learning (IERF). Specifically, we integrate the forgetting mechanism into multi-concept sequences, model the contextual dependencies of sequences, and design behavioral balancing factors in multiple dimensions to optimize the forgetting prediction of the learning process. Additionally, we construct a heterogeneous information network (HIN) of the learning process and design multi-attention mechanisms to highlight high-order learning relationships between networks and perceive the learner’s learning intentions. Experimental results on three public real-world datasets show that the proposed model outperforms state-of-the-art baselines, and enhances the interpretability under the exercise recommendation.

Abstract licence: CC BY-NC-ND

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