Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning
Abstract
The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners. As a critical motivate for human behaviors, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we aim to incorporate the element of curiosity for guiding learners to study spontaneously. In this paper, a curiosity-driven recommendation policy is proposed under the reinforcement learning framework, allowing for a both efficient and enjoyable personalized learning mode. Given intrinsic rewards from a well-designed predictive model, we apply the actor-critic method to approximate the policy directly through neural networks. Numeric analyses with a large continuous knowledge state space and concrete learning scenarios are used to further demonstrate the power of the proposed method.
Cite
@article{arxiv.1910.12577,
title = {Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning},
author = {Ruijian Han and Kani Chen and Chunxi Tan},
journal= {arXiv preprint arXiv:1910.12577},
year = {2019}
}