  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.
