  Personalized interior decoration design often incurs high labor costs. Recent
efforts in developing intelligent interior design systems have focused on
generating textual requirement-based decoration designs while neglecting the
problem of how to mine homeowner's hidden preferences and choose the proper
initial design. To fill this gap, we propose an Interactive Interior Design
Recommendation System (IIDRS) based on reinforcement learning (RL). IIDRS aims
to find an ideal plan by interacting with the user, who provides feedback on
the gap between the recommended plan and their ideal one. To improve
decision-making efficiency and effectiveness in large decoration spaces, we
propose a Decoration Recommendation Coarse-to-Fine Policy Network (DecorRCFN).
Additionally, to enhance generalization in online scenarios, we propose an
object-aware feedback generation method that augments model training with
diversified and dynamic textual feedback. Extensive experiments on a real-world
dataset demonstrate our method outperforms traditional methods by a large
margin in terms of recommendation accuracy. Further user studies demonstrate
that our method reaches higher real-world user satisfaction than baseline
methods.
