Interactive Interior Design Recommendation via Coarse-to-fine Multimodal Reinforcement Learning
He Zhang,
Ying Sun,
Weiyu Guo
et al.
Abstract: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… Show more
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