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.

翻译：个性化室内装修设计常需高昂的人力成本。近年来智能室内设计系统的研究主要聚焦于基于文本需求生成装修设计方案，但忽略了如何挖掘房主潜在偏好并选择合适初始设计的问题。为填补这一空缺，我们提出基于强化学习的交互式室内设计推荐系统（IIDRS）。该系统通过用户对推荐方案与理想方案差距的反馈，与用户互动寻找最优方案。为提升大规模装修空间中决策的效率与效果，我们提出由粗到细的装修推荐策略网络（DecorRCFN）。此外，为增强在线场景下的泛化能力，我们提出一种面向对象的反馈生成方法，通过多样化动态文本反馈增强模型训练。在真实数据集上的大量实验表明，本方法在推荐准确率上大幅超越传统方法。进一步用户研究显示，本方法在真实用户满意度上显著优于基线方法。