Nowadays, many people frequently have to search for new accommodation
options. Searching for a suitable apartment is a time-consuming process,
especially because visiting them is often mandatory to assess the truthfulness
of the advertisements found on the Web. While this process could be alleviated
by visiting the apartments in the metaverse, the Web-based recommendation
platforms are not suitable for the task. To address this shortcoming, in this
paper, we define a new problem called text-to-apartment recommendation, which
requires ranking the apartments based on their relevance to a textual query
expressing the user's interests. To tackle this problem, we introduce FArMARe,
a multi-task approach that supports cross-modal contrastive training with a
furniture-aware objective. Since public datasets related to indoor scenes do
not contain detailed descriptions of the furniture, we collect and annotate a
dataset comprising more than 6000 apartments. A thorough experimentation with
three different methods and two raw feature extraction procedures reveals the
effectiveness of FArMARe in dealing with the problem at hand.

为了应对在网络上查找适合的公寓时所面临的等待，本文定义了一个名为 “文本到公寓推荐” 的新问题，要求根据用户兴趣的文本查询对公寓进行排序。为了解决这个问题，文中引入了一个名为 FArMARe 的多任务方法，通过具有家具感知目标的交叉模态对比训练来支持。对三种不同的方法和两种原始特征提取过程进行了详细实验，并揭示了 FArMARe 在处理所面临的问题时的有效性。