FArMARe: a Furniture-Aware Multi-task methodology for Recommending
  Apartments based on the user interests
Ali Abdari Alex Falcon Giuseppe Serra
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.