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Nowadays, many people frequently have to search for new accommodationoptions. Searching for a suitable apartment is a time-consuming process,especially because visiting them is often mandatory to assess the truthfulnessof the advertisements found on the Web. While this process could be alleviatedby visiting the apartments in the metaverse, the Web-based recommendationplatforms are not suitable for the task. To address this shortcoming, in thispaper, we define a new problem called text-to-apartment recommendation, whichrequires ranking the apartments based on their relevance to a textual queryexpressing the user's interests. To tackle this problem, we introduce FArMARe,a multi-task approach that supports cross-modal contrastive training with afurniture-aware objective. Since public datasets related to indoor scenes donot contain detailed descriptions of the furniture, we collect and annotate adataset comprising more than 6000 apartments. A thorough experimentation withthree different methods and two raw feature extraction procedures reveals theeffectiveness of FArMARe in dealing with the problem at hand.

FArMARe: a Furniture-Aware Multi-task methodology for Recommending Apartments based on the user interests