Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Searches
It addresses the problem of improving apartment search experiences for users by enabling intuitive searches based on subjective criteria, though it is incremental in applying existing ML methods to a new domain-specific dataset.
This study tackled the problem of predicting subjective functionality and comfort scores for apartment floor plans using machine learning, resulting in a new apartment search system that provides a better user experience, as shown in a large-scale usability study.
This study presents a new user experience in apartment searches using functionality and comfort as query items. This study has three technical contributions. First, we present a new dataset on the perceived functionality and comfort scores of residential floor plans using nine question statements about the level of comfort, openness, privacy, etc. Second, we propose an algorithm to predict the scores from the floor plan images. Lastly, we implement a new apartment search system and conduct a large-scale usability study using crowdsourcing. The experimental results show that our apartment search system can provide a better user experience. To the best of our knowledge, this study is the first work to propose a highly accurate prediction model for the subjective functionality and comfort of apartments using machine learning.