Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 20, Issue 6, Pages 1499-1511Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2017.2769444
Keywords
Ambiance prediction; first impressions; home environments; Airbnb; social media; image processing
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Funding
- EPFL-UNIL CROSS program through the Mi Casa es su Casa project
- Swiss National Science Foundation through the UBImpressed Sinergia project
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Airbnb is changing the landscape of the hospitality industry, and to this day, little is known about the inferences that guests make about Airbnb listings. Our work constitutes a first attempt at understanding how potential Airbnb guests form first impressions from images, one of the main modalities featured on the platform. We contribute to the multimedia community by proposing the novel task of automatically predicting human impressions of ambiance from pictures of listings on Airbnb. We collected Airbnb images, focusing on the countries Switzerland and Mexico as case studies, and used crowdsourcing mechanisms to gather annotations on physical and ambiance attributes, finding that agreement among raters was high for most of the attributes. Our cluster analysis showed that both physical and psychological attributes could be grouped into three clusters. We then extracted state-of-the-art features from the images to automatically infer the annotated variables in a regression task. Results show the feasibility of predicting ambiance impressions of homes on Airbnb, with up to 42% of the variance explained by our model, and best results were obtained using activation layers of deep convolutional neural networks trained on the Places dataset, a collection of scene-centric images.
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