Visual Uniqueness: An Unsupervised Contrast Learning Approach
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2024-01-03
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2505
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This paper develops an unsupervised machine learning model that scores a product image on its visual uniqueness. Based on large-scale images of Airbnb properties in New York City, our model used contrastive loss and random data augmentation to compute the visual uniqueness of a property image automatically. The model achieves 88.10% accuracy on a hold-out set. We identified key image features that make a room unique. Leveraging the advanced explainable AI techniques to generate interpretable uniqueness heatmaps, we found certain decorations (e.g., pillows, paintings) may help enhance room uniqueness. Next, we validated the model against human perceptions via two lab studies and an eye-tracking controlled experiment: both the model-predicted uniqueness and key image features are consistent with human judgment. We discussed discriminative validity between uniqueness and aesthetics. This research offers important managerial implications for individual hosts to optimize the visual presentation to stand out in the crowded market.
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Data Analytics, Data Mining, and Machine Learning for Social Media, airbnb, contrastive learning, controlled experiment, eye-tracking, image analytics, visual uniqueness, xai (explainable ai)
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11 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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