Abstract
Matching and recommending products is beneficial for both customers and companies. With the rapid increase in home goods e-commerce, there is an increasing demand for quantitative methods for providing such recommendations for millions of products. This approach is facilitated largely by online stores such as Amazon and Wayfair, in which the goal is to maximize overall sales. Instead of focusing on overall sales, we take a product design perspective, by employing big-data analysis for determining the design qualities of a highly recommended product. Specifically, we focus on the visual style compatibility of such products. We build off previous work which implemented a style-based similarity metric for thousands of furniture products. Using analysis and visualization, we extract attributes of furniture products that are highly compatible style-wise. We propose a designer in-the-loop workflow that mirrors methods of displaying similar products to consumers browsing e-commerce websites. Our findings are useful when designing new products, since they provide insight regarding what furniture will be strongly compatible across multiple styles, and hence, more likely to be recommended.
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Acknowledgements
We thank Ilkay Yildiz and Hantian Liu for their preliminary work at Wayfair for the design and experimentation with the neural network style estimation model.
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Appendix
Appendix
See Fig. 9.
Top-25 most frequently recommended images out of the 37,421 in our sample dataset, with the overall similarity threshold. Such image collection is independent of the products themselves, and exists across all of the original data, including low scoring similarities
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Schwartz, M., Weiss, T., Ataer-Cansizoglu, E., Choi, JW. (2021). Style Similarity as Feedback for Product Design. In: Lee, JH. (eds) A New Perspective of Cultural DNA. KAIST Research Series. Springer, Singapore. https://doi.org/10.1007/978-981-15-7707-9_3
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