Style Similarity as Feedback for Product Design

TL;DR

This paper develops a style-based similarity metric for furniture products to enhance product recommendations by analyzing visual style compatibility, aiding designers in creating more universally compatible furniture.

Contribution

It introduces a novel style similarity metric and a designer-in-the-loop workflow for assessing furniture compatibility, advancing product design and recommendation methods.

Findings

Identifies key style attributes for furniture compatibility

Provides visualization tools for style analysis

Supports design decisions for cross-style furniture compatibility

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.