Image-based Recommendations on Styles and Substitutes
This addresses the need for automated style and substitute recommendations in e-commerce and fashion, though it is incremental as it builds on existing network inference techniques.
The paper tackled the problem of modeling human perceptions of visual relationships between objects, such as substitutes and complements, by developing a scalable network inference method on large image datasets, resulting in a system capable of recommending compatible clothes and accessories.
Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.