Enhancing Visual Fashion Recommendations with Users in the Loop

TL;DR

This paper introduces a user-centric visual fashion recommendation system that leverages user preferences and behavioral signals to improve recommendation quality and aesthetic appeal.

Contribution

It proposes a novel method to incorporate user preferences and behavioral signals into large-scale visual fashion recommendations, enhancing user satisfaction.

Findings

User preferences within fashion classes contain important behavioral signals.

Fashion classes correlate strongly with visual perception.

User approval improves with user-centric feedback integration.

Abstract

We describe a completely automated large scale visual recommendation system for fashion. Existing approaches have primarily relied on purely computational models to solving this problem that ignore the role of users in the system. In this paper, we propose to overcome this limitation by incorporating a user-centric design of visual fashion recommendations. Specifically, we propose a technique that augments 'user preferences' in models by exploiting elasticity in fashion choices. We further design a user study on these choices and gather results from the 'wisdom of crowd' for deeper analysis. Our key insights learnt through these results suggest that fashion preferences when constrained to a particular class, contain important behavioral signals that are often ignored in recommendation design. Further, presence of such classes also reflect strong correlations to visual perception which can be utilized to provide aesthetically pleasing user experiences. Finally, we illustrate that user approval of visual fashion recommendations can be substantially improved by carefully incorporating these user-centric feedback into the system framework.