Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling

TL;DR

This paper proposes a visual preference modeling approach to improve outfit recommendations for new users, effectively addressing the cold-start problem by leveraging input images and feature-weighted clustering.

Contribution

It introduces a novel visual preference modeling method combined with feature-weighted clustering to enhance personalization in cold-start outfit recommendation scenarios.

Findings

Outperforms state-of-the-art in clothing attribute prediction

Provides diverse and personalized recommendations in cold-start scenarios

Demonstrates effectiveness through quantitative and qualitative evaluations

Abstract

With the global transformation of the fashion industry and a rise in the demand for fashion items worldwide, the need for an effectual fashion recommendation has never been more. Despite various cutting-edge solutions proposed in the past for personalising fashion recommendation, the technology is still limited by its poor performance on new entities, i.e. the cold-start problem. In this paper, we attempt to address the cold-start problem for new users, by leveraging a novel visual preference modelling approach on a small set of input images. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation. Quantitatively, our results show that the proposed visual preference modelling approach outperforms state of the art in terms of clothing attribute prediction. Qualitatively, through a pilot study, we demonstrate the efficacy of our system to provide diverse and personalised recommendations in cold-start scenarios.