  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.
