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

• URL: http://arxiv.org/abs/2008.01437v1
• Date: Tue, 4 Aug 2020 10:07:09 GMT
• Title: Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling
• Authors: Dhruv Verma, Kshitij Gulati and Rajiv Ratn Shah
• Abstract summary: This paper attempts to address the cold-start problem for new users by leveraging a novel visual preference modelling approach. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation.
• Score: 51.147871738838305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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