With the proliferation of the online fashion industry, there have been increased efforts towards building cutting-edge solutions for personalising fashion recommendation. Despite this, the technology is still limited by its poor performance on new entities, i.e. the cold-start problem. 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. Additionally, we describe our proposed strategy to incorporate the modelled preference in occasion-oriented outfit recommendation. Finally, we propose Fashionist: a real-time web application to demonstrate our approach enabling personalised and diverse outfit recommendation for cold-start scenarios.
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Kindly cite our papers if you use our code
Dhruv Verma, Kshitij Gulati, Vasu Goel, and Rajiv Ratn Shah. 2020. Fashionist: Personalising Outfit Recommendation for Cold-Start Scenarios. In
Proceedings of the 28th ACM International Conference on Multimedia (MM'20). DOI:https://doi.org/10.1145/3394171.3414446
Dhruv Verma, Kshitij Gulati and Rajiv Ratn Shah. 2020. Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling.
In Proceedings of the IEEE Sixth International Conference on Multimedia Big Data (BigMM'20). DOI: 10.1109/BigMM50055.2020.00043