Name
Title | ||
|---|---|---|
Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigMM50055.2020.00043 | 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM) |
Keywords | DocType | ISBN |
personalised outfit recommendation,cold-start problem,visual preference modelling,feature-weighted clustering | Conference | 978-1-7281-9326-7 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dhruv Verma | 1 | 1 | 1.02 |
| Kshitij Gulati | 2 | 0 | 0.34 |
| Rajiv Ratn Shah | 3 | 223 | 59.73 |