BeautifAI - Personalised Occasion-based Makeup Recommendation

Kshitij Gulati, Gaurav Verma, Mukesh Mohania, Ashish Kundu
Proceedings of The 14th Asian Conference on Machine Learning, PMLR 189:407-419, 2023.

Abstract

With the global metamorphosis of the beauty industry and the rising demand for beauty products worldwide, the need for a robust makeup recommendation system has never been more. Despite the significant advancements made towards personalised makeup recommendation, the current research still falls short of incorporating the context of occasion and integrating feedback for users. In this work, we propose BeautifAI, a novel recommendation system, delivering personalised occasion-oriented makeup recommendations to users. The proposed work’s novel contributions, including incorporating occasion context to makeup recommendation and a region-wise method using neural embeddings, set our system apart from the current work in makeup recommendation. We also propose real-time makeup previews and continuous makeup feedback to provide a more personalised and interactive experience to users.

Cite this Paper

BibTeX
@InProceedings{pmlr-v189-gulati23a, title = {BeautifAI - Personalised Occasion-based Makeup Recommendation}, author = {Gulati, Kshitij and Verma, Gaurav and Mohania, Mukesh and Kundu, Ashish}, booktitle = {Proceedings of The 14th Asian Conference on Machine Learning}, pages = {407--419}, year = {2023}, editor = {Khan, Emtiyaz and Gonen, Mehmet}, volume = {189}, series = {Proceedings of Machine Learning Research}, month = {12--14 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v189/gulati23a/gulati23a.pdf}, url = {https://proceedings.mlr.press/v189/gulati23a.html}, abstract = {With the global metamorphosis of the beauty industry and the rising demand for beauty products worldwide, the need for a robust makeup recommendation system has never been more. Despite the significant advancements made towards personalised makeup recommendation, the current research still falls short of incorporating the context of occasion and integrating feedback for users. In this work, we propose BeautifAI, a novel recommendation system, delivering personalised occasion-oriented makeup recommendations to users. The proposed work’s novel contributions, including incorporating occasion context to makeup recommendation and a region-wise method using neural embeddings, set our system apart from the current work in makeup recommendation. We also propose real-time makeup previews and continuous makeup feedback to provide a more personalised and interactive experience to users.} }
Endnote
%0 Conference Paper %T BeautifAI - Personalised Occasion-based Makeup Recommendation %A Kshitij Gulati %A Gaurav Verma %A Mukesh Mohania %A Ashish Kundu %B Proceedings of The 14th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Emtiyaz Khan %E Mehmet Gonen %F pmlr-v189-gulati23a %I PMLR %P 407--419 %U https://proceedings.mlr.press/v189/gulati23a.html %V 189 %X With the global metamorphosis of the beauty industry and the rising demand for beauty products worldwide, the need for a robust makeup recommendation system has never been more. Despite the significant advancements made towards personalised makeup recommendation, the current research still falls short of incorporating the context of occasion and integrating feedback for users. In this work, we propose BeautifAI, a novel recommendation system, delivering personalised occasion-oriented makeup recommendations to users. The proposed work’s novel contributions, including incorporating occasion context to makeup recommendation and a region-wise method using neural embeddings, set our system apart from the current work in makeup recommendation. We also propose real-time makeup previews and continuous makeup feedback to provide a more personalised and interactive experience to users.
APA
Gulati, K., Verma, G., Mohania, M. & Kundu, A.. (2023). BeautifAI - Personalised Occasion-based Makeup Recommendation. Proceedings of The 14th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 189:407-419 Available from https://proceedings.mlr.press/v189/gulati23a.html.

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