Abstract
Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. “something darker”) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.
Cite
CITATION STYLE
Chia, P. J., Tagliabue, J., Bianchi, F., Greco, C., & Goncalves, D. (2022). “Does it come in black?” CLIP-like models are zero-shot recommenders. In ECNLP 2022 - 5th Workshop on e-Commerce and NLP, Proceedings of the Workshop (pp. 191–198). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.ecnlp-1.22
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