“Does it come in black?” CLIP-like models are zero-shot recommenders
Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, Diogo Goncalves
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.- Anthology ID:
- 2022.ecnlp-1.22
- Volume:
- Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ECNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 191–198
- Language:
- URL:
- https://aclanthology.org/2022.ecnlp-1.22
- DOI:
- 10.18653/v1/2022.ecnlp-1.22
- Cite (ACL):
- Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, and Diogo Goncalves. 2022. “Does it come in black?” CLIP-like models are zero-shot recommenders. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 191–198, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- “Does it come in black?” CLIP-like models are zero-shot recommenders (Chia et al., ECNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.ecnlp-1.22.pdf