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2022 ACL ACL 2022

“Does it come in black?” CLIP-like models are zero-shot recommenders

Abstract

AbstractProduct 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.

❓ The Questioner
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — product discovery
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Patrick John Chia , Jacopo Tagliabue , Federico Bianchi , Ciro Greco , Diogo Goncalves

Topics

Machine Learning > Learning Types > Zero-Shot Learning Data Science & Analytics > Applications > Recommender Systems

Keywords

zero-shot learning clip model recommender system product discovery comparative recommendation
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