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

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

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

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arxiv 2204.02473 v2 pith:K3DEZO4L submitted 2022-04-05 cs.IR cs.AI

Patrick John Chia , Jacopo Tagliabue , Federico Bianchi , Ciro Greco , Diogo Goncalves This is my paper · ORCID

classification cs.IRcs.AI

keywords itemmodelmodelsrecommendationssomethingzero-shotallowalong

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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.

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ENCLIP: Ensembling and Clustering-Based Contrastive Language-Image Pretraining for Fashion Multimodal Search with Limited Data and Low-Quality Images
cs.CV 2024-11 reject novelty 3.0 of 10

ENCLIP uses an ensemble of epoch-staggered CLIP fine-tunes plus K-means clustering to rank fashion search results, reporting gains over CLIP and FashionCLIP on a 44k-image dataset.

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