1. ACPS
  2. All Volumes and Issues
  3. Volume 10, Number 1, 2025
  4. Contrastive Language-Image Pre-Training (CLIP) in E- Commerce: Applications, Methodologies, and Performance

Contrastive Language-Image Pre-Training (CLIP) in E- Commerce: Applications, Methodologies, and Performance

ACPS.
2025;
Volume 10, Number 1
: pp. 100 - 104
https://doi.org/10.23939/acps2025.01.100
Authors:
  1. Oleksandr Khainas
  2. Nataliia Melnykova
  3. Solomiia Fedushko
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University, Ukraine, Uniwersytet Rolniczy im. Hugona Kołłątaja
3
Lviv Polytechnic National University, Ukraine, Comenius University Bratislava

This article thoroughly examines the architecture and applications of the Contrastive Language-Image Pre-training (CLIP) model within the e-commerce domain, focusing on key tasks such as visual search, product recommendation, and attribute extraction. The article also provides an in-depth analysis of the methodologies used for CLIP’s adaptation to e-commerce tasks and the relevant datasets employed. By highlighting the unique capabilities of the CLIP model, such as its ability to perform zero-shot learning and contrastive pre-training, this article underscores its potential impact on the industry while also acknowledging its limitations, including the ‘domain gap’ and the need for adaptation strategies. Furthermore, the article explores the future research directions for enhancing CLIP’s performance in specialized e-commerce contexts and compares it with other traditional and multimodal AI techniques.

CLIP
Multimodal AI
e-commerce
Visual Search
Product Recommendation
Attribute Extraction
Contrastive Learning
zero-shot learning
Catalog Management
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