Complete the Look Recommendations Model Version

What's Complete the Look?

‍Given a fashion product, we recommend visually complimentary products that match the chosen product and thus effectively "completing the look". For example, for a T-shirt, we recommend bottom-wear apparel, a footwear product and a collection of accessories.

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We are thrilled to unveil our latest release introducing a cutting-edge Generative AI and ML Automation Pipeline designed to revolutionize fashion recommendations. In this release, we present the groundbreaking "Complete the Look Recommendations" algorithm, a multi-step process that extracts, classifies, and generates complementary product categories.

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The algorithm starts by extracting product catalogue data, leveraging OpenAI's text-davinci-003 to classify categories into top wear, bottom wear, footwear, or accessories. Combining category classifiers and gender information, it generates complementary categories using OpenAI's text-davinci-003. The Faiss library and cosine similarity search map these categories to our catalogue, ultimately generating product recommendations. The results are seamlessly stored in MongoDB.

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In addition to this, our dynamic logic in Santos ensures recommendations for multiple products added to the cart. Recommendations are fetched for a product's UID or slug, with an API exposed to return the results.

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Our services include "Killer," which triggers and manages ML Pipelines, "VertexAI," the ML Pipeline Orchestrator Platform, "Impact," the repository hosting the "Complete the Look Recommendations" training code, and "Santos," serving recommendations by querying the DB.

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The system architecture flow, from Killer triggering the ML pipeline on Vertex AI to Santos serving recommendations, ensures a robust and efficient process. For a firsthand experience, explore our hosted model with recommendations on Hugging Face, showcasing the transformative impact on a sample Jio Commerce dataset: Hugging Face Link.

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To access, register for a Hugging Face account!