Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
Retrieving semantically similar but visually distinct contents has been a
critical capability in visual search systems. In this work, we aim to tackle
this problem with Visual Product Graph (VPG), leveraging high-performance
infrastructure for storage and state-of-the-art computer vision models for
image understanding. VPG is built to be an online real-time retrieval system
that enables navigation from individual products to composite scenes containing
those products, along with complementary recommendations. Our system not only
offers contextual insights by showcasing how products can be styled in a
context, but also provides recommendations for complementary products drawn
from these inspirations. We discuss the essential components for building the
Visual Product Graph, along with the core computer vision model improvements
across object detection, foundational visual embeddings, and other visual
signals. Our system achieves a 78.8% extremely similar@1 in end-to-end human
relevance evaluations, and a 6% module engagement rate. The "Ways to Style It"
module, powered by the Visual Product Graph technology, is deployed in
production at Pinterest.
