Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912398299889664 |
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| author | Du, Yue Li Alexander, Ben Antonenka, Mikhail Mahadev, Rohan Wu, Hao-yu Kislyuk, Dmitry |
| author_facet | Du, Yue Li Alexander, Ben Antonenka, Mikhail Mahadev, Rohan Wu, Hao-yu Kislyuk, Dmitry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations Du, Yue Li Alexander, Ben Antonenka, Mikhail Mahadev, Rohan Wu, Hao-yu Kislyuk, Dmitry Computer Vision and Pattern Recognition 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. |
| title | Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.21454 |