用视觉产品图谱实现商品到搭配场景的实时推荐
Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations
- 构建视觉产品图谱，连接单品与搭配场景
- 端到端评估准确率达78.8%，模块使用率提升6%
- 适合电商和时尚推荐系统开发者参考
在视觉搜索系统中，检索语义相似但视觉差异大的内容是一项关键能力。本文提出视觉产品图谱（VPG），利用高性能存储架构与前沿计算机视觉模型，构建一个在线实时检索系统。该系统支持从单个商品导航至包含该商品的组合场景，并提供互补商品推荐。系统不仅展示商品在真实场景中的搭配方式，还基于这些灵感生成推荐。文中阐述了构建VPG的核心组件，包括物体检测、基础视觉嵌入及其他视觉信号的模型优化。系统在端到端人工相关性评估中达到78.8%的extremely similar@1指标，模块使用率达6%。'Ways to Style It'功能已部署于Pinterest生产环境。
原文摘要 · Abstract (English)
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.
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