UniNote: A Unified Embedding Model for Multimodal Representation and Ranking
Item-to-Item (I2I) retrieval is a fundamental part of modern content platforms, supporting critical industrial workflows from recommendation engines to content auditing. While multimodal embedding methods have advanced general retrieval, they often falter in I2I scenarios due to the challenges of balancing global content representation with fine-grained local retrieval, the systemic inefficiency of decoupled embedding-and-ranking pipelines, and the inherent trade-offs between model precision and serving latency. To solve these issues, we propose \textbf{UniNote}, a unified embedding model designed for industrial I2I retrieval. Tailored retrieval strategies are introduced to support repres...
| Authors | Jinghan Zhao, Wenwei Jin, Anqi Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 May 2026 |
| Last updated at source | 1 June 2026 |
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