OmniSearchSage: Multi-Task, Multi-Entity Embeddings for Pinterest Search
Paper: https://arxiv.org/abs/2404.16260 (April 2024) Authors: Prabhat Agarwal, Minhazul Islam Sk, Nikil Pancha, Kurchi Subhra Hazra, Jiajing Xu, Chuck Rosenberg (Pinterest)
Overview
OmniSearchSage is Pinterest's approach to search embeddings: a single, multi-task model that produces embeddings for multiple entity types — search queries, Pins, boards, and users — instead of maintaining separate embedding models for each.
Key points
- Multi-entity, one model: Query, Pin, board, and user representations are learned jointly, mapping heterogeneous entities into a shared embedding space usable for retrieval and ranking in Pinterest Search.
- Multi-task training: The model optimizes multiple objectives simultaneously, allowing knowledge sharing across entity types and tasks while still serving each downstream surface.
- Production system: The work is an industrial deployment, focused not only on offline metrics but on serving-scale concerns such as index management, latency, and consistency across search components.
- Related industrial work: It sits alongside other large-scale embedding efforts discussed in this section, including Arctic-Embed 2.0 and BGE M3-Embedding.
- The Scandinavian Embedding Benchmarks
- A Universal Framework for Compressing Embeddings in CTR Prediction
- Arctic-Embed 2.0: Multilingual Retrieval Without Compromise
- BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity
Why it matters
For search and recommendation engineering, OmniSearchSage is a case study in consolidating embedding infrastructure: cross-entity similarity becomes native (e.g., relating queries to Pins directly), and model maintenance is simplified. Readers interested in retrieval-to-ranking pipelines should note how a shared embedding backbone changes the usual cascade of separate query encoders and item encoders.
> Note: For exact benchmark numbers and training details, consult the original PDF. This entry is based on the abstract and public metadata.