OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
<|reference_start|>OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search: In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of $>8\%$ relevance, $>7\%$ engagement, and $>5\%$ ads CTR in Pinterest's production search system. The main contributors to these gains are improved content understanding, better multi-task learning, and real-time serving. We enrich our entity representations using diverse text derived from image captions from a generative LLM, historical engagement, and user-curated boards. Our multitask learning setup produces a single search query embedding in the same space as pin and product embeddings and compatible with pre-existing pin and product embeddings. We show the value of each feature through ablation studies, and show the effectiveness of a unified model compared to standalone counterparts. Finally, we share how these embeddings have been deployed across the Pinterest search stack, from retrieval to ranking, scaling to serve $300k$ requests per second at low latency. Our implementation of this work is available at https://github.com/pinterest/atg-research/tree/main/omnisearchsage.<|reference_end|>
@article{agarwal2024omnisearchsage:,
    title={OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search},
    author={Prabhat Agarwal, Minhazul Islam Sk, Nikil Pancha, Kurchi Subhra Hazra,
  Jiajing Xu, Chuck Rosenberg},
    journal={arXiv preprint arXiv:2404.16260},
    year={2024},
    doi={10.1145/3589335.3648309},
    archivePrefix={arXiv},
    eprint={2404.16260},
    primaryClass={cs.IR cs.AI cs.LG}
}