OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910422912729088 |
|---|---|
| author | Agarwal, Prabhat Sk, Minhazul Islam Pancha, Nikil Hazra, Kurchi Subhra Xu, Jiajing Rosenberg, Chuck |
| author_facet | Agarwal, Prabhat Sk, Minhazul Islam Pancha, Nikil Hazra, Kurchi Subhra Xu, Jiajing Rosenberg, Chuck |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16260 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search Agarwal, Prabhat Sk, Minhazul Islam Pancha, Nikil Hazra, Kurchi Subhra Xu, Jiajing Rosenberg, Chuck Information Retrieval Artificial Intelligence Machine Learning H.3.3 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. |
| title | OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search |
| topic | Information Retrieval Artificial Intelligence Machine Learning H.3.3 |
| url | https://arxiv.org/abs/2404.16260 |