Improving Pinterest Search Relevance Using Large Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929554393661440 |
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| author | Wang, Han Sundararaman, Mukuntha Narayanan Gungor, Onur Xu, Yu Kamath, Krishna Chalasani, Rakesh Hazra, Kurchi Subhra Rao, Jinfeng |
| author_facet | Wang, Han Sundararaman, Mukuntha Narayanan Gungor, Onur Xu, Yu Kamath, Krishna Chalasani, Rakesh Hazra, Kurchi Subhra Rao, Jinfeng |
| contents | To improve relevance scoring on Pinterest Search, we integrate Large Language Models (LLMs) into our search relevance model, leveraging carefully designed text representations to predict the relevance of Pins effectively. Our approach uses search queries alongside content representations that include captions extracted from a generative visual language model. These are further enriched with link-based text data, historically high-quality engaged queries, user-curated boards, Pin titles and Pin descriptions, creating robust models for predicting search relevance. We use a semi-supervised learning approach to efficiently scale up the amount of training data, expanding beyond the expensive human labeled data available. By utilizing multilingual LLMs, our system extends training data to include unseen languages and domains, despite initial data and annotator expertise being confined to English. Furthermore, we distill from the LLM-based model into real-time servable model architectures and features. We provide comprehensive offline experimental validation for our proposed techniques and demonstrate the gains achieved through the final deployed system at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17152 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Pinterest Search Relevance Using Large Language Models Wang, Han Sundararaman, Mukuntha Narayanan Gungor, Onur Xu, Yu Kamath, Krishna Chalasani, Rakesh Hazra, Kurchi Subhra Rao, Jinfeng Information Retrieval Computation and Language To improve relevance scoring on Pinterest Search, we integrate Large Language Models (LLMs) into our search relevance model, leveraging carefully designed text representations to predict the relevance of Pins effectively. Our approach uses search queries alongside content representations that include captions extracted from a generative visual language model. These are further enriched with link-based text data, historically high-quality engaged queries, user-curated boards, Pin titles and Pin descriptions, creating robust models for predicting search relevance. We use a semi-supervised learning approach to efficiently scale up the amount of training data, expanding beyond the expensive human labeled data available. By utilizing multilingual LLMs, our system extends training data to include unseen languages and domains, despite initial data and annotator expertise being confined to English. Furthermore, we distill from the LLM-based model into real-time servable model architectures and features. We provide comprehensive offline experimental validation for our proposed techniques and demonstrate the gains achieved through the final deployed system at scale. |
| title | Improving Pinterest Search Relevance Using Large Language Models |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2410.17152 |