Decoupled Entity Representation Learning for Pinterest Ads Ranking
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918135836180480 |
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| author | Liu, Jie Li, Yinrui Sun, Jiankai Li, Kungang Sun, Han Wang, Sihan Wu, Huasen Gao, Siyuan Soares, Paulo Li, Nan Liu, Zhifang Li, Haoyang Ji, Siping Leng, Ling Deshikachar, Prathibha |
| author_facet | Liu, Jie Li, Yinrui Sun, Jiankai Li, Kungang Sun, Han Wang, Sihan Wu, Huasen Gao, Siyuan Soares, Paulo Li, Nan Liu, Zhifang Li, Haoyang Ji, Siping Leng, Ling Deshikachar, Prathibha |
| contents | In this paper, we introduce a novel framework following an upstream-downstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential for Pinterest to deliver personalized Pins and ads effectively. Our upstream models are trained on extensive data sources featuring varied signals, utilizing complex architectures to capture intricate relationships between users and Pins on Pinterest. To ensure scalability of the upstream models, entity embeddings are learned, and regularly refreshed, rather than real-time computation, allowing for asynchronous interaction between the upstream and downstream models. These embeddings are then integrated as input features in numerous downstream tasks, including ad retrieval and ranking models for CTR and CVR predictions. We demonstrate that our framework achieves notable performance improvements in both offline and online settings across various downstream tasks. This framework has been deployed in Pinterest's production ad ranking systems, resulting in significant gains in online metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoupled Entity Representation Learning for Pinterest Ads Ranking Liu, Jie Li, Yinrui Sun, Jiankai Li, Kungang Sun, Han Wang, Sihan Wu, Huasen Gao, Siyuan Soares, Paulo Li, Nan Liu, Zhifang Li, Haoyang Ji, Siping Leng, Ling Deshikachar, Prathibha Information Retrieval Artificial Intelligence Machine Learning In this paper, we introduce a novel framework following an upstream-downstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential for Pinterest to deliver personalized Pins and ads effectively. Our upstream models are trained on extensive data sources featuring varied signals, utilizing complex architectures to capture intricate relationships between users and Pins on Pinterest. To ensure scalability of the upstream models, entity embeddings are learned, and regularly refreshed, rather than real-time computation, allowing for asynchronous interaction between the upstream and downstream models. These embeddings are then integrated as input features in numerous downstream tasks, including ad retrieval and ranking models for CTR and CVR predictions. We demonstrate that our framework achieves notable performance improvements in both offline and online settings across various downstream tasks. This framework has been deployed in Pinterest's production ad ranking systems, resulting in significant gains in online metrics. |
| title | Decoupled Entity Representation Learning for Pinterest Ads Ranking |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.04337 |