Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking
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arXiv
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| Format: | Preprint |
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2025
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| author | Su, Runze Jin, Jiayin Li, Jiacheng Wang, Sihan Bai, Guangtong Wang, Zelun Tang, Li Meng, Yixiong Wu, Huasen Pan, Zhimeng Li, Kungang Sun, Han Liu, Zhifang Li, Haoyang Ji, Siping Peng, Degao Zhuang, Jinfeng Leng, Ling Deshikachar, Prathibha |
| author_facet | Su, Runze Jin, Jiayin Li, Jiacheng Wang, Sihan Bai, Guangtong Wang, Zelun Tang, Li Meng, Yixiong Wu, Huasen Pan, Zhimeng Li, Kungang Sun, Han Liu, Zhifang Li, Haoyang Ji, Siping Peng, Degao Zhuang, Jinfeng Leng, Ling Deshikachar, Prathibha |
| contents | Large embedding tables are indispensable in modern recommendation systems, thanks to their ability to effectively capture and memorize intricate details of interactions among diverse entities. As we explore integrating large embedding tables into Pinterest's ads ranking models, we encountered not only common challenges such as sparsity and scalability, but also several obstacles unique to our context. Notably, our initial attempts to train large embedding tables from scratch resulted in neutral metrics. To tackle this, we introduced a novel multi-faceted pretraining scheme that incorporates multiple pretraining algorithms. This approach greatly enriched the embedding tables and resulted in significant performance improvements. As a result, the multi-faceted large embedding tables bring great performance gain on both the Click-Through Rate (CTR) and Conversion Rate (CVR) domains. Moreover, we designed a CPU-GPU hybrid serving infrastructure to overcome GPU memory limits and elevate the scalability. This framework has been deployed in the Pinterest Ads system and achieved 1.34% online CPC reduction and 2.60% CTR increase with neutral end-to-end latency change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking Su, Runze Jin, Jiayin Li, Jiacheng Wang, Sihan Bai, Guangtong Wang, Zelun Tang, Li Meng, Yixiong Wu, Huasen Pan, Zhimeng Li, Kungang Sun, Han Liu, Zhifang Li, Haoyang Ji, Siping Peng, Degao Zhuang, Jinfeng Leng, Ling Deshikachar, Prathibha Information Retrieval Artificial Intelligence Machine Learning Large embedding tables are indispensable in modern recommendation systems, thanks to their ability to effectively capture and memorize intricate details of interactions among diverse entities. As we explore integrating large embedding tables into Pinterest's ads ranking models, we encountered not only common challenges such as sparsity and scalability, but also several obstacles unique to our context. Notably, our initial attempts to train large embedding tables from scratch resulted in neutral metrics. To tackle this, we introduced a novel multi-faceted pretraining scheme that incorporates multiple pretraining algorithms. This approach greatly enriched the embedding tables and resulted in significant performance improvements. As a result, the multi-faceted large embedding tables bring great performance gain on both the Click-Through Rate (CTR) and Conversion Rate (CVR) domains. Moreover, we designed a CPU-GPU hybrid serving infrastructure to overcome GPU memory limits and elevate the scalability. This framework has been deployed in the Pinterest Ads system and achieved 1.34% online CPC reduction and 2.60% CTR increase with neutral end-to-end latency change. |
| title | Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.05700 |