Paper
PinCLIP: Large-scale Foundational Multimodal Representation at Pinterest
Authors
Josh Beal, Eric Kim, Jinfeng Rao, Rex Wu, Dmitry Kislyuk, Charles Rosenberg
Abstract
While multi-modal Visual Language Models (VLMs) have demonstrated significant success across various domains, the integration of VLMs into recommendation and retrieval systems remains a challenge, due to issues like training objective discrepancies and serving efficiency bottlenecks. This paper introduces PinCLIP, a large-scale visual representation learning approach developed to enhance retrieval and ranking models at Pinterest by leveraging VLMs to learn image-text alignment. We propose a novel hybrid Vision Transformer architecture that utilizes a VLM backbone and a hybrid fusion mechanism to capture multi-modality content representation at varying granularities. Beyond standard image-to-text alignment objectives, we introduce a neighbor alignment objective to model the cross-fusion of multi-modal representations within the Pinterest Pin-Board graph. Offline evaluations show that PinCLIP outperforms state-of-the-art baselines, such as Qwen, by 20% in multi-modal retrieval tasks. Online A/B testing demonstrates significant business impact, including substantial engagement gains across all major surfaces in Pinterest. Notably, PinCLIP significantly addresses the "cold-start" problem, enhancing fresh content distribution with a 15% Repin increase in organic content and 8.7% higher click for new Ads.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.03544v1</id>\n <title>PinCLIP: Large-scale Foundational Multimodal Representation at Pinterest</title>\n <updated>2026-03-03T21:57:16Z</updated>\n <link href='https://arxiv.org/abs/2603.03544v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.03544v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>While multi-modal Visual Language Models (VLMs) have demonstrated significant success across various domains, the integration of VLMs into recommendation and retrieval systems remains a challenge, due to issues like training objective discrepancies and serving efficiency bottlenecks. This paper introduces PinCLIP, a large-scale visual representation learning approach developed to enhance retrieval and ranking models at Pinterest by leveraging VLMs to learn image-text alignment. We propose a novel hybrid Vision Transformer architecture that utilizes a VLM backbone and a hybrid fusion mechanism to capture multi-modality content representation at varying granularities. Beyond standard image-to-text alignment objectives, we introduce a neighbor alignment objective to model the cross-fusion of multi-modal representations within the Pinterest Pin-Board graph. Offline evaluations show that PinCLIP outperforms state-of-the-art baselines, such as Qwen, by 20% in multi-modal retrieval tasks. Online A/B testing demonstrates significant business impact, including substantial engagement gains across all major surfaces in Pinterest. Notably, PinCLIP significantly addresses the \"cold-start\" problem, enhancing fresh content distribution with a 15% Repin increase in organic content and 8.7% higher click for new Ads.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-03T21:57:16Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Josh Beal</name>\n </author>\n <author>\n <name>Eric Kim</name>\n </author>\n <author>\n <name>Jinfeng Rao</name>\n </author>\n <author>\n <name>Rex Wu</name>\n </author>\n <author>\n <name>Dmitry Kislyuk</name>\n </author>\n <author>\n <name>Charles Rosenberg</name>\n </author>\n </entry>"
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