DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash

• URL: http://arxiv.org/abs/2504.07110v1
• Date: Tue, 18 Mar 2025 20:38:31 GMT
• Title: DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash
• Authors: Omkar Gurjar, Kin Sum Liu, Praveen Kolli, Utsaw Kumar, Mandar Rahurkar,
• Abstract summary: We introduce a joint training framework for product and user queries by aligning uni-modal and multi-modal encoders through contrastive learning on image-text data.<n>Our novel approach trains a query encoder with an LLM-curated relevance dataset, eliminating the reliance on engagement history.<n>For personalized ads recommendation, a significant uplift in the click-through rate and conversion rate after the deployment confirms the impact on key business metrics.
• Score: 0.4288177321445912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the success of vision-language models in various generative tasks, obtaining high-quality semantic representations for products and user intents is still challenging due to the inability of off-the-shelf models to capture nuanced relationships between the entities. In this paper, we introduce a joint training framework for product and user queries by aligning uni-modal and multi-modal encoders through contrastive learning on image-text data. Our novel approach trains a query encoder with an LLM-curated relevance dataset, eliminating the reliance on engagement history. These embeddings demonstrate strong generalization capabilities and improve performance across applications, including product categorization and relevance prediction. For personalized ads recommendation, a significant uplift in the click-through rate and conversion rate after the deployment further confirms the impact on key business metrics. We believe that the flexibility of our framework makes it a promising solution toward enriching the user experience across the e-commerce landscape.

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