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CAMIE framework enhances product ad retrieval with multimodal embeddings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CAMIE, a novel framework for multimodal item embeddings designed to improve retrieval in dynamic product advertising systems. This framework leverages large language and multimodal models to represent item images and metadata within a unified embedding space. CAMIE is trained on co-engaged item pairs from user journeys, leading to significant improvements in click-through and conversion rates when deployed in production. AI

IMPACT This framework could significantly improve the effectiveness of personalized advertising by better aligning product recommendations with user engagement.

RANK_REASON The item describes a new research framework and its performance evaluation in a paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CAMIE framework enhances product ad retrieval with multimodal embeddings

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The item describes a new research framework and its performance evaluation in a paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yu Zhang ·

    CAMIE: Co-Engagement-Aware Multimodal Item Embeddings for Snap Dynamic Product Ads Retrieval

    Item-to-item (I2I) retrieval is a core primitive in large-scale recommendation and advertising systems. In production Snap Dynamic Product Ads (DPA), I2I retrieval faces two challenges: separate visual, textual, and multimodal encoders fragment the retrieval stack, and content-on…

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