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New PALATE method boosts personalization for e-commerce rankers

By PulseAugur Editorial · [1 sources] ·

A new paper introduces PALATE, a method for generating latent embeddings for consumers in local commerce and food-delivery marketplaces. PALATE uses a GPT-style generative model to learn a consumer's engagement sequence, with the final hidden state serving as a dense personalization vector. This approach significantly improved ranking AUC offline and led to measurable gains in revenue and conversion rates when integrated into production rankers for both discovery and search surfaces. AI

IMPACT Enhances personalization in e-commerce by creating more effective consumer embeddings.

RANK_REASON Academic paper detailing a new method for generating embeddings. [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 →

New PALATE method boosts personalization for e-commerce rankers

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Academic paper detailing a new method for generating embeddings. [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) · Utsaw Kumar ·

    PALATE: Pretrained Autoregressive LATent Embeddings

    Local-commerce and food-delivery marketplaces run large ads businesses: across hundreds of thousands of stores, merchants pay to be surfaced on browse and search surfaces, and a ranker decides which ads each consumer sees. Ranking well demands strong per-consumer personalization,…

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