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Pinterest deploys PinGER for efficient generative retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PinGER, a new generative retrieval system designed to improve efficiency and effectiveness in recommendation tasks. PinGER uses a novel approach with interleaved semantic IDs (SIDs) and dense tokens, allowing for more compact input histories without sacrificing retrieval quality. This system, deployed at Pinterest for Related Pins, has shown a 3.60% increase in saves for fresh Pins and a 0.62% increase in overall saves, while also reducing latency and eliminating the need for nearest-neighbor search. AI

IMPACT This research introduces a more efficient method for generative retrieval, potentially improving recommendation systems and reducing computational costs.

RANK_REASON The item describes a new research paper detailing a novel generative retrieval system and its deployment in a real-world application. [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 →

Pinterest deploys PinGER for efficient generative retrieval

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The item describes a new research paper detailing a novel generative retrieval system and its deployment in a real-world application. [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) · Charles Rosenberg ·

    PinGER: Token-Efficient Generative Retrieval with Semantic IDs

    Generative retrieval replaces nearest-neighbor search with token-by-token decoding of an item's semantic ID (SID). But existing SID-based methods primarily use L levels of tokens sequentially to represent each item in the input history. As a result, a fixed context window holds L…

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