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Facebook Marketplace enhances user diversity with personalized retrieval system

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PCap, a personalized framework designed to enhance diversity within Facebook Marketplace's retrieval system. This system models individual user diversity preferences using Shannon entropy and categorizes users into distinct diversity buckets. By applying personalized category caps during candidate retrieval and employing an automated online optimization method called Parameter Tuning Sequence, PCap aims to improve the user browsing experience. Large-scale online experiments have shown significant improvements in engagement metrics, offering practical insights for integrating personalized diversity into industrial retrieval systems. AI

IMPACT This framework could enhance user engagement and discovery on large e-commerce platforms by personalizing content diversity.

RANK_REASON The item is a research paper submitted to arXiv detailing a new framework for improving diversity in a retrieval system. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

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

Facebook Marketplace enhances user diversity with personalized retrieval system

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The item is a research paper submitted to arXiv detailing a new framework for improving diversity in a retrieval system. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Shuting Wang ·

    PCap: Personalized Retrieval-Stage Diversity Capping in Facebook Marketplace

    We propose a personalized capping framework (PCap) to improve the diversity in Facebook Marketplace by introducing user-level diversity constraints at the retrieval stage. PCap models individual diversity preferences using Shannon entropy-based scoring, segments users into divers…

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