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Research explores norm growth as popularity mechanism in unified discovery systems

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how orthogonal norm growth can act as a popularity mechanism in unified discovery systems that combine search and recommendation. The study focuses on how repeated updates to item vectors in these systems can increase their norm, impacting retrieval performance, particularly under unnormalized dot product calculations. Experiments on datasets like MovieLens 32M and Amazon Books suggest that accumulated tangent movement in item vectors is associated with frequency-norm association and increased head-item exposure when using raw-dot ranking. AI

RANK_REASON Research paper published on arXiv detailing a novel mechanism for retrieval systems. [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 →

Research explores norm growth as popularity mechanism in unified discovery systems

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Research paper published on arXiv detailing a novel mechanism for retrieval systems. [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) · Andrei Atamaniuk ·

    Orthogonal Norm Growth in Two-Tower Retrieval: A Popularity Mechanism for Unified Discovery

    Unified search and recommendation systems may update the same item vectors from both interaction streams. We study how repeated updates can increase an item vector's norm and affect retrieval under an unnormalized dot product. A cosine-only loss makes the output gradient perpendi…

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