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New method boosts user modeling by using negative behavior signals

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for sequential user modeling in click-through rate prediction that incorporates implicit negative behaviors, such as skips and scroll-pasts, alongside positive interactions. This approach, termed Target-Aware Polarity Fusion (TAPF), uses a lightweight gating mechanism to differentiate behavioral evidence and has shown consistent improvements of up to 9.6% in relative AUC across various model architectures. The study highlights that the primary contribution lies in the mixed-polarity data paradigm itself, which significantly outperforms models relying solely on positive signals. AI

IMPACT Enhances user modeling accuracy by incorporating previously underutilized negative behavioral signals.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for sequential user modeling. [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 method boosts user modeling by using negative behavior signals

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The cluster contains a research paper published on arXiv detailing a new method for sequential user modeling. [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) · Jie Jiang ·

    Beyond Positive Signals: Unlocking Implicit Negative Behaviors for Enhanced Sequential User Modeling

    User behavior sequence modeling has become a central component in modern click-through rate (CTR) prediction. Over the past years, the community has invested substantial effort into improving how sequences are encoded, from target-aware attention and interest evolution networks t…

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