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New simulator DASH improves ad system evaluation with cross-domain user simulation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DASH, a novel decision-aware user simulator designed to improve the evaluation of online advertising and recommendation systems. Unlike previous simulators that focus solely on observable actions like clicks and use single-domain histories, DASH incorporates heterogeneous cross-domain data and generates both thinking traces and behavioral actions. This approach aims to provide a more comprehensive view of user preferences and enhance the diagnostic value of simulations. Experiments on real-world data from Tencent advertising demonstrated DASH's effectiveness and efficiency. AI

IMPACT Enhances the fidelity and diagnostic value of user simulators for online advertising and recommendation systems.

RANK_REASON The cluster contains a research paper detailing a new method for user simulation in online advertising. [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 simulator DASH improves ad system evaluation with cross-domain user simulation

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The cluster contains a research paper detailing a new method for user simulation in online advertising. [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 Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising

    Recent advances in LLM-based user simulation have shown promise for offline evaluation of recommendation and advertising systems. However, existing simulators typically infer user preferences from single-domain interaction histories and are primarily optimized to reproduce observ…

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