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New AI framework MORE boosts e-commerce dialogue conversion

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new adaptive multi-objective reinforcement learning framework called MORE, designed to optimize both reasoning accuracy and linguistic naturalness in e-commerce dialogue systems. This approach treats reasoning functions as constraints to guide policy optimization, avoiding the instability of directly mixing rewards. Online experiments on ByteDance production traffic showed MORE improved conversion rates by over 16% and reached conversion by over 30%, while also boosting user satisfaction. AI

IMPACT This framework could significantly enhance the effectiveness and user satisfaction of AI-powered e-commerce customer service agents.

RANK_REASON Research paper detailing a new AI framework and its performance on benchmarks and real-world traffic.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Xiuying Chen ·

    One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems

    Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e.g., eligibility, credit limit) to ensure correct decision-making and user state interpretation, while also generating natural and faithful responses. The…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems

    Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e.g., eligibility, credit limit) to ensure correct decision-making and user state interpretation, while also generating natural and faithful responses. The…

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