用多模态模型预测广告记忆度，提升广告效果
MindMem: Multimodal for Predicting Advertisement Memorability Using LLMs and Deep Learning
- 融合文本、图像、音频数据预测广告记忆度
- 在两个数据集上相关性达0.631至0.731，优于现有方法
- 可指导广告内容优化，适合广告算法与营销研究者
在竞争激烈的广告环境中，成功依赖于有效处理消费者、广告主与广告平台之间的复杂互动。为此，我们提出MindMem，一个用于预测广告记忆度的多模态模型。通过整合文本、视觉和听觉信息，该模型在LAMBDA数据集上取得0.631的斯皮尔曼相关系数，在Memento10K数据集上达到0.731，持续超越现有方法。分析发现视频节奏、场景复杂度和情感共鸣是影响记忆度的关键因素。在此基础上，我们进一步提出MindMem-ReAd（基于大语言模型生成的广告重制），通过模拟优化广告内容与投放策略，使广告记忆度最高提升74.12%。结果表明，人工智能在广告领域的应用具有巨大潜力，为广告主提供增强用户参与、提升竞争力的强大工具。
原文摘要 · Abstract (English)
In the competitive landscape of advertising, success hinges on effectively navigating and leveraging complex interactions among consumers, advertisers, and advertisement platforms. These multifaceted interactions compel advertisers to optimize strategies for modeling consumer behavior, enhancing brand recall, and tailoring advertisement content. To address these challenges, we present MindMem, a multimodal predictive model for advertisement memorability. By integrating textual, visual, and auditory data, MindMem achieves state-of-the-art performance, with a Spearman's correlation coefficient of 0.631 on the LAMBDA and 0.731 on the Memento10K dataset, consistently surpassing existing methods. Furthermore, our analysis identified key factors influencing advertisement memorability, such as video pacing, scene complexity, and emotional resonance. Expanding on this, we introduced MindMem-ReAd (MindMem-Driven Re-generated Advertisement), which employs Large Language Model-based simulations to optimize advertisement content and placement, resulting in up to a 74.12% improvement in advertisement memorability. Our results highlight the transformative potential of Artificial Intelligence in advertising, offering advertisers a robust tool to drive engagement, enhance competitiveness, and maximize impact in a rapidly evolving market.
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