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.

翻译：在竞争激烈的广告领域，成功的关键在于有效驾驭并利用消费者、广告主与广告平台之间复杂的交互作用。这些多方面的互动迫使广告主必须优化其策略，以建模消费者行为、增强品牌回忆并定制广告内容。为应对这些挑战，我们提出了MindMem，一种用于广告记忆度预测的多模态模型。通过整合文本、视觉和听觉数据，MindMem实现了最先进的性能，在LAMBDA数据集上的Spearman相关系数达到0.631，在Memento10K数据集上达到0.731，持续超越现有方法。此外，我们的分析识别了影响广告记忆度的关键因素，例如视频节奏、场景复杂度和情感共鸣。在此基础上，我们进一步提出了MindMem-ReAd（MindMem驱动的再生广告），该框架利用基于大语言模型的模拟来优化广告内容与投放策略，从而使广告记忆度提升最高达74.12%。我们的研究结果凸显了人工智能在广告领域的变革潜力，为广告主提供了一个强大的工具，以在快速演变的市场中提升参与度、增强竞争力并最大化影响力。