MindMem: Multimodal for Predicting Advertisement Memorability Using LLMs and Deep Learning
This work addresses the challenge for advertisers in enhancing brand recall and engagement in a competitive market, representing an incremental advancement with specific gains in predictive accuracy and optimization.
The paper tackled the problem of predicting advertisement memorability by developing MindMem, a multimodal model that integrates textual, visual, and auditory data, achieving state-of-the-art performance with Spearman's correlation coefficients of 0.631 on LAMBDA and 0.731 on Memento10K, and improving memorability by up to 74.12% through content optimization.
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.