Denoising Diffusion Model-Driven Adaptive Estimation of Distribution Algorithm Integrating Multi-Modal Data

Personalized search and recommendation algorithms for multi-modal data have attracted widespread attention. However, existing methods often struggle with effectively integrating multi-source information and performing global search in complex optimization problems. To address these limitations, this paper proposed a denoising diffusion model-driven adaptive estimation of a distribution algorithm integrating multi-modal data. Multi-modal user-generated contents are extensively collected, such as users’ interaction behaviors, category tags, text comments, images, social network relationships, etc. A user interest preference model based on a denoising diffusion model is established by learning the fusion representation of multi-modal data, which extracts user preference features. The surrogate model based on user preferences and adaptive estimation of distribution strategies is presented in the framework of an estimation of distribution algorithm. A surrogate-driven adaptive estimation of distribution algorithm is designed to align with users’ cognitive experiences and behavioral patterns, thereby enhancing the optimization capability of the personalized search algorithm. Additionally, a dynamic model management mechanism is established to update the user interest preference model with new available modal information, which tracks the changes in users’ interest preferences in real-world scenarios. It assists users in efficiently filtering items that match their preferences from large-scale information sources. Extensive experiments on general public datasets demonstrate the feasibility, effectiveness, and superiority of the proposed algorithm, confirming its improvements in both search efficiency and recommendation performance for a personalized recommendation algorithm.