This article examines modern approaches to the personalization of multimodal content in e-commerce, driven by the growing complexity of user requests and the evident need to adapt recommendations to diverse data formats and content modality. The relevance of this topic is underscored by the increasing volume of  information associated with the article, including text, images, video, and audio, which necessitates the application of specialized methods for precise customization and improved personalization. The purpose of the study is to develop original proposals for optimizing recommendation algorithms based on multimodal information, enabling the consideration of both context and individual user preferences. The research reveals contradictions in the literature—while many studies focus on specific aspects of personalization, such as textual data or visual elements, integrative approaches to the analyzed content are insufficiently addressed. The author proposes solutions combining deep learning methods and behavioral model analysis to achieve more accurate results in predicting audience interests. The materials presented in this work will be useful for e-commerce professionals, developers of recommendation systems, and researchers focused on evaluating behavioral patterns.

algorithms, deep learning, multimodal content, personalization, user behavior, recommendations, e-commerce

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