Research on E-commerce Intelligent Recommendation Algorithm and User Experience Optimization Based on Multimodal Data
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Abstract
In the context of the deep development of the digital economy and the continuous innovation of e-commerce formats, user needs are diversified, personalized, and scene oriented. The recommendation algorithm driven by single-modal data has found it difficult to meet the dual needs of accurate recommendation and high-quality user experience. This article focuses on the core pain points in current e-commerce recommendations, such as insufficient understanding of user intent, insufficient integration of multimodal data, imbalance between recommendation accuracy and diversity, and incomplete user experience evaluation system. A systematic study is conducted around the entire chain of multimodal data fusion, intelligent recommendation algorithm optimization, and user experience evaluation and optimization. Construct a multi modal data system for e-commerce and propose a multimodal data fusion method based on attention mechanism and deep learning; Design an intelligent recommendation algorithm that integrates multi-objective optimization to achieve collaborative improvement in accuracy, diversity, novelty, and real-time performance; Establish a multidimensional user experience evaluation system and propose targeted optimization strategies.
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