In light of the accelerated growth of e-commerce, the generation of high-quality and efficient content has emerged as a pivotal factor in enhancing user experience and business value. However, conventional methods of content creation are prone to inefficiencies and creative constraints. This paper puts forth a comprehensive model based on the combination of existing generative pre-trained models and human-computer interaction design, with the objective of enhancing the efficiency and quality of e-commerce content generation. In particular, the initial stage of the process involves the utilisation of multi-modal data fusion technology, which facilitates the integration of diverse input sources, including images, videos, and textual data. This approach enhances the model's capacity to comprehend the multifaceted nature of products and services, thereby optimising the generation process. Secondly, a cross-domain sentiment analysis and recommendation engine was designed, which combined the self-attention mechanism in deep learning with the objective of analysing the popularity of the generated content according to consumer behaviour in real time and adjusting the generation strategy. Furthermore, the real-time feedback loop mechanism has been innovatively introduced, which enables the dynamic optimisation of content generation in accordance with user evaluation and click-through rate. This facilitates an improvement in the personalisation and accuracy of content. The experimental results demonstrate that the model markedly enhances the efficiency of content generation and user satisfaction on the e-commerce platform.
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Xue Song (2024) studied this question.