Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations
<|reference_start|>Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations: With the rapid development of artificial intelligence technology, Transformer structural pre-training model has become an important tool for large language model (LLM) tasks. In the field of e-commerce, these models are especially widely used, from text understanding to generating recommendation systems, which provide powerful technical support for improving user experience and optimizing service processes. This paper reviews the core application scenarios of Transformer pre-training model in e-commerce text understanding and recommendation generation, including but not limited to automatic generation of product descriptions, sentiment analysis of user comments, construction of personalized recommendation system and automated processing of customer service conversations. Through a detailed analysis of the model's working principle, implementation process, and application effects in specific cases, this paper emphasizes the unique advantages of pre-trained models in understanding complex user intentions and improving the quality of recommendations. In addition, the challenges and improvement directions for the future are also discussed, such as how to further improve the generalization ability of the model, the ability to handle large-scale data sets, and technical strategies to protect user privacy. Ultimately, the paper points out that the application of Transformer structural pre-training models in e-commerce has not only driven technological innovation, but also brought substantial benefits to merchants and consumers, and looking forward, these models will continue to play a key role in e-commerce and beyond.<|reference_end|>
@article{xiang2024text,
    title={Text Understanding and Generation Using Transformer Models for
  Intelligent E-commerce Recommendations},
    author={Yafei Xiang, Hanyi Yu, Yulu Gong, Shuning Huo, Mengran Zhu},
    journal={arXiv preprint arXiv:2402.16035},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16035},
    primaryClass={cs.CL cs.AI}
}