Text Understanding and Generation Using Transformer Models for
  Intelligent E-commerce Recommendations
Yafei Xiang Hanyi Yu Yulu Gong Shuning Huo Mengran Zhu
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.