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.

Transformer 结构预训练模型在电子商务领域的核心应用场景包括但不限于产品描述的自动生成、用户评论的情感分析、个性化推荐系统的构建和客服对话的自动处理。本研究通过对模型的工作原理、实施过程和具体案例中的应用效果进行详细分析，强调了预训练模型在理解复杂用户意图和提高推荐质量方面的独特优势，并讨论了未来的挑战和改进方向，例如如何进一步提高模型的泛化能力、处理大规模数据集的能力以及保护用户隐私的技术策略。最终，该论文指出 Transformer 结构预训练模型在电子商务领域不仅推动了技术创新，还给商家和消费者带来了实质性的利益，并展望这些模型在电子商务和其他领域将继续发挥关键作用。

基于 Transformer 模型的智能电子商务推荐的文本理解与生成

Text Understanding and Generation Using Transformer Models for  Intelligent E-commerce Recommendations

The classification of television content helps users organise and navigate
through the large list of channels and programs now available. In this paper,
we address the problem of television content classification by exploiting text
information extracted from program transcriptions. We present an analysis which
adapts a model for sentiment that has been widely and successfully applied in
other fields such as music or blog posts. We use a real-world dataset obtained
from the Boxfish API to compare the performance of classifiers trained on a
number of different feature sets. Our experiments show that, over a large
collection of television content, program genres can be represented in a
three-dimensional space of valence, arousal and dominance, and that promising
classification results can be achieved using features based on this
representation. This finding supports the use of the proposed representation of
television content as a feature space for similarity computation and
recommendation generation.

本文介绍了利用从节目文本信息中提取特征进行电视内容分类的方法，研究表明，通过情感值，电视节目可在一个三维空间中表示，得到很好的分类结果，证明了该方法可用于推荐生成。