[论文解读] Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations

Yafei Xiang, Hanyi Yu+3 more|arXiv (Cornell University)|Feb 25, 2024

Advanced Text Analysis Techniques被引用 9

一句话总结

这篇论文回顾 Transformer 预训练模型如何用于电子商务文本理解和推荐生成，覆盖从商品描述生成到情感分析和客户服务自动化的应用。

ABSTRACT

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.

研究动机与目标

• 解释 Transformer 预训练模型如何实现电子商务文本理解和推荐生成。
• 确定这些模型在电子商务中提供收益的核心应用场景。
• 讨论在现实案例中的实现工作流程、工作原理及影响。

提出的方法

• 评审 Transformer 预训练在电子商务文本任务中的核心应用场景。
• 分析模型工作原理、实现过程与案例研究中的应用效果。
• 强调在理解用户意图和提升推荐质量方面的优势。
• 讨论挑战与未来改进方向，如泛化、处理大规模数据与隐私保护。

实验结果

研究问题

• RQ1 Transformer 预训练模型在文本理解和生成方面对电子商务的主要任务有哪些？
• RQ2 这些模型如何提升商品描述、情感分析、个性化和客户服务在电子商务中的质量？
• RQ3 将 Transformer 模型应用于大规模、注重隐私的电子商务环境时的关键挑战与未来方向是什么？

主要发现

• Transformer 预训练模型在理解复杂用户意图以提供更好推荐方面具有优势。
• 它们支持自动生成商品描述并改进用户评论的情感分析。
• 基于这些模型的个性化推荐构建和自动化客户服务处理得到提升。
• 未来工作应聚焦泛化、对大数据集的可扩展性以及用户隐私保护。