Text understanding and generation using transformer models for intelligent e-commerce recommendations
Year: 2024
Type: conference-paper
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. 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.
DOI: 10.1117/12.3034062
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403467727