Text understanding and generation using transformer models for intelligent e-commerce recommendations

Yafei Xiang; Hanyi Yu; Yulu Gong; Shuning Huo; Mengran Zhu

doi:10.1117/12.3034062

16 October 2024 Text understanding and generation using transformer models for intelligent e-commerce recommendations

Yafei Xiang, Hanyi Yu, Yulu Gong, Shuning Huo, Mengran Zhu

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 1329156 (2024) https://doi.org/10.1117/12.3034062
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yafei Xiang, Hanyi Yu, Yulu Gong, Shuning Huo, and Mengran Zhu "Text understanding and generation using transformer models for intelligent e-commerce recommendations", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 1329156 (16 October 2024); https://doi.org/10.1117/12.3034062

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