Minjing Peng

Papers

Total Citations

H-Index

About

Minjing Peng is a researcher whose work sits at the intersection of artificial intelligence, natural language processing, and e-commerce automation. Their most recognized contribution centers on the development of intelligent customer service systems designed to address fundamental inefficiencies in human-operated e-commerce support — specifically the persistent challenges of high labor costs, staff turnover, and inconsistent service quality. Peng's most cited work, "An E-Commerce Customer Service Robot Based on Intention Recognition Model," proposes a sophisticated AI-driven framework that leverages advances in natural language processing to automate and standardize customer interactions in online retail environments. By building a robust intention recognition model, Peng demonstrates how machines can accurately interpret customer queries and respond in contextually appropriate ways, effectively bridging the gap between automated efficiency and human-like service quality. This paper has accumulated 19 citations, reflecting meaningful interest from the research community working on conversational AI and intelligent automation. Peng's research is particularly relevant for students and practitioners exploring how AI can be responsibly deployed in commercial settings, offering both practical engineering insights and broader implications for the future of human-machine interaction in digital marketplaces.