E Commerce
E-commerce research focuses on improving various aspects of online shopping experiences, from enhancing search relevance and product recommendations to securing user data and personalizing interactions. Current research employs diverse machine learning models, including large language models (LLMs), graph neural networks (GNNs), and deep learning architectures like transformers and deep interest networks, to achieve these goals. These advancements aim to optimize customer journeys, increase sales conversions, and address critical issues like data privacy and algorithmic fairness within the e-commerce ecosystem. The resulting improvements in efficiency, personalization, and security have significant implications for both businesses and consumers.
Papers
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce
Pranavi Pathakota, Kunwar Zaid, Anulekha Dhara, Hardik Meisheri, Shaun D Souza, Dheeraj Shah, Harshad Khadilkar
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, Huajun Chen
Automatic Product Copywriting for E-Commerce
Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu, Lingfei Wu
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization
Xueying Zhang, Yunjiang Jiang, Yue Shang, Zhaomeng Cheng, Chi Zhang, Xiaochuan Fan, Yun Xiao, Bo Long