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
Consistent Text Categorization using Data Augmentation in e-Commerce
Guy Horowitz, Stav Yanovsky Daye, Noa Avigdor-Elgrabli, Ariel Raviv
EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising
Guangyuan Shen, Shengjie Sun, Dehong Gao, Libin Yang, Yongping Shi, Wei Ning