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
LiLiuM: eBay's Large Language Models for e-commerce
Christian Herold, Michael Kozielski, Leonid Ekimov, Pavel Petrushkov, Pierre-Yves Vandenbussche, Shahram Khadivi
When Box Meets Graph Neural Network in Tag-aware Recommendation
Fake Lin, Ziwei Zhao, Xi Zhu, Da Zhang, Shitian Shen, Xueying Li, Tong Xu, Suojuan Zhang, Enhong Chen