5 citations · 9 across the 7 of their papers we have counts for
7 papers
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
Yuchen Zhuang, Xin Shen, Yan Zhao +4
Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. Most existing works focus o…
Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection
Xin Shen, Praful Agrawal, Zhongwei Cheng
Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications. A major ch…
Learning Personalized Page Content Ranking Using Customer Representation
Xin Shen, Yan Zhao, Sujan Perera +3
On E-commerce stores, there are rich recommendation content to help shoppers shopping more efficiently. However given numerous products, it's crucial to select most relevant conten…
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
Xin Shen, Xiaonan Zhao, Rui Luo
Fine-grained multi-label classification models have broad applications in e-commerce, such as visual based label predictions ranging from fashion attribute detection to brand recog…
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li +4
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequen…
FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration
Xin Shen, Kyungdon Joo, Jean Oh
We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera intrinsic and distortion parameters. Our method consists of two parts:…
Two-stage Training for Learning from Label Proportions
Jiabin Liu, Bo Wang, Xin Shen +2
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utiliz…