5 citations · 9 across the 7 of their papers we have counts for
7 papers
Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang, Xin Shen, Heming Du +10
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images ar…
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…
Learning to Personalize Recommendation based on Customers' Shopping Intents
Xin Shen, Jiaying Shi, Sungro Yoon +4
Understanding the customers' high level shopping intent, such as their desire to go camping or hold a birthday party, is critically important for an E-commerce platform; it can hel…