38 citations · 133 across the 10 of their papers we have counts for
10 papers
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
Bowei He, Xu He, Renrui Zhang +3
With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data req…
Dynamically Expandable Graph Convolution for Streaming Recommendation
Bowei He, Xu He, Yingxue Zhang +2
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models…
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities
Shuo Sun, Wanqi Xue, Rundong Wang +4
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, in…
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction
Xiaofeng Pan, Yibin Shen, Jing Zhang +5
Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave diff…
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Wei Qiu, Xinrun Wang, Runsheng Yu +5
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (…
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Runsheng Yu, Yu Gong, Xu He +4
A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data c…
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Xu He, Bo An, Yanghua Li +6
With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items wi…
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Xu He, Bo An, Yanghua Li +4
Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limite…
Learning Behaviors with Uncertain Human Feedback
Xu He, Haipeng Chen, Bo An
Human feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the op…
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang, Xu He, Runsheng Yu +3
We consider the problem of the limited-bandwidth communication for multi-agent reinforcement learning, where agents cooperate with the assistance of a communication protocol and a…