11 citations · 35 across the 11 of their papers we have counts for
11 papers
Scaling New Frontiers: Insights into Large Recommendation Models
Wei Guo, Hao Wang, Luankang Zhang +16
Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate inc…
Two-Stage Constrained Actor-Critic for Short Video Recommendation
Qingpeng Cai, Zhenghai Xue, Chi Zhang +9
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially intera…
Reinforcement Learning from Diverse Human Preferences
Wanqi Xue, Bo An, Shuicheng Yan +1
The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques. Describing an agent's desired behavio…
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Wanqi Xue, Qingpeng Cai, Zhenghai Xue +6
Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric,…
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Wanqi Xue, Qingpeng Cai, Ruohan Zhan +4
Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dw…
NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search
Wanqi Xue, Bo An, Chai Kiat Yeo
How resources are deployed to secure critical targets in networks can be modelled by Network Security Games (NSGs). While recent advances in deep learning (DL) provide a powerful a…
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…
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning
Wanqi Xue, Wei Qiu, Bo An +3
Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between…
Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play
Wanqi Xue, Youzhi Zhang, Shuxin Li +3
Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as…
CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space
Shuxin Li, Youzhi Zhang, Xinrun Wang +2
In many real-world scenarios, a team of agents coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action…
One-Shot Image Classification by Learning to Restore Prototypes
Wanqi Xue, Wei Wang
One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically re…