Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Xue, Wanqi"'
Autor:
Guo, Wei, Wang, Hao, Zhang, Luankang, Chin, Jin Yao, Liu, Zhongzhou, Cheng, Kai, Pan, Qiushi, Lee, Yi Quan, Xue, Wanqi, Shen, Tingjia, Song, Kenan, Wang, Kefan, Xie, Wenjia, Ye, Yuyang, Guo, Huifeng, Liu, Yong, Lian, Defu, Tang, Ruiming, Chen, Enhong
Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for
Externí odkaz:
http://arxiv.org/abs/2412.00714
Autor:
Cai, Qingpeng, Xue, Zhenghai, Zhang, Chi, Xue, Wanqi, Liu, Shuchang, Zhan, Ruohan, Wang, Xueliang, Zuo, Tianyou, Xie, Wentao, Zheng, Dong, Jiang, Peng, Gai, Kun
Publikováno v:
The Web Conference 2023
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 interact with the system and provide complex and multi-faceted responses, in
Externí odkaz:
http://arxiv.org/abs/2302.01680
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 behaviors and properties can be difficult, even for experts. A new paradigm c
Externí odkaz:
http://arxiv.org/abs/2301.11774
Autor:
Xue, Wanqi, Cai, Qingpeng, Xue, Zhenghai, Sun, Shuo, Liu, Shuchang, Zheng, Dong, Jiang, Peng, Gai, Kun, An, Bo
Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric, remains difficult to improve. Meanwhile, recent reinforcement learnin
Externí odkaz:
http://arxiv.org/abs/2212.02779
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 dwell time. Meanwhile, reinforcement learning (RL) is widely regarded as
Externí odkaz:
http://arxiv.org/abs/2206.02620
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 approach to dealing with large-scale NSGs, DL methods such as NSG-NFSP
Externí odkaz:
http://arxiv.org/abs/2201.07224
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because o
Externí odkaz:
http://arxiv.org/abs/2201.09058
Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between agents. Meanwhile, adversarial machine learning (ML) has shown that M
Externí odkaz:
http://arxiv.org/abs/2108.03803
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 Network Security Games (NSGs). Unfortunately, existing approaches, in
Externí odkaz:
http://arxiv.org/abs/2106.00897
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 space grows exponentially with the number of agents, which results in
Externí odkaz:
http://arxiv.org/abs/2105.08440