Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Qingpeng Cai"'
Autor:
Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai
Publikováno v:
Proceedings of the ACM Web Conference 2023.
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are pr
Autor:
Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Peng Jiang, Kun Gai, Xiangyu Zhao, Yongfeng Zhang
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction. However, the action space of the recommendation policy is a list of it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a169657ef4186e79b24c16d658d73634
http://arxiv.org/abs/2302.03431
http://arxiv.org/abs/2302.03431
Autor:
Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c98d05b5066385c8f749b380a1a38546
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Publikováno v:
Autonomous Agents and Multi-Agent Systems. 35
Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, particularly for on-policy methods. Previous exploration methods either rely
Publikováno v:
AAAI
Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, ca
Publikováno v:
IJCAI
Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several benefits. However, it does not satisfy the non-expansion property, and its
Publikováno v:
AAAI
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95500ebd963b207ed07f026dd96533a9
http://arxiv.org/abs/1909.03939
http://arxiv.org/abs/1909.03939
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 32
In large e-commerce websites, sellers have been observed to engage in fraudulent behaviour, faking historical transactions in order to receive favourable treatment from the platforms, specifically through the allocation of additional buyer impression
Publikováno v:
WWW
Cai, Q, Filos-Ratsikas, A, Tang, P & Zhang, Y 2018, Reinforcement Mechanism Design for E-Commerce . in Proceedings of the 2018 World Wide Web Conference . WWW '18, pp. 1339–1348, The Web Conference 2018, Lyon, France, 23/04/18 . https://doi.org/10.1145/3178876.3186039
Cai, Q, Filos-Ratsikas, A, Tang, P & Zhang, Y 2018, Reinforcement Mechanism Design for E-Commerce . in Proceedings of the 2018 World Wide Web Conference . WWW '18, pp. 1339–1348, The Web Conference 2018, Lyon, France, 23/04/18 . https://doi.org/10.1145/3178876.3186039
We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform. We employ a general framework of reinforcement mechanism design, which u