Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Zhu, Menghui"'
Autor:
Yang, Yang, Chen, Bo, Zhu, Chenxu, Zhu, Menghui, Dai, Xinyi, Guo, Huifeng, Zhang, Muyu, Dong, Zhenhua, Tang, Ruiming
Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing post
Externí odkaz:
http://arxiv.org/abs/2408.07907
Autor:
Xi, Yunjia, Wang, Hangyu, Chen, Bo, Lin, Jianghao, Zhu, Menghui, Liu, Weiwen, Tang, Ruiming, Zhang, Weinan, Yu, Yong
Recently, increasing attention has been paid to LLM-based recommender systems, but their deployment is still under exploration in the industry. Most deployments utilize LLMs as feature enhancers, generating augmentation knowledge in the offline stage
Externí odkaz:
http://arxiv.org/abs/2408.05676
Autor:
Du, Kounianhua, Chen, Jizheng, Lin, Jianghao, Zhu, Menghui, Chen, Bo, Li, Shuai, Tang, Ruiming
Recommender models play a vital role in various industrial scenarios, while often faced with the catastrophic forgetting problem caused by the fast shifting data distribution, e.g., the evolving user interests, click signals fluctuation during sales
Externí odkaz:
http://arxiv.org/abs/2406.00012
Autor:
Liu, Huanshuo, Chen, Bo, Zhu, Menghui, Lin, Jianghao, Qin, Jiarui, Yang, Yang, Zhang, Hao, Tang, Ruiming
Click-through rate (CTR) prediction plays an important role in personalized recommendations. Recently, sample-level retrieval-based models (e.g., RIM) have achieved remarkable performance by retrieving and aggregating relevant samples. However, their
Externí odkaz:
http://arxiv.org/abs/2404.18304
Autor:
Gao, Jingtong, Chen, Bo, Zhu, Menghui, Zhao, Xiangyu, Li, Xiaopeng, Wang, Yuhao, Wang, Yichao, Guo, Huifeng, Tang, Ruiming
Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overal
Externí odkaz:
http://arxiv.org/abs/2309.02061
Autor:
Chen, Xianyu, Shen, Jian, Xia, Wei, Jin, Jiarui, Song, Yakun, Zhang, Weinan, Liu, Weiwen, Zhu, Menghui, Tang, Ruiming, Dong, Kai, Xia, Dingyin, Yu, Yong
With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path
Externí odkaz:
http://arxiv.org/abs/2306.04234
In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their developers or physical restrictions. This indicates unnecessary re-trainin
Externí odkaz:
http://arxiv.org/abs/2212.09033
Publikováno v:
In Journal of Environmental Chemical Engineering October 2024 12(5)
Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses on designing heuristic rules or distance metrics to check whether a sta
Externí odkaz:
http://arxiv.org/abs/2201.11685
Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. Compared to the standard RL solutions that learn a policy solely depending on the states o
Externí odkaz:
http://arxiv.org/abs/2201.08299