Zobrazeno 1 - 10
of 356
pro vyhledávání: '"LIU Shuchang"'
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 32, Iss 2, Pp 185-196 (2022)
Reliability and safety of an electro-hydraulic position servo system (EHPSS) can be greatly reduced for potential sensor and actuator faults. This paper proposes a novel reconfiguration control (RC) scheme that combines multi-model and adaptive contr
Externí odkaz:
https://doaj.org/article/4b91ad58e0f9468b9afaf8da555c5da3
Autor:
Zhang, Zijian, Liu, Shuchang, Liu, Ziru, Zhong, Rui, Cai, Qingpeng, Zhao, Xiangyu, Zhang, Chunxu, Liu, Qidong, Jiang, Peng
User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerating their iteration and optimization. However, prevalent user simulator
Externí odkaz:
http://arxiv.org/abs/2412.16984
Autor:
Zhang, Kaike, Wu, Yunfan, lyu, Yougang, Su, Du, Ge, Yingqiang, Liu, Shuchang, Cao, Qi, Ren, Zhaochun, Sun, Fei
Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairne
Externí odkaz:
http://arxiv.org/abs/2411.14760
Autor:
Meng, Chang, Zhai, Chenhao, Wang, Xueliang, Liu, Shuchang, Feng, Xiaoqiang, Hu, Lantao, Li, Xiu, Li, Han, Gai, Kun
With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommendation pipeline that urgently need personalized modeling techniques for
Externí odkaz:
http://arxiv.org/abs/2410.16755
Autor:
Lin, Chengzhi, Lin, Hezheng, Liu, Shuchang, Ruan, Cangguang, Xu, LingJing, Yang, Dezhao, Wang, Chuyuan, Liu, Yongqi
The proliferation of online micro-video platforms has underscored the necessity for advanced recommender systems to mitigate information overload and deliver tailored content. Despite advancements, accurately and promptly capturing dynamic user inter
Externí odkaz:
http://arxiv.org/abs/2410.03538
The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recommendation diversity, which can negatively impact user satisfaction. To
Externí odkaz:
http://arxiv.org/abs/2408.12470
Accurately predicting watch time is crucial for optimizing recommendations and user experience in short video platforms. However, existing methods that estimate a single average watch time often fail to capture the inherent uncertainty and diversity
Externí odkaz:
http://arxiv.org/abs/2407.12223
Autor:
Liu, Ziru, Liu, Shuchang, Yang, Bin, Xue, Zhenghai, Cai, Qingpeng, Zhao, Xiangyu, Zhang, Zijian, Hu, Lantao, Li, Han, Jiang, Peng
Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed out that how frequently the users come back for the service also refle
Externí odkaz:
http://arxiv.org/abs/2406.06043
In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the creation of music, images, and other artistic forms across a wide range of industries. However, current models for image- and video-to-music synthesis str
Externí odkaz:
http://arxiv.org/abs/2405.02801
Autor:
Zhang, Zijian, Liu, Shuchang, Yu, Jiaao, Cai, Qingpeng, Zhao, Xiangyu, Zhang, Chunxu, Liu, Ziru, Liu, Qidong, Zhao, Hongwei, Hu, Lantao, Jiang, Peng, Gai, Kun
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually
Externí odkaz:
http://arxiv.org/abs/2404.18465