Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Dai, Quanyu"'
Autor:
Zhang, Zeyu, Dai, Quanyu, Chen, Luyu, Jiang, Zeren, Li, Rui, Zhu, Jieming, Chen, Xu, Xie, Yi, Dong, Zhenhua, Wen, Ji-Rong
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries. However, there still lacks an objective and automatic evaluation on their memory capability, la
Externí odkaz:
http://arxiv.org/abs/2409.20163
Autor:
Zhang, Zeyu, Bo, Xiaohe, Ma, Chen, Li, Rui, Chen, Xu, Dai, Quanyu, Zhu, Jieming, Dong, Zhenhua, Wen, Ji-Rong
Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving re
Externí odkaz:
http://arxiv.org/abs/2404.13501
Autor:
Liu, Qijiong, Zhu, Jieming, Yang, Yanting, Dai, Quanyu, Du, Zhaocheng, Wu, Xiao-Ming, Zhao, Zhou, Zhang, Rui, Dong, Zhenhua
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potenti
Externí odkaz:
http://arxiv.org/abs/2404.00621
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems. Concurrently, Green AI advocates
Externí odkaz:
http://arxiv.org/abs/2403.04736
Autor:
Wang, Hao, Chen, Zhichao, Fan, Jiajun, Li, Haoxuan, Liu, Tianqiao, Liu, Weiming, Dai, Quanyu, Wang, Yichao, Dong, Zhenhua, Tang, Ruiming
Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issue by aligning distributions of different treatment groups in the laten
Externí odkaz:
http://arxiv.org/abs/2310.18286
Publikováno v:
Neurocomputing (2024)
Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in n
Externí odkaz:
http://arxiv.org/abs/2309.07402
Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content. These huge Transformer-based architectures, when finetuned on recommendation t
Externí odkaz:
http://arxiv.org/abs/2308.14155
Autor:
Wang, Qizhou, Ye, Junjie, Liu, Feng, Dai, Quanyu, Kalander, Marcus, Liu, Tongliang, Hao, Jianye, Han, Bo
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typically deviate from test OOD data. Thus, the performance of OE, when fa
Externí odkaz:
http://arxiv.org/abs/2303.05033
Autor:
Chen, Xu, Zhang, Jingsen, Wang, Lei, Dai, Quanyu, Dong, Zhenhua, Tang, Ruiming, Zhang, Rui, Chen, Li, Wen, Ji-Rong
Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential for improving the recommendation persuasiveness, informativeness and user satisfaction. Despite a lot of promising explai
Externí odkaz:
http://arxiv.org/abs/2303.00168
Autor:
Dai, Quanyu, Li, Haoxuan, Wu, Peng, Dong, Zhenhua, Zhou, Xiao-Hua, Zhang, Rui, zhang, Rui, Sun, Jie
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selec
Externí odkaz:
http://arxiv.org/abs/2211.06684