Zobrazeno 1 - 10
of 1 189
pro vyhledávání: '"Zhu, XiaoFei"'
Cross-domain recommendation (CDR) plays a critical role in alleviating the sparsity and cold-start problem and substantially boosting the performance of recommender systems. Existing CDR methods prefer to either learn a common preference bridge share
Externí odkaz:
http://arxiv.org/abs/2408.00038
Autor:
Yang, Zhou, Ren, Zhaochun, Ye, Chenglong, Wang, Yufeng, Sun, Haizhou, Chen, Chao, Zhu, Xiaofei, Wu, Yunbing, Liao, Xiangwen
In-context learning (ICL) achieves remarkable performance in various domains such as knowledge acquisition, commonsense reasoning, and semantic understanding. However, its performance significantly deteriorates for emotion detection tasks, especially
Externí odkaz:
http://arxiv.org/abs/2406.02642
Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning them
Externí odkaz:
http://arxiv.org/abs/2404.13878
Autor:
Yang, Zhou, Ren, Zhaochun, Wang, Yufeng, Zhu, Xiaofei, Chen, Zhihao, Cai, Tiecheng, Wu, Yunbing, Su, Yisong, Ju, Sibo, Liao, Xiangwen
Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static
Externí odkaz:
http://arxiv.org/abs/2402.17437
Autor:
Yang, Zhou, Ren, Zhaochun, Wang, Yufeng, Chen, Chao, Sun, Haizhou, Zhu, Xiaofei, Liao, Xiangwen
Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturi
Externí odkaz:
http://arxiv.org/abs/2402.17959
Autor:
Yang, Zhou, Ren, Zhaochun, Yufeng, Wang, Peng, Shizhong, Sun, Haizhou, Zhu, Xiaofei, Liao, Xiangwen
Empathetic response generation is increasingly significant in AI, necessitating nuanced emotional and cognitive understanding coupled with articulate response expression. Current large language models (LLMs) excel in response expression; however, the
Externí odkaz:
http://arxiv.org/abs/2402.11801
Autor:
Ai, Qingyao, Bai, Ting, Cao, Zhao, Chang, Yi, Chen, Jiawei, Chen, Zhumin, Cheng, Zhiyong, Dong, Shoubin, Dou, Zhicheng, Feng, Fuli, Gao, Shen, Guo, Jiafeng, He, Xiangnan, Lan, Yanyan, Li, Chenliang, Liu, Yiqun, Lyu, Ziyu, Ma, Weizhi, Ma, Jun, Ren, Zhaochun, Ren, Pengjie, Wang, Zhiqiang, Wang, Mingwen, Wen, Ji-Rong, Wu, Le, Xin, Xin, Xu, Jun, Yin, Dawei, Zhang, Peng, Zhang, Fan, Zhang, Weinan, Zhang, Min, Zhu, Xiaofei
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understa
Externí odkaz:
http://arxiv.org/abs/2307.09751
Publikováno v:
In Analytica Chimica Acta 22 November 2024 1330
Autor:
Chen, Yunlong, Zhu, Jiahui, Xia, Tifeng, Feng, Yuqing, Chang, Jiayi, Zhu, Xiaofei, Bai, Jinghe, Wang, Jianqiu, Yan, Wenfu, Zhou, Defeng
Publikováno v:
In Fuel 1 November 2024 375
Publikováno v:
In Information Processing and Management November 2024 61(6)