no code implementations • 25 May 2024 • Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Ji-Rong Wen
In this paper, we propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
1 code implementation • 17 Apr 2024 • Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu
With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
no code implementations • 17 Jan 2024 • Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu, Jun Xu
Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests.
1 code implementation • 31 Oct 2023 • Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong liu, Xiao Zhang, Gang Wang, Jun Xu
We refer to this category of biases in neural retrieval models towards the LLM-generated text as the \textbf{source bias}.
1 code implementation • 3 May 2023 • Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, Jun Xu
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond.
1 code implementation • 23 Feb 2022 • Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, yuhao deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.