no code implementations • 3 Nov 2023 • Wenqi Sun, Ruobing Xie, Shuqing Bian, Wayne Xin Zhao, Jie zhou
There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems.
2 code implementations • 15 Jun 2022 • Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
no code implementations • 27 Mar 2022 • Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Shuqing Bian, Yang song, Tao Zhang, Ji-Rong Wen
As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates.
no code implementations • 25 Sep 2020 • Shuqing Bian, Xu Chen, Wayne Xin Zhao, Kun Zhou, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen
Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.
2 code implementations • 8 Jul 2020 • Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-Rong Wen, Jingsong Yu
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations.
Ranked #3 on Text Generation on ReDial
no code implementations • IJCNLP 2019 • Shuqing Bian, Wayne Xin Zhao, Yang song, Tao Zhang, Ji-Rong Wen
Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match.
no code implementations • 8 Mar 2018 • Shuqing Bian, Zhenpeng Deng, Fei Li, Will Monroe, Peng Shi, Zijun Sun, Wei Wu, Sikuang Wang, William Yang Wang, Arianna Yuan, Tianwei Zhang, Jiwei Li
For the best setting, the proposed system is able to identify scam ICO projects with 0. 83 precision.