Papers by Yunfan Wu
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback (2022.findings-naacl)
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| Challenge: | Existing work on news recommendation only used positive and negative implicit feedback and suffered from the noise impact. |
| Approach: | They propose a denoising neural network for news recommendation with positive and negative implicit feedback, named DRPN. |
| Outcome: | The proposed method improves on the real-world large-scale dataset. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |