2 code implementations • 28 Nov 2023 • Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang
The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training.
1 code implementation • 21 Mar 2023 • Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang, Da Luo, Kangyi Lin
This solution is designed to tackle the popularity bias issue in recommendation systems.
2 code implementations • 14 Mar 2023 • Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao
This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.
1 code implementation • 21 Nov 2022 • Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, Ji-Rong Wen, Wayne Xin Zhao
Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference.
1 code implementation • 28 Oct 2022 • Yanyan Shen, Lifan Zhao, Weiyu Cheng, Zibin Zhang, Wenwen Zhou, Kangyi Lin
Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users.
no code implementations • 25 Jan 2022 • Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, Sophia Ananiadou
Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.