no code implementations • 27 Mar 2024 • Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, Xin Xin
However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.
1 code implementation • 25 Mar 2024 • Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
1 code implementation • 12 Dec 2023 • Jiyuan Yang, Yue Ding, Yidan Wang, Pengjie Ren, Zhumin Chen, Fei Cai, Jun Ma, Rui Zhang, Zhaochun Ren, Xin Xin
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
1 code implementation • 16 Oct 2022 • Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma, Pengjie Ren, Hengliang Luo, Xinlei Shi, Zhumin Chen, Zhaochun Ren
Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.